Initial Public Offering Valuation Dynamics; Evidence …prr.hec.gov.pk/jspui/bitstream/123456789/11825/1/Thesis...Supervisor Dr. Muhammad Khalid Sohail Assistant Professor, Department

Initial Public Offering Valuation Dynamics;

Evidence from Pakistani Capital Market

By

Abdul Rasheed

CIIT/FA11-PMS-005/ISB

PhD Thesis

In

Management Sciences

COMSATS University Islamabad

Islamabad - Pakistan

Spring, 2018

II




A Thesis Presented to


In partial fulfillment

of the requirement for the degree of

PhD (Management Sciences)

By

Abdul Rasheed


Spring, 2018

III



A Post Graduate Thesis submitted to the Department of Management Sciences

as partial fulfilment of the requirement for the award of Degree of Ph.D. in

Management Sciences.

Supervisor

Dr. Muhammad Khalid Sohail

Assistant Professor, Department of Management Sciences

COMSATS University Islamabad (CUI)

(Islamabad Campus)

Name Registration Number

Abdul Rasheed CIIT/FA11-PMS-005/ISB

IV

Certificate of Approval

This is to certify that the research work presented in this thesis, entitled “Initial Public

Offering Valuation Dynamics; Evidence from Pakistani Capital Market” was

conducted by Mr. Abdul Rasheed, CIIT/FA11-PMS-005/ISB, under the supervision

of Dr. Muhammad Khalid Sohail. No part of this thesis has been submitted anywhere

else for any other degree. This thesis is submitted to the Department of Management

Sciences, COMSATS University Islamabad, in the partial fulfillment of the

requirement for the degree of Doctor of Philosophy in the field of Management

Sciences.

Student Name: Abdul Rasheed Signature: __________________

Examinations Committee:

Signature:___________________________

External Examiner 1:

Prof. Dr. Usman Mustafa

Director

Project Evaluation and Training Division,

Pakistan Institute of Development

Economics (PIDE),

Quid-i-Azam University Campus,

Islamabad

Signature:__________________________.

External Examiner 2:

Dr. Ashfaq Ahmad

Associate Professor

Hailey College of Commerce,

University of the Punjab, Lahore

.

Dr. Muhammad Khalid Sohail

Supervisor

Department of Management Sciences,

COMSATS University Islamabad,

Islamabad

.

Dr. Khalid Riaz

Dean,

Faculty of Business Administration,


.

.

V

Author’s Declaration

I, Abdul Rasheed, CIIT/FA11-PMS-005/ISB hereby state that my PhD thesis titled

“Initial Public Offering Valuation Dynamics; Evidence from Pakistani Capital

Market” is my own work and has not been submitted previously by me for taking any

degree from this university i.e. COMSATS University Islamabad or anywhere else in

the country / world.

At any time if my statement is found to be incorrect even after my graduate the

University has the right to withdraw my PhD degree.

Date: ___

________________________

Abdul Rasheed


VI

Plagiarism Undertaking

I, solemnly declare that research work presented in the thesis title “Initial Public

Offering Valuation Dynamics; Evidence from Pakistani Capital Market” is solely my

work with no significant contribution from any other person. Small contribution / help

wherever taken has been duly acknowledged and that complete thesis has been written

by me.

I understand the zero tolerance policy of the HEC and COMSATS University

Islamabad towards plagiarism. Therefore, I as an author of the above titled thesis

declare that no portion of my thesis has been plagiarized and any material used as

reference is properly referred/cited.

I understand that if I am found guilty of any formal plagiarism in the above titled

thesis even after award of PhD degree, the University reserves the rights to withdraw /

revoke my PhD degree and that HEC and the University has the right to publish my

name on the HEC / University Website on which names of students are placed who

submitted plagiarized thesis.

Date: ______

________________________

Abdul Rasheed


VII

Certificate

It is certified that Abdul Rasheed, CIIT/FA11-PMS-005/ISB has carried out all the

work related to this thesis under my supervision at the Department of Management

Sciences, COMSATS University Islamabad, Islamabad and the work fulfills the

requirement for award of PhD degree.

Date:

Supervisor:

____________________________ Dr. Muhammad Khalid Sohail

Assistant Professor

Department of Management Sciences


Islamabad

Head of Department:

__________________________________

Dr. Aneel Salman, Assistant Professor

Department of Management Sciences,


Islamabad

VIII

DEDICATION

I dedicate my work to my beloved Mother

IX

ACKNOWLEDGEMENTS

First and foremost, I am grateful to ALLAH Almighty who bestowed upon me the

knowledge, skills, courage and strength to complete this thesis. I pay my sincere

gratitude from the core of my heart to Holy Prophet Hazrat Muhammad (Peace Be

Upon Him) who is the sole reason for creation of this universe and is the role model

for the whole mankind.

I wish to pay my sincerest gratitude to my supervisor, Dr. Muhammad Khalid Sohail,

Assistant Professor, Department of Management Science, COMSATS University

Islamabad, who imparted his knowledge, broad experience and positive vision to me.

He has always been a source of inspiration for me since I started my research work. I

would never forget his unflinching readiness to help me out whenever I was in a

trouble, no matter how trivial the trouble was.

I am obliged to Professor Dr. Khalid Riaz, Dean, Faculty of Business Administration

for their guidance and help in research work as well as in administrative matters

whenever I seek. Likewise, I am also grateful to Dr. Aneel Salman, Head of the

Department of Management Sciences and Dr. Zahid Iqbal, former Head of the

department of Management Sciences for their great help in administrative matters.

I owe my heartily gratitude to my sweet mother, Mrs. Zohra (Late) and my lovely

wife, Mahmoona Rasheed, as without their love, cooperation and continuous

encouragement this research work was not possible. They cooperated in every respect

throughout my PhD program right from the beginning till the end. I would like to pay

special thanks to my nephew, Jasim Sarwar for his never ending encouragement and

moral support throughout the PhD program.

.

Abdul Rasheed


X

ABSTRACT

Initial Public Offering Valuation Dynamics; Evidence from

Pakistani Capital Market

The thesis examines the valuation dynamics of Pakistani Initial Public Offerings

(IPOs): their practices, motivations and implications. This study examines the pre-IPO

valuation dynamics and the post-IPO price performance paradigms using 88 IPOs

floated from 2000 to 2016 on the Pakistan Stock Exchange. The main objectives of

this study includes: (1) to provide insights of preferred valuation methods when

valuing IPOs, (2) to compare the bias and accuracy attached to each valuation

methods, (3) to provide the usefulness of prospectus information on the initial

valuations, the underpricing and the long-run underperformance, and finally, (4) to

validate the long-run underperformance using calendar-time approaches.

The binary logit model, the signed predictcion errors (SPE) and the absolute

prediction errors (APE) were used to explain the choice, bias and accuracy attached to

each valuation methods respectively. The accounting-based valuation model was used

to estimate the impact of fundamental, risk and signaling factors on the post-IPO

performance. The capital asset pricing model (CAPM), Fama-French three- (FF3F)

and five-factor (FF5F) models were used as robust measures to affirm the long-run

underperformance anomaly.

The findings document that the Pakistani underwriters repeatedly used dividend

discount model (DDM), discounted cash flow (DCF) model and the comparable

multiples valuation methods when valuing IPOs. The findings of SPE reveal that the

DDM and DCF methods seem to be unbiased value estimators than the comparable

multiples. The findings of APE document that the DCF produce more valuation

accuracy than the other valuation methods.

The average underpricing of 32.85% was observed in the Pakistani primary market.

This research extends the underpricing analysis in various aspects such as: (1) the

level of underpricing was negatively related to the firm size, (2) the underpricing of

IPOs issued in the hot-issue market was significantly higher than the IPOs issued in

the cold-issue market, (3) the underpricing of IPOs issued through bookbuilding was

lower than the IPOs issued through the fixed price auction, (4) the underpricing of

privatization IPOs was higher than the underpricing of non-privatization IPOs, (5) the

underpricing of survivor IPOs was higher than the underpricing of non-survivor IPOs,

XI

(6) the IPOs offered in the Oil & Gas and Chemicals sectors produce more

underpricing than the other sectors. The finding of initial excess returns (IER)

regression analysis reveals that the earnings, financial leverage, efficiency risk, firm

beta and the underwriter reputation were the key determinants to explain variation in

the level of underpricing.

In the long-run returns (LRR) analysis, the buy and hold abnormal returns (BHAR)

produce negative returns of -23.52% and -65.22% in year 3 and year 5 respectively.

On the similar pattern, the cumulative abnormal returns (CAR) produce negative

returns of -24.62% and -29.37% in year 3 and year 5 respectively. This study extend

the long-run performance analysis in various aspects such as: (1) the IPOs issued in

hot-issue market produce more negative returns than the IPOs issued during cold-

issue market, (2) the Automobile & Electrical Goods sector IPOs produce worst

negative returns, while the Modaraba & Foods sector IPOs outperform the market in

the long run, (3) the privatization IPOs outperform the market in the long run than the

non-privatization IPOs. The finding of LRR regression analysis reveals that the book

value of shareholder’s equity, earnings, capital availability risk, firm beta, underwriter

reputation, the percentage of shares offered and initial excess returns were the

significant determinants that explain the variation in the long-run returns.In the

Calendar-time approach, the negative values of intercepts of CAPM, FF3F & FF5F

validate the negative performance in the long run. The market risk premium was the

most significant determinant in all asset pricing models, while HML-value factor (in

equally-weighted FF5F) and CMA-investment factor (in value-weighted FF5F) were

also significant determinants in the Fama-French five-factor models.

This study is one of the few studies in IPO valuation literature that is being

accomplished in a growing and transforming from loose regulated capital market to

synchronize the state of affairs and first in Pakistan to investigate the explanatory

power of prospectus information on IPO valuation dynamics.

Keywords: Pakistan Stock Exchange, Initial Public Offerings, Valuation Methods,

Asset Pricing Models, Event- & Calendar-time Approaches

XII

TABLE OF CONTENTS 1. Introduction ................................................................................................... 1

1.1 Background of the study ........................................................................... 2

1.2 Overview of IPOs in the Pakistan Stock Exchange .................................. 4

1.2.1 The Pakistan Stock Exchange ........................................................... 4

1.2.2 The History of Karachi Stock Exchange ........................................... 5

1.2.3 The Performance of Karachi Stock Exchange .................................. 6

1.2.4 The History of Initial Public Offerings in Pakistan ........................... 8

1.2.5 The Listing Procedure of IPOs in Pakistan Stock Exchange ............ 9

1.3 The Issues Related to IPO Valuations and Pricing ................................. 12

1.4 The Issues Related with IPO aftermarket Price Performance ................. 13

1.5 Problem Statement .................................................................................. 15

1.6 Research Objectives and Questions ........................................................ 16

1.6.1 Research Objectives ........................................................................ 16

1.6.2 Research Questions.......................................................................... 16

1.7 Significance of the Study ........................................................................ 17

1.8 Structure of Dissertation ......................................................................... 19

2. Literature Review ........................................................................................ 20

2.1 IPO Valuation ......................................................................................... 21

2.1.1 Post-IPO Valuation Methods based studies .................................... 21

2.1.2 Pre IPO Valuation Methods based studies ...................................... 28

2.2 The IPO Underpricing Phenomenon ....................................................... 33

2.2.1 The winner’s Curse Hypothesis ....................................................... 34

2.2.2 The Prestigious Underwriter Hypothesis......................................... 35

2.2.3 The Signaling Hypothesis ................................................................ 37

2.2.4 The Lawsuit Avoidance Hypothesis ................................................ 40

2.2.5 The Prospect theory ......................................................................... 43

2.2.6 An International Empirical Evidence .............................................. 44

2.2.7 Underpricing in Pakistan ................................................................. 49

2.3 The IPO Long-run performance .............................................................. 52

2.3.1 Fad Hypothesis ................................................................................ 53

XIII

2.3.2 Heterogeneous expectations hypothesis .......................................... 54

2.3.3 Agency Hypothesis .......................................................................... 55

2.3.4 Signaling Hypothesis ....................................................................... 56

2.3.5 An International Empirical Evidence .............................................. 57

2.3.6 Difficulties with Long-term Returns Measurement ........................ 63

2.3.7 Long-run IPOs Performance in Pakistan ......................................... 65

3. Research Methodology ................................................................................ 68

3.1 Data and Sample Description .................................................................. 69

3.2 Theoretical Background .......................................................................... 72

3.3 The Choice, Bias and Accuracy of Valuation Methods .......................... 73

3.3.1 Enlightening the choice of valuation methods ................................ 73

3.3.2 Bias and Accuracy of Valuation Methods ....................................... 78

3.4 The Basic Valuation Model .................................................................... 83

3.4.1 The IPO Valuation Model ............................................................... 85

3.4.2 Hypotheses about the Valuation Model .......................................... 92

3.5 The Initial Excess Return Model ............................................................ 94

3.5.1 Hypotheses about the Initial Excess Returns Model ....................... 97

3.6 The Long-run Performance Model ......................................................... 99

3.6.1 Hypotheses about the Long Run Returns Model ........................... 103

3.6.2 CAPM, Fama-French Three- and Five-Factor Models ................. 106

3.6.2.1 FF 3-Factor Variables construction ................................................. 107

3.6.2.2 FF 5-Factor Variables construction ................................................. 108

4. Results and Discussion .............................................................................. 110

4.1 The Choice, Bias and Accuracy of Valuation Methods ........................ 111

4.1.1 Explaining the Choice of Valuation Methods ............................... 111

4.1.2 Explaining the Bias of Valuation Methods ................................... 126

4.1.3 Explaining the Accuracy of Valuation Methods ........................... 131

4.2 The IPOs Initial Prices (Valuation) Analysis........................................ 138

4.2.1 Descriptive Statistics ..................................................................... 140

4.2.2 The Univariate Analysis ................................................................ 143

4.2.3 Accounting Based Valuation Models Analysis ............................. 146

XIV

4.3 The IPOs Initial Excess Returns Analysis ............................................ 155

4.3.1 Descriptive Statistics of IER ......................................................... 156


4.3.3 The Analysis of Initial Excess Returns Models ............................ 165

4.3.4 The Sensitivity Analysis ................................................................ 170

4.4 The IPOs Long-run Returns Analysis ................................................... 172

4.4.1 Descriptive Statistics of LRR ........................................................ 173


4.4.3 The Analysis of Long-run Returns (LRR) Models ....................... 186

4.5 IPOs Long-run Performance Using Calendar-Time Approach ............ 193

4.5.1 Descriptive Statistics ..................................................................... 193


4.5.3 The Analysis of Asset Pricing Models .......................................... 196

5. Summary and Conclusion ......................................................................... 200

5.1 The Analysis of Pre-IPO Valuation Dynamics ..................................... 202

5.2 The Analysis of Post-IPO Price Performance ....................................... 203

5.3 Policy Implications of the Study ........................................................... 206

5.3.1 The Pakistan Stock Exchange ....................................................... 207

5.3.2 The Investment Banks/Underwriters ............................................. 207

5.3.3 The Unlisted/Potential IPO-issuing Firms..................................... 208

5.3.4 The Investment Community .......................................................... 209

5.4 Limitations of the Study ........................................................................ 209

5.5 Suggestions for Future Research .......................................................... 210

References ...................................................................................................... 212

Appendix ........................................................................................................ 242

XV

LIST OF TABLES___________________________________________

Table 1. 1: The Performance of KSE-100 Index during 1995-2016 .............................. 7

Table 1. 2: Shares Offered and Funds Raised during 1995-2016 .................................. 9

Table 1. 3: Step by Step IPO Listing Procedure in Pakistan Stock Exchange ............. 11

Table 2. 1: Post IPO valuation studies under different comparable benchmarks ........ 25

Table 2. 2: IPOs Initial Excess Returns Studies From International Literature ........... 47

Table 2. 3: IPOs Initial Excess Returns Studies From Pakistani Literature ................ 50

Table 2. 4: IPOs Long-run Performance Studies From International Literature ......... 60

Table 2. 5: IPOs Long-run performance Studies using the Event-Time Approach ..... 61

Table 2. 6: IPOs Long-run performance Studies using Calendar-Time Approach...... 65

Table 2. 7: IPOs Long-run Performance Studies From Pakistani Literature ............... 66

Table 3. 1: Sample Selection Criteria and Description ................................................ 70

Table 3. 2: Sector-wise IPO firms in the sample ......................................................... 71

Table 3. 3: Operational definitions of variables used in Equation (1), (6) and (7) ...... 82

Table 3. 4: Operational definitions of variables used in IPO valuation model ............ 91

Table 4. 1: Descriptive Statistics of IPO Firm’s characteristics ................................ 112

Table 4. 2: Correlation Matrix of Variables used in Binary Logit & Cross-sectional

Models........................................................................................................................ 115

Table 4. 3: The Summary of Valuation Methods disclosed in prospectus ................ 119

Table 4. 4: Results of Binary Logit of Preferred Valuation Methods ........................ 121

Table 4.5: Analysis of Signed Prediction Errors (at 1st Day Closing Prices) ........... 127

Table 4. 6: Analysis of Signed Prediction Errors (at IPO Offer Prices) .................... 128

Table 4. 7: Cross-sectional Regressions of Bias of Valuation Methods .................... 130

Table 4. 8: Analysis of Absolute Prediction Errors (at 1st Day Closing Prices) ....... 132

Table 4. 9: Analysis of Absolute Prediction Errors (at IPO Offer Prices) ................. 133

Table 4. 10: The Analysis of Value Relevancy Through Regressions ...................... 135

Table 4. 11: Cross-sectional Regressions of Accuracy of Valuation Methods.......... 137

Table 4. 12: Descriptive Statistics of Variables used in Performance Models .......... 140

Table 4. 13: Correlation Matrix of Variables used in Valuation and Aftermarket

Performance Models .................................................................................................. 144

Table 4. 14: Empirical Findings of Basic Valuation Models..................................... 147

Table 4. 15: Cross-sectional Analysis of Valuation Models using Full Sample ....... 150

XVI

Table 4. 16: Cross-sectional Analysis of Valuation Models using non-PIPO Sample

.................................................................................................................................... 153

Table 4. 17: Descriptive Statistics of Initial Excess Returns Analysis ...................... 156

Table 4. 18: Descriptive Statistics of IERs in different Issue Proceeds ..................... 157

Table 4. 19: Descriptive Statistics of IERs in Hot- & Cold-issue Periods ................ 159

Table 4. 20: Descriptive Statistics of IERs of various sub-samples .......................... 160

Table 4. 21: Year-wise Initial Excess Returns Analysis ............................................ 161

Table 4. 22: Sector-wise Initial Excess Returns Analysis ......................................... 162

Table 4. 23: Correlation Matrix of Variables used in Initial Excess Returns Analysis

.................................................................................................................................... 164

Table 4. 24: Regression Analysis of IER Models using Full IPO Sample ................ 166

Table 4. 25: Regression Analysis of IER Models using Non-PIPO Sample ............. 171

Table 4. 26: Descriptive Statistics of equally-weighted Long-run Returns ............... 173

Table 4. 27: Descriptive Statistics of value-weighted Long-run Returns .................. 173

Table 4. 28: Year-wise Long-run Returns Analysis using BHAR and CAR............. 174

Table 4. 29: Year-wise Long-run Returns Analysis using BHAR ............................. 175

Table 4. 30: Year-wise Long-run Returns Analysis using CAR ............................... 176

Table 4. 31: Sector-wise Long-run Returns Analysis using BHAR .......................... 177

Table 4. 32: Sector-wise Long-run Returns Analysis using CAR ............................. 177

Table 4. 33: Privatization and Non-Privatization IPOs Long-run Returns Analysis . 179

Table 4. 34: Firm’s Size-wise Long-run Returns Analysis using BHAR .................. 180

Table 4. 35: Firm’s Size-wise Long-run Returns Analysis using CAR ..................... 180

Table 4. 36: Initial Returns-wise Long-run Returns Analysis using BHAR ............. 181

Table 4. 37: Initial Returns-wise Long-run Returns Analysis using CAR ................ 181

Table 4. 38: Financial and Non-Financial IPOs Long-run Returns Analysis ............ 182

Table 4. 39: Correlation Matrix of Variables used in LRR Models .......................... 184

Table 4. 40: Regression Analysis of LRR Models using Full Sample ...................... 187

Table 4. 41: Regression Analysis of LRR Models using Non-PIPO Sample ............ 191

Table 4. 42: Descriptive Statistics of Variables Used in Asset Pricing Models ........ 194

Table 4. 43: Correlation Matrix of Variables Used in Asset Pricing Models ............ 195

Table 4. 44: Regression Analysis of Capital Asset Pricing Models .......................... 196

Table 4. 45: Regression Analysis of Fama-French Three Factor (FF3F) Models ..... 197

Table 4. 46: Regression Analysis of Fama-French Five Factor (FF5F) Models ....... 198

Table A. 1: Characteristics of IPO Firms used in Sample ......................................... 243

XVII

Table A. 2: The List of Non-Survivor IPO Firms During Sample Period ................. 245

Table A. 3: The List of State Owned Enterprize (SOE) IPO Firms .......................... 245

Table A. 4: Firms Post-IPO Cash and Stock Dividends (%) Announcements History

.................................................................................................................................... 246

Table A. 5: Cross-sectional Regressions of Bias of Valuation Methods ................... 249

Table A. 6: Value Relevance Regressions (at IPO Offer prices) ............................... 250

Table A. 7: Cross-sectional Regressions of Accuracy of Valuation Methods (IPO

Offer Prices) ............................................................................................................... 251

Table A. 8: Empirical findings of basic valuation models......................................... 252

Table A. 9: Cross-sectional Analysis of Full Sample Valuation Models .................. 253

Table A. 10: Equally and Value-weighted Monthly Returns using BHAR & CAR . 254

Table A. 11: Correlation Matrix of Variables used in LRR Models ......................... 259

Table A. 12: Regression Analysis of LRR Models using Full Sample ..................... 261

Table A. 13: Regression Analysis of LRR Models using Non-PIPO Sample ........... 262

Table A. 14: Descriptive Statistics of Variables Used in Value-weighted Analysis . 263

Table A. 15: Correlation matrix of variable used in value-weighted analysis........... 264

Table A. 16: Year-wise New-Listings and De-Listings in PSX ................................ 265

XVIII

LIST OF FIGURES__________________________________________

Figure 1. 1: Regulatory and Operational Structure of Pakistan Stock Exchange .......... 5

Figure 1. 2: The Performance of KSE-100 Index during 1995-2016 ............................ 7

Figure 1. 3: Shares Offered and Funds Raised during 1995-2016 ................................. 8

Figure 3. 1: Year-wise Number of IPOs in the Sample ............................................... 70

Figure 3. 2: Sector-wise Number of IPOs in the Sample ............................................. 71

Figure 4. 1: IPO Valuation and Pricing process involve in New Offerings .............. 118

Figure 4. 2: Aftermarket IPO Valuation and Performance Analysis ......................... 139

Figure 4. 3: Yearly Listed IPOs and IER in Hot-Cold Issue Market ......................... 158

Figure 4. 4: Sector-wise Initial Excess Returns Analysis .......................................... 162

Figure 4. 5: Year-wise Long-run Returns Analysis using BHAR and CAR ............. 175

Figure 4. 6: Year-wise Long-run Returns using BHAR and CAR ............................ 176

Figure 4. 7: Sector-wise Long-run returns Analysis using BHAR and CAR ............ 178

Figure 4. 8: Privatization and Non-Privatization IPOs Long-run Returns Analysis.. 179

Figure 4. 9: Long-run Performance Analysis of Financial and Non-Financial IPOs 182

Figure A. 1: Equal- and Value-weighted Monthly returns using BHAR and CAR .. 255

Figure A. 2: Year-wise Long run returns for year 1 using BHAR and CAR ............ 256





Figure A. 7: Sector-wise Long run returns for year 1, 3 & 5 using CAR .................. 258

Figure A. 8: Sector-wise Long run returns for year 1, 3 & 5 using BHAR ............... 258

Figure A. 9: The cumulative effect of New-Listings & De-Listings in PSX ............ 265

XIX

List of Abbreviations

ADB Asian Development Bank

AMCs Asset Management Companies

APE Absolute Prediction Errors

BHAR Buy-and-Hold Abnormal Returns

CAPM Capital Asset Pricing Model

CAR Cumulative Abnormal Returns

DCF Discounted Cash Flow

DDM Dividend Discount Model

EW Equally Weighted Returns

FF3F Fama-French Three Factor

FF5F Fama-French Five Factor Model

FTSE The Financial Times Stock Exchange

GOP Government of Pakistan

IER Initial Excess Returns

IPOs Initial Public Offerings

ISE Islamabad Stock Exchange

KSE Karachi Stock Exchange

LRR Long run Returns

LSE Lahore Stock Exchange

MOF Ministry of Finance

MSCI Morgan Stanley Capital International

NCCPL National Clearing Company of Pakistan

PSX Pakistan Stock Exchange

SBP State Bank of Pakistan

SECP Securities and Exchange Commission of Pakistan

SEO Seasoned Equity Offerings

SPE Signed Prediction Errors

VW Value Weighted Returns

1

1. Chapter 1

1. Introduction

2

1.1 Background of the study

Thousands of public limited companies around the globe have offered their

shares to the general public through primary market in the last couple of decades. An

Initial Public Offering (IPO) is a procedure whereby a firm sells its shares first time to

the general public through the fixed price or book-building auction. Firms can finance

their operations and investments through internal sources as well as external sources

of funding. Internal sources mostly refer as earnings and retained earnings, and

external sources mostly refer as debt financing and equity financing. A decision to

raise equity capital through selling stock to the general public is a significant event in

the lifecycle of issuer firm. A number of IPO studies have raised the concerns whether

firms decide to go public mainly to finance their future investments or for other

reasons such as market timing (Colak & Gunay, 2011; Agathee et al., 2012a). Chang

et al., (2012) argue that most firms decide to go public in hot-issue1 market due to the

several IPO stylized facts such as leave less money2 on the table, favorable investor

sentiments 3 , overvalued stock prices and to avoid time-varying-adverse-selection

problem. There are several theoretical explanations found by researchers that why

firms raising equity capital through selling stocks to the general public and/or

institutions such as plans for future growth, dilution of ownership, exit strategy, to

increase liquidity, cheap cost of capital, to increase external monitoring, reduction in

financial leverage, mergers and acquisitions, to increase equity investment, and

corporate control and to reduce agency costs4 etc (Burkart and Lee, 2008; Lyandres et

al, 2007; Kim and Weisbach, 2008; Huyghebaert and Hulle, 2006; Ritter and Welch,

2002; Pagano & Panetta, 1998).

The initial public offering is a complex procedure because market participants

are uncertain about the worth of IPO firms’ equity and unable to predict the market

demand for their shares. Therefore the issuing firm passes on the IPO offer price

decision to underwriters who act as a valuation expert and certify as financial advisor

1 A situation, when the number of new offerings gets listed with great pace than the usual number of

new offerings in a certain time period. 2 Leave less money on the table means underwriters deliberately set offer price discount at the time of

going public to compensate the market liquidity risk for more details see J. Ritter (1984, 1988) and

Ritter, J. R., & Welch, I. (2002). 3 When investors are least concerned about market risk, valuation and willing to pay high prices based

on good current and forecasted macroeconomic factors 4 When company actual performance is less than the potential performance due to conflicts of interests

between managers and owners is called agency cost.

3

to set preliminary offer prices. In practice, the lead underwriters estimate ex-ante fair

value estimates of the issuing firms using several valuation methods. For this purpose,

the lead underwriters employ different valuation methods based on the firm-specific

characteristics, macroeconomic factors, aggregate stock market returns and market

returns volatility before the IPOs to estimate fair values for new offerings.

Roosenboom (2012) argues that it is difficult to access actual IPO valuation process

employed by investment banks to value IPOs because in most of the countries it is not

mandatory to disclose in the prospectus. Bradley et al., (2003) mentioned that the US

underwriters are not allowed to publish predictions about IPO firms’ valuations.

Aggarwal et al., (2009) examine that how valuations of new offerings has changed

over different state of economies. They find that firms having more negative earnings

before IPOs produce higher valuation and firms with more positive earnings have

higher valuation than firms with less positive earnings. Abdulai (2015), Roosenboom

(2012) and Deloof et al., (2009) document that the lead underwriters deliberately

discount the offer price to attract more participation in the bidding process. Their

findings reveal that the prestigious underwriters offered less offer price discount and

vice versa.

It has been observed from the existing literature of corporate finance

particularly in IPO studies that there are two main irregularities identified namely as;

(1) abnormal returns in very few days, and (2) long-run underperformance of IPO

firms. Ibbotson and Jaffe (1975) and Ritter (1988) first-time document the IPO market

fluctuations over time, both the aftermarket IPO performance and the number of firms

went public vary substantially. A number of IPO studies reported the first day

abnormal returns vary over the last couple of decades across the countries (see Table

2.2). Loughran and Ritter (2004) unveil that the level of IPO underpricing in the US

markets is 11.70%, while Jelic and Briston (1999) document that the level of IPO

underpricing in the emerging market of Hungry is 52%. Ritter (2011) points out that

the initial abnormal returns in the emerging markets of India and Malaysia are higher

than the developed markets of the US and UK. Ritter (1991) first-time documents the

long-run underperformance using data of US IPOs. Kooli and Suret (2004) also

confirmed the long-run underperformance in the Canadian IPO product market.

Govindasamy (2010) documented the long-run underperformance using the sample of

229 South Africa IPOs floated during the 1995-2006 period. Bossin and Sentis (2014)

4

report the long-run underperformance in France and Brown (1999) reports negative

returns for the UK. Bossin and Sentis (2014) used both the event-study and calendar-

time approaches to estimate the long-run performance of both clusters (i.e. orphan

IPOs5 and non-orphan IPOs6). They observed poor performance of both types of IPOs

in the long run relative to the market portfolio during the sample period.

1.2 Overview of IPOs in the Pakistan Stock Exchange

This section provides the introduction, types and history of all stock exchanges in

Pakistan. This section also provides the information of IPOs activity in Pakistan since

last couple of decades.

1.2.1 The Pakistan Stock Exchange

The Pakistan Stock Exchange (PSX) was established on January 11, 2016 when the

Government of Pakistan (GOP) decided to merge the Karachi Stock Exchange (KSE)

established in 1947, the Lahore Stock Exchange (LSE) established in 1970 and the

Islamabad Stock Exchange (ISE) established in 1992 under the Corporatization,

Demutualization and Integration Act, 2012 process which initially defined in August

2012 in order to reduce market fragmentation and build a sturdy stance for attracting

strategic partner required for technological expertise. The PSX was converted into a

public limited company with the ownership rights were segregated from the trading

rights. The PSX has three trading floors in Karachi, Lahore and Islamabad while the

establishment is laid down in Karachi to regulate and surveillance the market

activities. In recent years, the Pakistan stock exchange was reclassified as an MSCI

Emerging Market in the 2016-2017 review and FTSE as a Secondary Emerging

Market because PSX delivered above 40% returns in 2012, 2013 and 2014 (see Table

1.1) which reclassified it as the best emerging market awards in each year (source;

The News, July 22, 2016). Figure 1.1 presents the regulatory institutions and capital

market structure in Pakistan.

As on June 30, 2017, there were 560 companies listed on the exchange with 35

sectors, the total listed capital was PKR 1,312 billion and the total market

capitalization was PKR 9,522 billion. The PSX has now seven indices as the KSE-

5 The IPOs without carrying recommendations from the financial analyst during IPOs placement process 6 The IPOs carrying recommendations from the financial analyst during IPOs placement process.

5

ALL Shares Index, KSE-100 Index, KSE-30 Index, PSX-KMI All Shares Index,

KMI-30 Index, Oil & Gas Sector Index and Banking Sector Index (Source: PSX Daily

quotation of June 30, 2017). There are about 400 members which are brokerage

houses of the exchange, from out of 400 members, 21 members are operating as asset

management companies (AMCs). At current, the investors registered on the PSX

capital market includes as 1,886 foreign institutions, 883 local institutional and above

220,000 individual investors (PSX Annual Report 2017).

Figure 1. 1: Regulatory and Operational Structure of Pakistan Stock Exchange

1.2.2 The History of Karachi Stock Exchange

One of the oldest stock exchanges in the South Asia, the Karachi Stock Exchange was

established on September 18, 1947, Karachi, Pakistan. It starts working by five firms

with a paid-up capital of PKR 37 million rupees and launched the manual trading

system generally known as the open-out-cry system. Before 1991, the KSE50 index

was used as a benchmark, while in November, 1991 the KSE100 index was launched

with a base index of 2,000. The KSE was one of the largest stock exchanges in

6

Pakistan before the implementation of demutualization and integration process. The

KSE was declared one of the top 10 stock markets in the world in 2015. According to

Bloomberg, the benchmark of PSX (KSE100 index) is the 3rd best benchmark in the

world since 2009. Since its establishment, over 70 years of age, the KSE has enabled

by helping a wide range of participants from retail investors to foreign institutional

investors, the investor’s community and the firms listed for trading. Specifically, the

KSE engaged helping the large firms and/or new entrepreneurs to generate long-term

funds from the general public by selling their shares.

1.2.3 The Performance of Karachi Stock Exchange

According to Ahmad (2000), the KSE is unpredictable and the most volatile exchange

in the world. However, the KSE has shown hard to believe performance during the

2000-2010 periods and also in subsequent years except in 2001 and 2008 years due to

the US internet bubble crisis and the US Subprime mortgage crisis respectively. When

the market benchmark dropped more than 50% during 2008 due to the US Subprime

mortgage crisis effect, the management of KSE decided to lock the further drop in

benchmark index, which creates another issue of liquidity crunch because investors

were unable to sell their shares anymore for a couple of months. Due to this

irregularity, MSCI reclassify the status of KSE from emerging markets to the frontier

market, which raised another panic for the investors. The KSE-100 Index has

produced 48.9%, 49.4% and 27.2% gains in 2012, 2013 and 2014 years respectively.

Due to this tremendous performance, KSE remains at top 10 markets of the world. In

2016, the KSE was reclassified as an MSCI Emerging Markets and FTSE as a

Secondary Emerging Market due to the consistent performance of PSX and the recent

capital market developments. At present, the PSX is well thought-out a possible

investment opportunity for the investor community. Table 1.1 and Figure 1.2 present

the historical performance of KSE during the 1995-2016 periods.

7

Figure 1. 2: The Performance of KSE-100 Index during 1995-2016

Table 1. 1: The Performance of KSE-100 Index during 1995-2016

Year %age Gain Opening Closing Min Max Std Dev Days

2016 45.681 33,229 47,807 30,565 47,807 4,433.49 248

2015 2.132 32,480 32,816 28,927 36,229 1,193.30 249

2014 27.196 25,609 32,131 25,479 32,149 1,725.02 246

2013 49.427 16,795 25,261 16,108 25,579 2,656.20 247

2012 48.976 11,282 16,905 10,909 16,943 1,522.99 249

2011 -5.613 11,849 11,348 10,842 12,682 429.24 248

2010 28.077 9,438 12,022 9,230 12,031 620.94 248

2009 60.050 5,753 9,387 4,815 9,846 1,354.85 246

2008 -58.337 13,353 5,865 5,865 15,676 2,566.31 218

2007 40.204 10,067 14,077 10,067 14,815 1,204.40 245

2006 5.063 9,652 10,041 8,769 12,274 666.01 241

2005 53.681 6,222 9,557 6,222 10,126 892.42 250

2004 39.066 4,474 6,218 4,474 6,218 321.81 248

2003 65.528 2,701 4,472 2,356 4,604 688.47 248

2002 112.198 1,273 2,701 1,273 2,701 274.31 261

2001 -15.557 1,508 1,273 1,075 1,550 97.39 261

2000 7.004 1,457 1,508 1,276 2,054 202.15 260

1999 49.053 945 1,409 852 1,430 134.87 261

1998 -46.104 1,754 945 766 1,754 318.18 261

1997 30.796 1,341 1,754 1,339 2,068 166.99 261

1996 -10.656 1,501 1,341 1,332 1,854 133.12 262

1995 -28.010 2,085 1,501 1,323 2,085 161.28 258

Source: PSX DataStream

0

10,000

20,000

30,000

40,000

50,000

60,000

Jan

-04

-19

95

Jan

-04

-19

96

Jan

-04

-19

97

Jan

-04

-19

98

Jan

-04

-19

99

Jan

-04

-20

00

Jan

-04

-20

01

Jan

-04

-20

02

Jan

-04

-20

03

Jan

-04

-20

04

Jan

-04

-20

05

Jan

-04

-20

06

Jan

-04

-20

07

Jan

-04

-20

08

Jan

-04

-20

09

Jan

-04

-20

10

Jan

-04

-20

11

Jan

-04

-20

12

Jan

-04

-20

13

Jan

-04

-20

14

Jan

-04

-20

15

Jan

-04

-20

16

KSE 100 Index

KSE 100 Index

8

1.2.4 The History of Initial Public Offerings in Pakistan

From 1990 onwards, the capital market of Pakistan was accessible for foreign

investors under the capital market liberalization reform process. Due to heavy

investments plunge, the hundreds of Pakistani private and state-owned enterprises

decided to go public to raise equity capital for their expansions and debt repayments.

From 1992 to 1997, 272 IPOs were launched in the KSE, however, the highest

number of IPOs (86 firms) were launched in 1992 followed by 1994 (73 IPOs). The

evidence can be observed from Table 1.2, from 2000 to 2016 (the sample period of

this study) 90 IPOs were issued in the Pakistan where the year 2005 and 2007 were

the best years in terms of number of IPOs issued in each year. The performance of

IPOs issued in 1998 and 1999 was disappointing due to the Pakistan Atomic Missile

Tests and the Military Takeover. In addition, the IPOs activity in recent years is also

unsatisfactory as compared with the firms registered at SECP where thousands of

private and public limited firms registered in SECP during the study period,

particularly 3385, 3925, 3960, 4587, 5000 and 6200 in the year of 2011, 2012, 2013,

2014, 2015 and 2016 respectively (Source: the annual reports released by SECP).

Figure 1. 3: Shares Offered and Funds Raised during 1995-2016

The study did not include IPOs listed before 1999 primarily for two reasons: (1) many

scholars have stated the cases of “fly-by-night” capitalist who wear down the

investors' wealth from 1991 to 1997; (2) after 1998, KSE introduced computerized

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0

5

10

15

20

25

30

35

40

45

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

IPO Activity During 1995-2016

New Listings Funds Raised (million)

9

trading platform with full automation of back-office operations such as electronic

cash settlement and electronic transfer of shares facilities. From 2000 onward,

Securities and Exchange Commission of Pakistan (SECP) introduced more stringent

regulations.

Table 1. 2: Shares Offered and Funds Raised during 1995-2016

Year New Listings Shares Offered Funds Raised

1995 41 569,405,300 837,578,250

1996 30 222,652,500 313,650,000

1997 4 66,250,000 66,250,000

1998 1 9,960,000 9,960,000

1999 0 0 0

2000 3 46,500,000 542,000,000

2001 2 21,250,000 300,000,000

2002 4 87,754,000 877,540,000

2003 3 42,250,000 470,937,500

2004 8 298,606,710 9,287,835,220

2005 14 424,922,000 11,766,795,000

2006 3 91,100,000 1,126,500,000

2007 11 253,535,500 15,988,747,000

2008 9 171,000,000 4,552,500,000

2009 3 151,160,000 1,511,600,000

2010 6 514,333,334 6,758,000,020

2011 4 189,216,000 3,078,664,480

2012 3 50,000,000 500,000,000

2013 1 56,976,000 1,253,472,000

2014 5 213,949,500 6,547,888,500

2015 7 728,377,857 14,442,470,707

2016 4 159,751,000 4,459,437,500

Total 4,368,949,701 84,691,826,177

Source: SECP official website

1.2.5 The Listing Procedure of IPOs in Pakistan Stock Exchange

As discussed earlier, the decision of going public is an important phase of the life

cycle of the issuing firm. IPOs not only allow to raise funds from the primary market

to meet their financial needs but also offer an opportunity to investors to take part in

the growth of issuing firms. In each country, different regulatory institutions are

engaged to control, regulate and monitor the process of new listings. In Pakistan, the

SECP and the PSX are the main regulatory institutions and the step-by-step listing

procedure with respect to expected time involved is reported in Table 1.3.

10

• According to section 3 of The Issue of Capital Rule 1996, the issuing firm can

raise capital through the public offer for the first time which owns a loan-

based project or an equity-based project.

• According to section 4 of The Issue of Capital Rule 1996, In case of shares

offered at a premium, the issuing firm shall have the profitable operational

record of at least one year and justification of premium shall be disclosed in

the prospectus.

• According to Companies Ordinance 1984, the ordinance allows new issuance

to the public only through the approval of prospectus from the stock exchange

and commission. According to section 9 of SEC Ordinance 1996, an issuer

firm who plans to get listed on PSX shall submit an application.

• According to section 5 of PSX Rule Book,

o An issuing firm shall have a post-issue paid-up capital of at least PKR

200 million.

o In case of post-issue paid-up capital of an issuing firm is up to PKR

500 million then the allocation of capital to the general public shall not

be less than 25% of the post-issue paid-up capital.

o In case of post-issue paid-up capital of an issuing firm is above PKR

500 million then the allocation of capital to the general public shall be

at least PKR 125 million or 12.50% of the post-issue paid-up capital,

whichever is higher.

o An issuer may allocate shares up to 5% and 25% of the public offer to

their employees and overseas Pakistani respectively.

Most of the information has been extracted from AKD Securities Limited7, provided

the details of new offerings in Pakistan with respect to expected time required for

each step. The step-by-step listing procedure of new offerings is reported in the Table

1.3.

7 AKD Securities Limited is one of the leading investment bankers in Pakistan and Mr. Jasim Sarwar facilitate the researcher in order to provide detail information and required data for IPO placement related activities.

11

Table 1. 3: Step by Step IPO Listing Procedure in Pakistan Stock Exchange

Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Week 15 – Week 17

Step 1

Agreement with Lead

Underwriter/Advisors Step 2

Due diligence of company Drafting of Prospectus Step 3

Financial Feasibility & Model

Drafting of the information

Memorandum Filing of Listing

Application to PSX

(Payment of Listing Fees)

Step 4

Appointment of BR/ LA/

Balloter/ CDS

Eligibility/Shares Registrar

Application to SECP for

Approval of Preliminary

Prospectus

Step 5

Floor Price Setting Circulation and

Publication of Pre.

Prospectus in

newspaper

Step 6

Finalization of Prospectus Clearance of Preliminary

Prospectus by PSX Publication and

Circulation of Final

prospectus

Step 7

Floor Price Approval Marketing/Road

Shows

Balloting in case of

Oversubscription Note:

Approval from Cabinet

Committee on

Privatization incase of a

State-owned Enterprise

Approval for Listing by

SECP BR Book Runner

Book Building

Registration / Dutch

Auction (not required

in case of fixed-price)

Refund of Bids from

BookBuilding Portion

Refund of unsuccessful Bidders

LA Legal Advisor

Transfer of Shares to

investor CDS Accounts

CDS Central Depository System

Strike Price General Public

Subscription

Formal Listing

PSX Pakistan Stock Exchange

SECP Securities & Exchange Commission of Pakistan

Receipt of Funds

Sources: Chapter 5: Listing of Companies and Securities Regulations of PSX

12

1.3 The Issues Related to IPO Valuations and Pricing

Apparently, it has been observed that the discussion regarding selecting

comparable firms for valuation is very little in the emerging economies. As a result,

the practitioners usually employ explanation of comparable firms based on developing

economies when valuing IPOs using multiples in the circumstance of emerging

economies (Goh et al., 2015; Ivashkovskaya & Kuznetsov, 2007). By comparing the

predicted valuation accuracy adjusted by the country risk of the US and Russia,

Ivashkovskaya & Kuznetsov (2007) argued that the price-book (P/B) and enterprise

value-sales (EV/S) multiples produce more valuation accuracy than the price-earnings

(P/E) valuation accuracy. In the beginning, this study hypothesized that the

performance of valuation accuracy of numerous valuation methods in emerging

economies may be varying than the developed economies. Park and Lee (2003)

observed that the Japanese investment banks usually prefer multiples for the

simplicity of IPOs valuation compared to the direct valuation methods. Schreiner and

Spremann (2007) conjectured that the criterion to select comparable firms is

complicated to be recognized because each firm confronts different business issues.

Damodaran (2007) argued that the practitioners unable to implement multiples

correctly because they mostly depend on subjective decisions. Goh et al., (2015)

examined the valuation accuracy of Malaysian IPOs listed in the agribusiness sector.

They examined that the median is a more accurate measure to estimate the

performance of valuation methods and price-earnings multiple outperform than the

other multiple measures. Mills (2005) findings reveal that the discounted cash flow

valuation method is widely used by practitioners. Kaplan and Ruback (1995)

compared the performance of DCF valuations with the valuations achieved from the

comparable firms from similar industries and find that the DCF valuations produced

high valuation accuracy than obtained from the comparables. This study disagree with

the conjecture of Houston et al., (2006), post-IPO valuation estimates are similar as

underwriter’s valuation before the IPOs. Furthermore, an extant literature of IPO

valuations (Kim and Ritter, 1999; Liu et al., 2002; Pumanandam & Swaminathan,

2004; How, Lam & Yeo, 2007; Colaco, Cesari & Hedge, 2013, 2017; Yoon, 2015;

Herawati, Achsani, Hartoyo & Sembel 2017) has mainly focused on the ex-post data

to estimate the fair value of new offerings. Roosenboom (2007) was emphatic about

this lack of attention when noticing that the IPO literature, particularly on valuation is

13

not very thick. In emerging market of Africa, Hearn (2010) mentioned the lack of

attention about the valuation of IPO literature and predicted the accuracy of several

valuation methods. Pereiro (2006) compounded the problems of less attention on IPO

valuation and pricing studies in emerging markets, investment banks usually applying

traditional methods used for valuation in the developed economies. Nwude (2010)

argued that the appropriate methods for Nigerian IPOs valuation and pricing are

problematic because of an extant literature unable to provide clear-cut valuation

methods.

It has been observed from a number of IPO studies, the underwriters did not disclose

their ex-ante valuation procedures in the offering documents (such as Prospectus). In

many advanced economies, researchers used analysts’ reports to estimate their fair

value estimates rather than the actual valuation procedures followed by the lead

underwriters. In the literature, only Abdulai (2015) in Ghana, Goh et al., (2015) in

Malaysia, Roosenboom (2012, 2007) in France and Deloof et al., (2009, 2002) in

Belgium used information of valuation methods disclosed in the prospectus to

examine the choice and valuation accuracy of different valuation methods. The

empirical findings of an extant literature about valuation accuracy based on the ex-

ante fair value estimates disclosed in the prospectuses rather than the ex-post

valuation estimates are not similar.

1.4 The Issues Related with IPO aftermarket Price Performance

The implementation of IPO process has two well-known puzzling stylized

facts across the world, namely as, 1) IPO underpricing (short-run abnormal returns)

phenomenon and 2) long-run underperformance (market adjusted negative returns for

subsequent years after listing) phenomenon. IPO underpricing is the difference

between preliminary offer price of IPO and first day closing price. These initial

returns are significantly positive across the countries but vary in percentages

(Ljungqvist et, al., 2006). Various theories have been proposed to explain the IPO

underpricing such as short run abnormal returns occurs due to agency theory (Jensen

and Meckling, 1976), information asymmetry hypothesis (Rock, 1986; Carter &

Manaster, 1990, Dolvin & Pyles, 2006), signaling theory (Allen & Faulhaber, 1989;

Welch, 1989; Jegaesh et al., 1993), the lawsuit hypothesis (Tinic, 1988; Hughes &

Thakor, 1992; Hao, 2007), prestigious underwriter hypothesis (Gordon & Jin, 1993;

14

Kumar and Tsesekos, 1993), and the ownership dispersion hypothesis (Booth & Chua,

1996). The aforementioned underpricing theories are thoroughly discussed in the

literature review chapter Section 2.2. When there is uncertainty about the 8intrinsic

value of IPO firm then some momentous mispricing is to be expected. Loughran and

Ritter (2002) explain the phenomenon of underpricing as “Leave so much money on

the table” when the market price is higher than the IPO offer price, due to bulky

underpricing, issuer loses wealth than they expected to be. It’s difficult to assess the

exact causes to IPO underpricing.

The IPO long-run underperformance is another anomaly observed in the

domain of public equity aftermarket performance analysis. The long-run poor

performance is explored by various scholars, over many years, across the countries.

Starting from (Aggarwal and Rivoli, 1990; Ritter, 1991; Loughran and Ritter, 1995;

Lowery, 2003), first time uncover the confirmation of negative abnormal returns over

the three and five years after the IPO. They argued that if IPOs are overvalued

systematically then in the long run market adjust this mispricing as long-run

underperformance. They called this underperformance phenomenon is called as ‘Fad’.

They conclude their findings that the long-run underperformance of IPOs reflect in

investor behavioral biases that cause them to be overoptimistic when IPOs are issued

in the hot-issue markets because when more information becomes available, the

market adjusts the initial mispricing in the long run price performance. This argument

is also confirmed by various scholars (Peterle and Berk, 2016; Agathee et al., 2012a;

Colak and Gunay, 2011; Moorman, 2010; Helwege and Liang, 2004; Nanda and Yun,

1997). A number of IPO studies have been trying to discover the reasons of long-run

underperformance under as the heterogeneous expectations hypothesis proposed by

(Miller, 1977; Houge et al., 2001), the agency hypothesis proposed by (Carter et al.,

1998; Johnson and Miller, 1988; Megginson and Weiss, 1991; Carter and Manaster,

1990; Brav and Gompers, 1997), the signaling hypothesis proposed by (Welch, 1989;

Jegadeesh et al., 1993; Ljunqvist, 1996; Koh et al., 1996). The aforementioned long-

run underperformance theories are thoroughly discussed in the literature review

chapter Section 2.3.

8 Intrinsic value is same as market value and it is derived based on current financial position, discount

all business risk factors and future cash-flows.

15

Another implication of long-run underperformance caused by inaccurate

estimation of abnormal returns is discussed by various scholars. This inconsistency

has been investigated in three different ways as: (1) the issue of accuracy and biases

of measurement methods such as event-time approach (BHARs and CARs) and

calendar-time approach (Asset pricing models such as CAPM, Fama-French multi-

factor model, Corhart four-factor model etc) are discussed by (Loughran and Ritter,

2000; Barber and Lyon, 1996; Fama, 1998), (2) the second issue with

underperformance is the selection of market benchmarks to risk-adjusted returns are

discussed by (Kothari and Warner, 1997; Barber and Lyon, 1997; Ritter and Welch,

2002; Gompers and Lerner, 2003; Choi, Lee and Megginson, 2010), and (3) the third

issue with underperformance is the power of statistical tests used for the long-term

performance analysis are discussed by (Loughran and Ritter, 1995; Brav, 2000;

Jenkinson and Ljunqvist, 2001). The issues related to long-run performance

measurement are thoroughly discussed in the literature review chapter Section 2.3.6.

Pakistan economy is effectively in a state of metamorphoses – simultaneously

developing, growing and transforming from loose regulated financial system to

synchronize. The primary markets of developing countries like Pakistan also confront

similar problems of IPO puzzling stylized facts in the process of implementing IPOs

in the capital market. The proposed study aims to examine the IPOs valuation on the

basis of prospectus document which includes all relevant information about the IPO

issuer firms.

1.5 Problem Statement

It is important to take an academic review of the IPOs valuation practice on the PSX,

because in recent years, a seeming lack of confidence is observed by Pakistani

unlisted firms who are eligible to fulfill the prerequisites to issue IPOs but they

intentionally preferred to stay as private firms due to the issues about pre-issue

valuation biases, post-issue ‘mispricing’ on early trading days and inconsistent

policies by the regulators as discussed in the literature. In 1997-98, Asian

Development Bank (ADB) aid $50 million for capital market liberalization and

financial reforms. But disappointingly, since the financial reforms have been

implemented, the capital market is facing the issues of i) the stumpy market depth and

16

size, and ii) the high pace of delisting firms is more than the pace of new IPO

offerings in the market.

Table A.16 & Figure A.9 (see Appendix) present the details of year-wise delisting of

firms and listing IPO firms from 1997 to 2016, which motivated the scholar to

investigate the insights of ex-ante valuation process and ex-post price performance of

Pakistani IPO firms.

1.6 Research Objectives and Questions

The research objectives and research questions are discussed in this section.

1.6.1 Research Objectives

i. To investigate the firm-specific characteristics and market-related factors that had

influenced the choice of valuation methods when valuing IPOs.

ii. To investigate the firm-specific characteristics and market-related factors that had

influenced the valuation biase and accuracy associated with each valuation

method.

iii. To examine the usefulness of prospectus information on the initial valuations to

set preliminary prices in pre-issue pricing process.

iv. To inspect the impact of fundamental, ex ante risk and signaling factors on the

IPOs short-run and long-run adjusted returns.

v. To validate the robustness of IPOs long-run underperformance using asset pricing

models (Calendar-time approach).

1.6.2 Research Questions

i. What firm-specific and market-related factors had influenced the choice of several

valuation methods that are employed when valuing IPOs?

ii. What firm-specific and market-related factors had influenced the valuation biases

and accuracy associated with each valuation method?

iii. Does the prospectus information help to price the IPO firms in the ex-ante pricing

decision process?

iv. Does the prospectus information have an explanatory power on the short-run and

long-run adjusted returns?

17

v. Does the calendar-time approach validate the IPOs long-run underperformance

anomaly?

1.7 Significance of the Study

This study examines the valuation dynamics of Pakistani Initial Public Offerings:

their valuation practices, motivations and implications. Most developed market

challenges are generally augmented in the emerging markets. This study has five main

contributions. First, this study contributes and explains the IPO valuation practice in

Pakistan using the underwriter’s valuation analysis information published in the

prospectus documents. The findings suggest that the Pakistani underwriters frequently

used dividend discount model, discounted cash flow and comparable multiples

valuation approaches to estimate the fair value of issuing firm’s equity. The findings

also suggest that the investment banks choose each valuation method on the basis of

firm-specific characteristics, aggregate stock market returns and aggregate stock

market volatility before the IPO.

Second, based on the underwriter’s valuation analysis information, this study

evaluates the valuation biases and accuracy of each valuation method employed by

lead underwriters when valuing IPOs. The finding suggests that the DCF produce less

valuation bias and high valuation accuracy than the other valuation methods. This

disagree with Purnanandam and Swaminathan (2004), Kim and Ritter (1999), How,

Lam and Yeo (2007), Chang and Tang (2007), Sahoo and Rajib (2013), Yoon (2015),

and Colaco, Cesari and Hegde (2017) who completely rely on comparable multiples

valuations on the basis of post-IPO financial data rather than the real valuation

estimates of underwriters. So, the practitioners can’t completely rely on explanations

from the developed market findings.

Third, this study, first time in Pakistan, used the unique data in terms of, (1) the cash

and stock dividends adjusted prices to accurately measure the short-run and long-run

price performance of IPO firms, (2) for pre-IPO valuation analysis, 88 out of 94 IPOs

were taken as sample, (3) the 70% (65 out of 94) IPOs were used for post-IPO price

performance analysis that only survive at least five years since the date of formal

listings in the market, and (4) 225 non-IPO firms data (that remain listed from 2000 to

2017 in PSX) were used in capital asset pricing model (CAPM), Fama-French 3 factor

18

(FF3F) & 5 factor (FF5F) models to counter estimate the long-run IPO portfolio

performance.

Fourth, most studies analyzed the relationship of IPO valuation and underpricing

phenomenon (Klein, 1996; Beatty et al, 2002, Cynthia J, et al, 2008; Roosenboom,

2007, 2012; Glordano et al, 2011; Huge, 2013) and another form of studies examined

the relationship between initial excess returns and long-run abnormal performance

analysis (Levis, 1993; Ritter, 1991; Lian and Wang, 2009; Xia et al, 2013). This study

examined the three different elevations involved in the implementation of IPO process

as (1) the pre-issue valuation analysis commenced by the lead underwriters, (2) the

initial excess returns analysis due to initial ‘mispricing’ phenomenon, and (3) the

long-run abnormal returns analysis due to long-run underperformance phenomenon.

Fifth, this study provide a comparative analysis of long-run performance using the

Event- and Calendar-time approaches to address the issues related to mis-

measurement of long-run returns. This study employed the cumulative abnormal

returns (CARs) and buy-and-hold abnormal returns (BHARs) as Event-time approach,

while capital asset pricing model (CAPM), Fama-French three factor (FF3F) and

Fama-French five factor (FF5F) models as Calendar-time approach to study the

sensitivity of price performance under equally- and value-weighted returns methods.

This study, first time, provides the application and comparison of most recent FF5F

model with other asset pricing models in the literature of the IPO, especially in the

context of emerging markets.

Finally, the existing literature inspected these issues for developed and developing

markets but the country like Pakistan is not taken into account and this study is

devoted to analyzing the numerous aspects related primary market of Pakistan. The

primary markets of developing countries like Pakistan also confront similar problems

in the process of implementing IPOs in the PSX. The findings of this study contribute

to the knowledge that to what extent the Pakistani capital market has converged on the

way to or diverged from a mature market. The portfolio managers can devise their

trading strategies under the findings of this study in order to get superior returns due

to the short run abnormal returns. The regulatory institutions can modify their

procedures for prospectus approval & listing permissions in order to control

19

irregularities, observed in pre-issue fair value estimates and post-issue underpricing &

underperformance, and to attract more listings get listed and to increase investor base.

1.8 Structure of Dissertation

This dissertation consists of five chapters. In the current Chapter one- Introduction,

the brief introduction and background of Initial Public Offerings (IPO), the brief

overview and listing procedure of IPOs in Pakistan, issues related IPO valuations,

issues related aftermarket performance of IPOs, research objectives and questions,

and significance of the study is discussed. The Chapter two is about Literature Review

and divided into three parts. First, literature about IPO valuation is discussed in two

ways as (1) IPO studies used ex-post data for the accuracy of valuation methods and

(2) IPO studies used ex-ante data for accuracy of valuation methods. Second, I discuss

the theoretical foundations and empirical evidence of short run abnormal returns and

long run underperformance of IPOs across the countries. Third, I discuss the issues

related methodologies to estimate long run IPO returns such as comparison of

Calendar Time and Event Study approaches. Chapter three is about Methodology and

Framework of this dissertation. In the first part, population and sample selection

criteria are discussed. The second part presents the methodology and hypotheses to

investigate the choice, bias and accuracy of the valuation methods used by the lead

underwriters. In the third part, discuss the methodology and hypotheses which

investigates the impact of fundamental factors, ex-ante risk factors and signaling

factors on the ex-post IPO performance. In the last, asset pricing models such

asCAPM, Fama-French three factor and Fama-French five-factor models are

discussed to estimate the IPO long-run performance. Chapter four presents the Results

and Discussion of this study. In the first part, the results of binary logistic and cross-

sectional regression models to estimate the impact of choice, bias and accuracy of ex-

ante valuation methods are discussed. In the second part, the results of cross-sectional

models are discussed to examine the impact of prospectus information on the ex-post

IPO returns. In the last part, the CAPM, Fama-French three- and five-factor models

are used to check the robustness of long-run underperformance. Chapter five is about

Summary and Conclusion of this study. In this chapter, the findings summary,

limitation and recommendations, direction for future research and policies

implications are discussed. The last contents of this dissertation are References and

Appendix.

20

2. Chapter 2

2. Literature Review

21

The initial public offerings (IPOs) have been scrutinized mainly with attention to ex-

ante valuation estimates, the theoretical rationalizations of short-run underpricing, the

methodological issues related to long-run underperformance and the most recent

corporate governance issues. A comprehensive literature reflects these issues in detail.

In this chapter, a number of studies are reviewed with respect to (1) the explanations

on the pre IPO valuation estimates with reference to choice, accuracy and bias of

alternative valuation methods, (2) the theoretical and empirical aspects of initial

excess returns also known as underpricing phenomenon and, (3) finally, the

explanatory power of prospectus and market information related to IPOs long-run

underperformance and relative priority of alternate long-run performance models such

as the event- and the calendar-time approaches.

2.1 IPO Valuation

The valuation about initial public offerings has devoted limited attention in the

existing literature. Valuation models have arrived a considerable empirical research in

the field of corporate finance in recent years. Based on the valuation theory, each

model should turn out the same valuation if they are properly constructed. On the

other hand, the relative dominance of each valuation model in academic research and

in practice is an unsettled issue. The literature regarding IPO valuations is categorized

on the basis of accounting information used by the lead underwriters and the financial

analysts’ to predict the valuation estimates.

2.1.1 Post-IPO Valuation Methods based studies

Boatsman and Baskin (1981) used two different types of price-earnings

models to compare the valuation accuracy of comparable firms and the accuracy

defined by the absolute values of prediction errors as a percentage of actual values.

Firstly, they select a random non-IPO firms from the similar sector, and then they

select firms from the similar sector with the most comparable last 10year average of

growth rate of earnings. They report that the valuation accuracy of the second model

is greater than the firm selected randomly in the first model.

Alford (1992) employs price-earnings multiples valuation method to

investigate the valuation accuracy of the IPO firms selected on the basis of industry,

firm size (a proxy for risk) and the growth rate of earnings using the data of

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comparable firms. Alford also examines the impact of earnings with adjustment for

cross-sectional differences in the leverage. He documents the results of most similar

firms selected based on the industry, the median absolute prediction error measured as

absolute values of prediction errors as a %age of actual values is 24.50%. His findings

using the size in addition to industry membership did not improve the accuracy of

price-earnings multiples method. Lastly, his findings document that the price-earnings

valuation method with adjustment for differences in leverage for all comparable firms

drops the valuation accuracy. He concluded that the industry membership is an

effective principle for selecting comparable firms.

Kaplan and Ruback (1995) examine the valuation accuracy of discounted cash

flow (DCF) valuation method in the setting of the management buyout and the

recapitalization on a sample of 51 highly leverage transactions of large and mature

firms using the adjusted present value model. They pointed out that the event

transaction prices are nearby to the present value of forecasted cash-flows and not

rejected the hypothesis of forecasts are made to justify the prices. They also document

that a CAPM based DCF valuation method has almost the same intent of valuation

accuracy as a comparable firms valuation method as EBITDA used as an accounting

measure. Gilson et al., (2000) also investigate the valuation accuracy of DCF and the

comparable firms’ multiples using the data of firms emerging from the bankruptcy.

They find that the degree of valuation accuracy almost similar in the both approaches.

They also point out that the financial interest of numerous stakeholders in the

bankruptcy proceedings distresses the projections of cash-flows that are used. Cheng

and McNamara (2000) examine the valuation accuracy of P/B, P/E and a combine

(P/E-P/B) valuation methods using the most similar non-IPO firms selected based on

the industry association, size and return on equity. They point out that, when firm’s

value is not available then the combine price-earnings and price-book valuation

method in the comparable firms’ industry association is better than the other valuation

approaches. They also find that the price-earnings valuation approach performs well

than the price-book and a combine (P/E-P/B) valuation methods. These findings

indicate that the both earnings and book values are value relevant but earnings are

more important than the book values and they are not the perfect substitute for each

other. They find that the P/B outperform better than the P/E valuation method when

23

peers are selected based on size-effect and the extent of valuation accuracy surges

with firm size and the size of the target firm’s sector.

Berkman et al., (2000) investigate the valuation accuracy of conformist DCF

and the price-to-earnings valuation methods using the sample of 45 New Zealand

IPOs floated 1989 to 1995. They find that the P/E comparable and DCF have almost

similar valuation accuracy because the median value of absolute pricing error is

approximately 20.0% and cross-sectional model explains 70.0% variation in the

market price when deflated by book value.

Kim and Ritter (1999) examine that how IPOs offer prices are set using the

comparable firms’ multiples valuation of 190 US firms from the same industry listed

from 1992 to 1993. They employ price-sales, price-earnings (P/E), enterprise value to

operating cash-flow and enterprise value to sales ratios to evaluate the offer price

valuation accuracy of the comparable firms. They document that the forecasted price-

earnings (P/E) lead to all other multiples in the valuation accuracy and this valuation

accuracy is more predictive for older firms than for younger firms. They also

document that the forward earnings per share (EPS) for succeeding years lead the use

of present year EPS. Moreover, underwriters able to achieve more valuation accuracy

by campaigning market demand before setting a final price.

Bhojraj and Lee (2002) try to develop a systematic approach to select the

comparable firm from peer groups on the basis of market-based multiples used as

equity valuation techniques using the large sample of US newly listed firms during

1982-1998. They conjecture that the selection of peer firms based on specific

characteristics that compel the cross-sectional fluctuation in the specific multiples

valuation. This study suggests that the proposed systematic approach select

comparable firms on the basis of profitability, growth and the risk characteristics of

each valuation method as explained by the relevant theories. They test the efficacy of

their proposed methodology of comparable firms’ selection criteria using up to three

years forecasted enterprise value to sales and price to book value ratios. Results reveal

that comparable firms selected in this respect get sharp improvements than the

comparable firms selected based on different methods.

Purnanandam and Swaminathan (2004) examine that how IPOs are valued at

the offer prices relative to fair prices using the sample of 2000 US non-financial

24

newly listed firms during 1980-1997. They employ price to EBITDA, price to sales

and price to earnings multiples of comparable firms to estimate the fair values. An

industry grouping was chosen on the basis of Fama and French (1997) SIC industry

codes and comparable firms were chosen based on their operating characteristics.

They observed that the median IPO is overvalued about 14% to 50% based on the

comparable firms from industry peers. When they add forward earnings in the

comparable firms’ selection criteria, the median IPO overvaluation is noticed about

33%. When they add earnings forecast along the industry membership and sales then

the median IPO overvaluation is observed 14%. These findings point out that the IPO

investors mainly focusing on too much analyst forecasted growth and less on the prior

profitability in valuing IPOs.

Firth (2008) compares the IPO share prices offered at the time of listing on the

capital market with the other valuation methods using the data from China listed IPOs

from 1992 to 2002. Their results point out that the price-earnings multiples have more

valuation strength in order to offer the quality of the issuer firm.

How, Lam and Yeo (2007) examine the efficacy of P/E and MTB multiples

estimated on the basis of firms’ management forecast of earnings and book-value of

IPO firm’s equities provided in the prospectuses are strappingly related with the

average prices to earnings and price to book value of two similar firms chosen based

on the industry membership SIC code, earnings growth and size without any prior

adjustment in the multiples. They find out that median prediction errors using the IPO

offer prices of price-to-earnings ratio were (-6.44%) to (-21.54%) and (1.95%) to (-

13.63%) when the price-to-book value was used. They also calculate median

prediction errors using the first trading day closing prices were (-1.58%) to (-13.60%)

for price-to-earnings and (14.25%) to (-1.78%) for price-to-book value multiples. The

findings suggest that the P/E and P/BV used on the basis of management prediction

reported in the offering documents such as prospectuses are closely linked to the

average of P/E and P/BV of matched firms (selected based on the industry

association, earnings growth and size).

Chang and Tang (2007) investigate the explanatory power of commonly used

multiples valuation techniques such as price-earnings, price-book value and price-

sales ratios during the IPO pricing process using the sample of 84 IPOs listed on

25

Taiwan Stock Exchange during 1991-1992 and 1995-1996. They choose comparable

firms based on the comparable revenue characteristics and found the poor accuracy of

aforementioned valuation approaches in evaluating IPO pricing. They find the

positive beta coefficient for peer group medians and suggest that the higher values for

IPOs are associated with the higher values of the peer group.

Table 2. 1: Post IPO valuation studies under different comparable benchmarks

Author(s) Country Sample

Size

Sample

Period

Comparable firms selection

methodology

Boatsman and Baskin

(1981)

US 80 1976 Randomly selected firms from

same industry with 10years

earnings growth rate

LeClair (1990) US 1,165 1984 Similar industry, current earnings

and trailing earnings

Alford (1992) US 4,698 1978,82,86 Industry SIC code, the degree of

risk and earnings growth rate

Kim and Ritter (1999) US 190 1992-1993 Newly listed IPOs on same

industry and firms shortlisted by

research boutique

Lie and Lie (2002) US 8,621 1998-1999 Industry SIC code

Bhojraj and Lee (2002) US 741 1982-1998 Comparable firms on the basis of

risk, growth and profitability

How et al., (2002) Australia 275 1993-2000 Industry membership, size and

growth

Purnanandam and

Swaminathan (2004)

US 2,000 1980-1997 Industry membership, size and

profitability

Cotter et al., (2005) Australia 71 1995-1998 Similar industry

Chang and Tang (2007) Taiwan 84 1991,92,95,96 Newly listed IPOs from same

industry

How, Lam and Yeo

(2007)

Australia 275 1993-2000 Similar industry, growth and size

Firth et al., (2008) China 745 1992-2002 Similar industry

Sahoo and Rajib (2013) India 120 2002-2007 Industry membership, revenue,

book value and return on new-

worth

Source: compiled from various research studies

Campbell, Du, Rhee and Tang (2008) investigate the initial underpricing and

the valuation accuracy with respect to information asymmetry, investor sentiment and

the underwriter reputation using the sample of 2140 IPOs listed during 1970-2004.

They estimate that initial underpricing is larger for overvalued IPOs as compare to

undervalued IPOs and positive association with the information asymmetry and the

investor sentiment. This study employs methodology consistent with the

Purananadam and Swaminathan (2004) as price-earnings, price-sales and price-

26

EBITDA multiples approaches to estimate the intrinsic value of IPO firms. They

don’t find any supporting evidence of systematic overvaluation or undervaluation of

IPOs on the basis of comparable firm’s accounting ratios.

Colaco, Cesari & Hedge (2013) explore the effect of retail investor sentiments

on the initial IPO valuations. They employ search volume index (SVI) as retail

investor sentiment using the sample of 147 US IPO firms listed during 2004-2011.

They employ methodology as comparable with Punanandam and Swaminathan (2004)

and the Liu et al., (2002) as price-earnings, price-to-sales, price-to-EBITDA and

price-to-assets multiples to measure the fair value estimates as compared with the peer

group multiples. They find that a typical SVI trend before the early estimation is

directly linked to the price-to-sales, price-to-EBITDA and price-to-assets multiples.

They argue that the reward to high net-worth institutions and investment bankers for

their particular responsibilities in the book-building process may be unwarranted since

they free-ride on the retail investor sentiments behavior fashions toward initial

valuations and they have not rewarded anyway and forced to buy shares at the high

prices.

Sahoo and Rajib (2013) empirically investigate the impact of multiples

valuation with comparable firms on the basis of: (i) industry membership, (ii) industry

membership and revenue, (iii) industry membership, revenue and book value and, (iv)

industry membership, identical revenue characteristics and the return on net-worth in

estimating the IPO offer prices using the sample of 120 Indian IPO firms listed during

2002-2007. They find that the comparable firms were selected on the basis of the

same industry having similar revenue characteristics, which have the significant

impact on the IPO pricing. They suggest that the peer group PER multiples selected

on the basis of book value, growth and leverage improve the accuracy of multiples

valuations. They point out that the peer group multiples selected based on similar

industry, revenue growth, book value per share and return on equity have 78.20%

accuracy power than the other estimates.

Yoon (2015) conducts an empirical analysis of the three most commonly used

valuation methods for equity valuation such as P/E, EV/EBITDA and the EV/Sales

multiples to investigate the mispriced securities using the calendar time portfolio

regression as advocated by Fama and French (1992, 1998). This study used alpha in

conventional CAPM from value-weighted and equal-weighted regressions for all

27

portfolios are significant which indicates that the multiples valuation approach can be

known as a good estimator to mispriced securities. This paper also used alpha in

three-factor model from value-weighted and equal weighted regressions are

statistically significant after controlling the size and value effect. These results

suggest that mispricing is concentrated in smaller size firms and noticeable in an

equal-weighted designed when size is controlled for in a multifactor model. These

findings are inline with Loughran and Ritter (2000), and Fama (1998) who observed

that the abnormal returns in equal-weighted portfolios are driven by smaller firms.

Furthermore, EV/Sales multiples valuation produces higher alphas than in the P/E

ratio and EV/EBITDA ratios. In a value-weighted one factor CAPM regression, the

price-earnings multiples perform better than the Sales and EBITDA multiples.

Colaco, Cesari and Hegde (2017) examine the impact of retail investor’s

attention on the early IPO valuation. They scan the association between the SVI as a

measure of investor’s attention with IPO valuations through the price to sales, price to

total assets, and price to EBITDA ratios using the sample of IPOs floated during 2004

to 2011. According to the Jumpstart Our Business Startups Act 2012 in the US, the

lead underwriters and the issuers are not allowed to communicate and produce the

earnings estimates before the filing of the initial registration, produce soliciting offers

and raise signals to heighten the demand from investors during the filing of the

prospectus and the initial price is filed. They conclude that, in the absence of

restricted settings for lead underwriters and the institutional control demand, the retail

investor’s concentration take the major part in the initial IPO valuations regardless

that retail investors are painstaking absurd frequently.

Herawati, Achsani, Hartoyo and Sembel (2017) compare the valuation of IPO

offer prices defined by the underwriters at the time of listing and with price-earnings

ratios (PER) using the sample of 240 firms listed on the Indonesia Stock Exchange

during 2000-2014. The results of this study show that the share prices estimated

through price-earnings ratio is greater than 65% of values that are offered at the time

of formal listing. Furthermore, they compare the valuation differences to the extent of

underwriter reputation and find that all underwriters have offered a significant offer

price discount to the fair price estimates. They argue that the underwriters offered a

guarantee to issuer firm through creating sufficient demand to augment investor’s

participation and also to investors through investment opportunity in riskier securities.

28

They suggest that the existing studies considered the reputation of underwriters in the

process of IPOs and auditor’s reputation is also equally importation for the issuer firm

in order to prepare quality financial statements which play a vital role in the valuation

of IPO pricing process.

In the literature of post-issue financial data valuation studies conclude that the

most of the authors completely rely on the comparable multiples, on the basis of

similar industry, size, revenue growth and earnings, valuation methods to estimate the

fair value of issuing firm’s equity while very few studies, only compare the valuation

estimates using DCF and comparable multiples method. Boatsman and Baskin (1981),

Alford (1992), Chang and Tang (2007) argu that P/E selected on the basis of industry

membership produce more valuation accuracy than selected on the basis of other

benchmarks, while (Purnanandam and Swaminathan, 2004; Chang and Tang, 2007;

Campbell, Du, Rhee and Tang, 2008; Colaco, Cesari & Hedge, 2013) P/E selected on

the basis of forward earnings produce higher valuation accuracy than the

price/EBITDA and price/sales multiples. Kaplan and Ruback (1995), Gilson et al.,

(2000) and Berkman, Bradbury and Ferguson (2000) found similar valuation accuracy

of DCF and comparable multiples.

2.1.2 Pre IPO Valuation Methods based studies

Deloof, Maeseneire and Inghelbrech (2002) first time used pre IPO valuation

estimations disclosed in the prospectuses to investigate the valuation accuracy of IPO

offer prices and aftermarket returns used by the underwriters. They took 33 Belgium

non-financial IPO firms based on the valuation methods disclosed in the offering

documents listed during 1993-2000. They use the percentage of valuation errors

within 15%, of mean absolute valuation errors and the mean squared valuation errors

measures to estimate the accuracy of valuation methods. They find that the investment

bankers used Discounted Free Cash Flow (DFCF), Dividend Discount Model (DDM)

and Multiples (price/earnings, price/cash flow, enterprise value/EBITDA, enterprise

value/sales, price/book value, dividend yield and price/earnings growth peer ratios)

valuation methods in the Belgium primary market. They highlight that DFCF is the

most popular method in Belgium. They point out that the DDM tends to

underestimate the intrinsic value whereas the DFCF generates unbiased intrinsic value

estimates which reveal that the underwriters deliberately offer price discount by

29

relying on the valuation approach that tends to underestimate the value. Their results

document that multiples valuation with forward earnings and cash flows leads to more

accurate valuations than the multiples valuation on the basis of latest financial year

earnings and the cash flows because the offer prices are closer to stock prices

estimated on the basis of forecasted earnings and the cash flows by adjusting more

valuable price-sensitive information.

Cassia, Paleari and Vismara (2004) investigate that how underwriters use

valuation methods to set preliminary IPO prices and their valuation accuracy using the

data of 83 Italian newly listed firms during 1999-2002. They extract “real-world”

valuation details from the prospectus document and observed that the Italian lead

underwriters employ multiples valuation and DCF methods to price IPOs. They

observed that EV/EBITDA, P/E, P/BV and P/CF are most commonly used multiples

in Italy. These studies uncover that the investment banks mostly depend on P/BV and

P/E ratios during IPO pricing process. On the other side, the results of EV to sales and

EV to EBITDA are overvalued from comparable firms than the estimates from the

IPO firms. The results come from P/E, P/BV and P/EBITDA is closer to IPO offer

prices. They argue that the valuing IPO is a great responsibility to lead underwriters to

avoid mispricing and also to build the reputation. Underwriters deliberately use

conservative measures to lease good taste for investors and successfully subscribe

their deals.

Roosenboom (2007) investigates that how French underwriters choose the

valuation methods to set preliminary IPO offer prices using the sample of 228 French

non-financial newly listed firms from 1990 to 1999. He used pre IPO characteristics,

valuation methods disclosed in offering documents and the unique data of detailed

valuation reports from the leading financial advisors. He employs binary logit model

analysis to enlighten the choice and accuracy of valuation methods. This study

observed that the French lead underwriters used the DCF, DDM, the peer group

multiples valuation estimates and EVA valuation methods to price IPOs and these

valuation methods are selected on the basis of firm characteristics, the aggregate stock

market returns and volatility before IPOs. This study points out that underwriters have

a preference to choose peer group multiples valuation when valuing rapidly growing,

technology and profitable firms. They prefer to use DDM when valuing older firms

from the mature industries because the older firms pay a large portion of their

30

earnings as dividends and when the aggregate stock market returns are low before

going public. The discounted free cash flow model and the economic-value added

valuation measures used when aggregate stock market returns are high prior to IPO.

This study document that the choice of valuation method is purely based on firm’s

industry membership, the aggregate stock market returns and the aggregate stock

market returns volatility but also partially based on the capital market dynamics,

industry circumstances and the firm-specific factors. This study also documents the

significant determinants of offer price discount as the underwriters with high

reputation are linked with the lesser discounts, growth in trailing earnings are

associated with lesser discounts but large offer price discounts are associated with the

riskier firms.

Deloof, Maeseneire and Inghelbrech (2009) investigate that how do leading

underwriters value the IPOs using the numerous valuation methods to set the

preliminary offer prices and the valuation accuracy of these methods to produce

unbiased estimations of 49 non-financial IPO firms listed on the Euronext Brussels

(previously known as Brussels Stock Exchange) from 1993 to 2001. This study used:

(i) the percentage of offer price to value estimates differences within 15% and the

mean absolute error to investigate the pricing error, (ii) mean and median estimated

value to market value estimation errors to highlight the degree to which value

estimates are biased, and (iii) the percentage of valuation errors within 15% and the

mean absolute errors measures used to estimate the valuation accuracy. They

observed that the discounted free cash flow, the dividend discount and the peer group

multiples valuation approaches used by Belgium investment banks, of which DFCF is

noticed to value in all the IPOs, the offer prices are value relevant to DDM

estimations when DDM is used, and the peer group multiples valuation based on the

forecasted earnings and cash flows produce more accurate estimates than the based on

the latest financial earnings and the cash flows disclosed in the offering documents.

They point out that the underwriters deliberately offer price discounts to DFCF value

estimates, DFCF is the most popular and reliable valuation technique whereas DFCF

produces unbiased results. The DDM estimations are more consistent with IPO offer

prices than the other valuation methods as DDM tends to underestimate the values.

Roosenboom (2012) investigates that how investment banks decide the IPO

offer prices, an ex-ante estimate of the market value and how these fair valuations are

31

subsequently used as a basis for IPO pricing using the sample of 228 non-financial

firms listed in France from 1990 to 1999. This study used a unique access of detailed

valuation reports by lead underwriters during the IPO pricing process. This study

employs the methodology as comparable with Francis et al., (2000) to evaluate the

bias, accuracy and explain-ability of the valuation method. This study observed that

the French lead underwriters used peer multiples, DDM, DCF, economic value added

and analysts-specific valuation methods to value private firm owner’s equity. This

study observed that the multiples valuation of the comparable firms is the most

popular valuation approach by French lead underwriters. The findings reveal that

DDM, DCF and the multiples valuation have similar bias, accuracy and

explainability. The lead underwriters intentionally “leave money on the table” to fair

value estimate in the process of IPO pricing. Underwriters market this offer price

discount during the IPO marketing campaign to amplify the investor’s participation in

the IPO auction or book-building process. This results in partial recovery in offer

price discount and turns out the high offer price updates. The findings are inline with

partial adjustment in offer price discount phenomenon and this phenomenon cause the

reason of large underpricings after controlling the investor demand.

Abdulai (2015) investigates the impact of choice, pricing and performance of

different valuation measures on post-issue performance of 30 IPOs listed on the

Ghana Stock Exchange (GSE) during 1992-2012. He employed binary logit and

cross-sectional regressions to examine the performance of valuation measures, for that

purpose, data obtained from the prospectuses and the GSE DataStream. He argues that

the DCF most commonly used method by underwriters. The findings of comparable

multiples document that the P/E and P/B are the most common measures used as

multiples while the DDM is a least popular measure and only used by one IPO firm.

The findings reveal that the operating profitability before the IPO is a significant

determinant of choice of multiples while firm age, size and dividend payout are the

key determinants of direct valuation methods.

Goh, Rasli, Dziekonski and Khan (2015) investigate the choice of different

multiples of newly firms related agricultural business sector listed in the Malaysia

during 2003-2009. They argue that the accuracy of each valuation multiples is based

on the; (1) a reliable benchmark multiples, and (2) to recognize the comparative

performance of comparable multiples and core elements of agricultural business

32

sector. They employ Price-earnings (P/E), Price-book (P/B), Price-cash flow (P/CF),

Price-sales (P/S), price-total assets (P/TA) and Return-on-equity (ROE) ratios of

comparable firms based on the industry membership and the firm’s rate of growth.

The findings reveal that; (1) the median is an accurate measure to estimate the

comparable multiples and the accuracy of valuation measures, (2) the ROE used as a

control factor adjustment in the comparable firms leads to unbiased valuation

estimates, (3) the price-sales (P/S) ratio perform worst in the valuation performance

while price-earnings ratio produce more accurate valuations when valuing the

agribusiness IPOs.

The most recent study, Rasheed, Sohail, Din and Ijaz (2018) investigate that

how lead underwriters select alternative valuation methods to value IPOs listed in

Pakistan. This study also unfold the value revelancy of each valuation method used by

underwriters. This study used sample of 88 IPOs floated during 2000-2016. They find

that underwriters prefer to choose DDM method when firms have corporate payout

record before the IPO and offered through the prestigious underwriters. The

investment banks are more lsikely to choose DCF when valuing young firms, rapidly

growing firms and that have more assets-in-tangibility. The investment banks prefer

to select comparable multiples when valuing mature firms and IPOs offered in bullish

sentiments. The findings of value relevance analysis unveil that the DCF has lowest

but P/B has highest predictive power to market estimates. Amir et al., (2018) also find

similar results with Rasheed et al., (2018).

The findings of IPO literature that used underwriter’s real valuation analysis

information to estimate the fair value and accuracy of several valuation methods

employed when valuing IPOs demonstrate that investment banks employed DDM,

DCF, P/E, P/B and economic value added method to estimate fair value estimates.

Deloof et al., (2009) found the similar valuation accuracy of DDM and DCF while

Deloof, Maeseneire & Inghelbrecht (2002) and Berkman et al., (2000) found that the

DCF valuation method produce more valuation accuracy than the P/E, P/B and other

multiples to estimate fair value estimates. On the other hand, Cassia, paleari &

Vismara (2004), Schreiner and Spremann (2007), Demirakos, Strong & Walker

(2010), Roosenboom (2012) and, Goh, Rasli, Dziekonski & Khan (2015) argue that

the comparable multiples such as P/E and P/B valuation multiples outperform the

DDM and DCF in order to produce more accurate fair value estimates. The literature

33

on valuation biases also depict that the P/E ratio produce less valuation bias than the

other methods (Deloof, Maeseneire & Inghelbrecht, 2002; Cassia, paleari & Vismara,

2004; How, Lam & Yeo, 2007), while Deloof, Maeseneire & Inghelbrecht (2009)

document that the DCF produce less valuation bias than DDM and on the similar

pattern, Francis, Olsson & Oswald (2000) report DDM produce less valuation bias

than the DCF.

2.2 The IPO Underpricing Phenomenon

The IPO underpricing is the difference between the preliminary offer price of

newly offered equity and the closing price over the first few days. These initial returns

are statistically positively significant across the world but vary in percentage

(Ljungqvist, 2006). When there is uncertainty about the intrinsic value of IPO firms

then some momentous mispricing is to be expected. IPO underpricing phenomenon

was firstly documented by Reilly and Hatfield (1969) and they took the sample of 53

US IPO firms listed during 1963-1965 and found initial returns range from 18.30% to

20.20%. After Reilly and Hatfield (1969) study, many researchers found similar

results by taking different time intervals and the sample sizes across the world (e.g.,

Neuberger & Hammond, 1974; Logue, 1973; McDonld & Fischer, 1972). Ibbotson

(1975) firstly, measured the initial excess returns and provides the comprehensive

explanation of potential determinants. He provides six possible explanations of

underpricing in the IPOs are as (1) the US Securities Exchange Commission (SEC)

provide instructions to the underwriters, determine the IPO offer price below than the

expected intrinsic value; (2) IPO issuer firms and underwriters deliberately offered a

discount to the investors in the IPO offer price “to leave a good taste in IPO” to make

seasoned equity offerings at attractive price; (3) Underwriters may favor to their

investors through the large underpricing; (4) underwriters deliberately present offer

price discount in the IPOs to control the uncertainity of undersubscription and

credibility; (5) investors may compensate issuers for large offer price discount by

some undisclosed mechanism, and (6) underwriters may present offer price discount

to protect issuers by the investor’s lawsuit. Loughran and Ritter (2002) explain the

phenomenon of underpricing as “Leave so much money on the table” when market

prices are higher than the IPO offer prices, due to bulky underpricing, issuer loses

wealth than they expected to be. It’s difficult to assess the exact causes to IPO

34

underpricing; various researchers try to explain this phenomenon with numerous

models and propositions.

2.2.1 The winner’s Curse Hypothesis

The most widely used underpricing model was built by Rock (1986) to discuss

the theoretical foundations of the IPO underpricing. Rock (1986) theorizes that with

fixed-price offers, underpricing arises due to information asymmetry between the

market participants. Rock assumes that investors who have more information called

the informed investors and less informed investors are called uninformed investors.

The informed investors struggle only for ‘good’ issues and there is a high probability

that the uninformed investors obtained more ‘bad’ issues due to less information

about fair value. Therefore, it is expected that the ‘good’ issues have excess demand

by market participants and the ‘bad’ issues face excess supply. This adverse selection

behavior is known as a winner’s curse problem. Thus, underwriters deliberately offers

a discount in the IPO preliminary offer prices in order to encourage uninformed

investors. Beatty and Ritter (1986) also used the Rock (1986) model to propose

another justification of IPO underpricing based on the Rock’s assumptions of

asymmetric information. They argue that the ex-ante uncertainty about the fair value

of issuing firm may affect the IPO underpricing. They argue that the degree of ex-ante

uncertainty is positively related to the degree of IPO underpricing. They used the

number of uses of IPO proceeds and the reciprocal of IPO gross proceeds as a proxy

for ex-ante uncertainty. They argue that the issuer firm unwilling to share the

information of proceeds utilization plan in order to avoid increased exposure to the

lawsuits and to meet stringent disclosure requirements by market regulators. This

implies that the ex-ante uncertainty is a key determinant to explain the degree of

underpricing. They conclude that the degree of ex-ante certainty is positively related

to the extent of underpricing. Keloharju (1993) also confirm the presence of winner’s

curse problem using the Finnish Capital Market data. Michaely and Shaw (1994)

examined the Rock winner’s curse hypothesis using the two IPO samples of relatively

homogenous IPOs and the general IPOs respectively. They find that the results are

consistent with the Rock hypothesis but the level of underpricing vary in both

samples.

35

The level of ownership structure is an important factor during IPO valuation

process. The recent literature, Bruton et al., (2010) examined the impact of ownership

concentration on the IPO performance in the France and UK, and found that

concentration ownership improves IPO performance. As agency theory stated, the

numerous researchers argue that ownership concentration decreases the asymmetric

information problems associated with disperse ownership (Barry et al., 1990; Shleifer

and Vishny, 1997).

2.2.2 The Prestigious Underwriter Hypothesis

This hypothesis keeps the same assumptions on information asymmetry

hypothesis proposed by the Rock (1986) among market participants, the investment

bankers and issuers hold more inside information about firm’s prospects than the

outside investors called uninformed investors. Baron (1982) firstly introduced the

model of underwriter reputation based on the agency theory to focus on the optimal

behavior of issuers as the principals and the underwriters as the agents to explain the

underpricing. The Baron’s model suggests that a investment banker has more

information about the firm’s intrinsic value and in turn results in an excess demand of

an IPO. He reveals the positive relationship between the demand of an IPO and the

underwriter reputation. The prestigious underwriters leave less money on the table.

There is an inverse significant relationship between the underwriter reputation and the

level of underpricing. Muscarella and Vetsyupens (1989) argue that the reputed

offering agents also face underpricing in their own IPOs. Beatty and Ritter (1986)

demonstrate the role of underwriters by taking care of their credibility in the

underpricing equilibrium. Therefore, the underwriters deliberately present limited

offer price discount to increase excess demand due to three necessary conditions.

First, the underwriters are uncertain about the demand and aftermarket price of the

IPO firms. Second, the underwriters have non-salvage reputation investment at stake.

Third, the underwriters may suffer from committing fees and the commission if they

offer too much money in the form of large underpricing or little. They also argue that

if the underwriters cheat during the allocation process, could lose the confidence of

potential issuers and the credibility among market participants. Following these

studies, various researchers investigate the impact of underwriter reputation on the

degree of underpricing and found a negative association between the ranking of

investment bankers and the extent of initial excess returns.

36

Furthermore, Johnson and Miller (1988) examined the relationship between

the underwriter reputation and the degree of underpricing by proposing another

hypothesis of adding ex-ante uncertainty in the model. They illustrate that the

underwriter prestige cannot change the underpricing equilibrium when the pre-IPO

uncertainty has been taken into account. When more information is available to the

investors then excess demand occurs by the informed investors. Based on the mean-

variance efficiency theory, a risky firm could not control the underwriting costs by

choosing a prestigious underwriter; on the other hand, firms can control the

underpricing through a higher investment banker commission. Therefore, the total

IPO transaction proceeds costs are positively related to the extent of ex-ante

uncertainty about the firm’s intrinsic value and isolated the impact of underwriter

reputation on underpricing. They used a sample of 962 US IPOs from 1981 to1983

and find a negative association between the level of underwriter reputation and the

level of underpricing.

Carter and Manaster (1990) used similarly to (Johnson and Miller, 1988)

procedure to assign the value of underwriter reputation by examining the tombstone

advertisements. By analyzing the relative placement of underwriters on the

advertisements as compared to other peers, each underwriter is allotted a rank from

nine to zero with respect to their appearance from top to bottom on the advertisement

respectively. The results of this procedure assigned zero to least prestige and the nine

to most prestige underwriters. They used the sample of 501 US IPO firms listed

during 1979-1983 and support the argument that the underwriters launch only less

risky IPOs in order to safeguard their credibility in the financial market. Therefore,

from the issuer’s point of view, the less risky firms try to choose prestigious

underwriters to signal the quality of the firm’s prospects.

Kumar and Tsesekos (1993) investigate the relationship between the

underwriter reputation and the type of commitment agreement. They considered the

use of over-allotment option makes benefit to the investment bank to build their

relationship with the potential investors and reputation through the overallotment

option as a provision in the underwriting contract. Hence, the overallotment option

allows the investment banker to satisfy the excess demand of investors. The

underwriters having low ranking have more interest in building a relationship with the

investors and also making a more investor base. They hypothesize that there is a

37

negative association between the relative size of overallotment option and the degree

of underwriter reputation. From the issuer’s point of view, the underwriters are

certified as financial advisors and set the IPO offer prices consistent with the inside

information about the firm’s forecasted cash flows. They find that the type of the

underwriting agreement determined the level of support offered by the investment

bankers.

Logue et al., (2002) investigate the impact of underwriter reputation during the

IPO process. Their results depict that the underwriter reputation is a significant

determinant during the due diligence process and the allocation of shares to the

investors, but less related to the post-IPO price stabilization motions and not related to

the post-IPO returns in the long run. They also explored that the underwriter

reputation is a key determinant of the IPO underpricing but not related to the IPO

long-run performance.

Amihud, Hauser and Kirsh (2003) used the IPOs data of Tel Aviv Stock

Exchange to perform a powerful test of the Rock’s model. They have the subscription

of each investor and their allocation were undertaken by equal pro rate basis during

their study period. As similar to Rock’s model, the small investors allotted greater

allocation in overpriced IPOs. Furthermore, the large number of orders was submitted

by informed and uninformed investors in the more underpriced IPOs, while

uninformed investors submitted more orders in the overpriced IPOs and ultimately

earned adverse excess returns across the sample offerings in which they subscribed.

2.2.3 The Signaling Hypothesis

Allen & Faulhaber (1989), Welch (1989) and Grinblatt & Hwang (1989)

introduced the signaling model based on the information asymmetry assumption to

explain the underpricing anomaly. Though, this model supposed that the issuer firms

know better regarding the future prospects instead of the external investors or the

underwriters. They also assumed that there are two types of firms, (i) good firms and,

(ii) bad firms. Though, the outside investors unaware about the quality of firms until it

is publicly available. Consequently, the quality firms reveal inside information to

potential investors and deliberately offer a discount in the preliminary offer prices to

give a signal of quality firms. In the extant literature of the IPO, firms employ

numerous variables as a signal of their quality such as selection of prestige

38

underwriters or auditors, prior IPO quality of management and executives, quality of

share capital structure and pre-IPO debt borrowings, and others. If signals work

successfully, the good quality firms split themselves from fewer quality firms with the

help of aftermarket performance. According to Ibbotson’s words, issuers deliberately

underprice their IPOs to ‘leave a good taste in the market’; therefore, this will assist

the firms to successfully accomplish the seasoned equity offerings in the market.

Thus, deliberate underpricing used as a signal to fetch a high price in the subsequent

equity offerings and could recover the loss of initial offerings (Jegadessh et al., 1993).

Various signaling models show different empirical relationships between the

underpricing and the intrinsic value of the IPO firms, the degree of a firm’s quality,

the project uncertainty, the seasoned equity offerings and the market sentiment at the

time of issue. The Grinblatt & Hwang model associate the project uncertainty to the

level of underpricing and the proportion of shares held by initial sponsors at the time

of offerings. They argue that the degree of underpricing is an increasing function of

the percentage of shares offered in the IPO event.The Allen & Faulhaber model

discuss another implication about the hot issue of market timings and the degree of

underpricing. This model provides that the market timing issue happens to the specific

sector when a regulatory or economic shock significantly impacts the profitability of

that particular sector. The signaling models proposed that the degree of underpricing

is an increasing function of ex-ante uncertainty before the IPO. The proposed

association is entailed by exiting signaling models until the strident market is required

to attain equilibrium that guarantees the degree of underpricing. The Welch’s model

assumes that the initial public offerings always followed by seasoned equity offerings

and the market value of IPO firms drop less upon news of SEO when a firm tries to

adjust underpricing equilibrium in order to change investors previous’s conviction

about firm value.

Welch (1989) shows there is a significant positive association between the

probabilities of firms’ undertaking seasoned equity offerings and the degree of

underpricing. Michael & Shaw (1994) used different sample period and their

empirical findings inconsistent with the signaling models. They find that the firms

with less underpricing experience high earnings and payouts in the aftermarket are not

consistent with the signaling model assumptions. They also find that the degree of

underpricing is not positively linked with the firm value and the ex-ante uncertainty.

39

They do not find supporting evidence for the positive association between the degree

of underpricing and the post-IPO payout policy which is proposed by Allen &

Faulhaber in the signaling model. They also investigate the long-run performance and

find that the firms experience to undergo seasoned equity offerings in the aftermarket

outperforms the non-issuing firms. On the other hand, they also do not find any

association between the degree of underpricing and the proportion of shareholding

with insiders in the long run.

Jegadesh et al., (1993) used the sample of 1,985 US IPO firms listed during

1980 to 1986 to investigate the relationship between the underpricing and the

probability of undertaking SEOs. Their results support the evidence of signaling

model hypothesis and find the positive association between the probability of

undertaking SEO and the underpricing. Espenlaub andTonks (1998) used the UK

IPOs data to investigate the association between the post-IPO directors’ sales and the

probability of undertaking SEO. They argue that the initial shareholders deliberately

underprice the IPOs to get benefits in the seasoned equity offerings. They proposed

that the there is a positive association between the degree of underpricing and the

probability of directors’ sales in the SEOs. Their results indicate the positive

association between the size of post-IPO executives’ sales and the degree of

underpricing but they do not find evidence that the post-IPO directors’ sales are a

significant determinant of conducting seasoned equity offerings. Keasay and

McGuiness (1992) investigate the implication of positive association between the

underpricing and the firm value using the UK USM digital data and used fifth-day

market capitalization is as a proxy for firm value. They find a positive association

between the underpricing and the firm value as predicted by the signaling hypothesis.

Su and Fleisher (1999) test the signaling hypotheses using the 308 IPOs listed during

1987 to 1995 in the China. They find stunning initial returns of 949% and argue that

their findings are consistent with the signaling hypotheses and the degree of initial

excess returns appears to be large to indicate an effect of rational economic

bargaining.

Yu and Tse (2006) test the reasons of underpricing in the Chinese primary

market using the sample period from 1995 to 1998. Their results do not support the

evidence that the underpricing is not mainly because of the signaling hypothesis but

consistent with the winner’s curse hypothesis. Chorruk and Worthington (2010)

40

investigate the initial returns using the 136 Thai IPOs listed between 1997 and 2008

and they find a underpricing of 17.60% and recognize that the initial excess returns

has not declined over time. Low and Yong (2011) estimate underpricing using the 368

Malaysian IPOs listed between 2000 and 2007 where the fixed-price mechanism is

most common for IPO process and control the underwriter’s knowledge of cumulative

demand for offered equity. They document an underpricing of 30.80% and argue that

the issuers offer their equities at a lower price to attract high demand and the signal of

high-quality firms.

2.2.4 The Lawsuit Avoidance Hypothesis

The insurance hypothesis is an additional underpricing explanation which

initially proposed by Ibbotson (1975) argues that the underpricing is used to avoid the

lawsuit from the external investors. Later on, Tinic (1988) and Hughes & Thakor

(1992) test this hypothesis and argue that the issuer firms and the underwriters

deliberately underprice their IPOs to avoid lawsuits from the outside investors.

Tinic (1988) tests the lawsuit-avoidance hypothesis and argue that the

expected cost of lawsuit file by outside investors would be sky-scraping9 for issuing

firms for the reason that to conduct the due diligence examinations which entail a

number of uncertainties and complications. As a result, underwriters and issuer firms

deliberately underprice their IPOs in order to avoid lawsuits situations. Furthermore,

in the IPO placement process, the issuer usually unaware of the disclosure

requirements and may possibly seem to be an unimportant part of the information to

be shared probably be observed a material oversight in a common deed. Secondly, the

underwriter assess the quality of management and their capacity to perform

organizational operations in a good manner based on the judgment and subjective

evaluation. Even, the speculative qualities and risks about IPO security are usually

mentioned in the due-diligence and prospectus documents. While the underwriters

and issuers both are at risk to the legal obligations and want to protect them would be

to buy jointly insurance policy against possible losses. The issuers and the

underwriters along with the legal protection from potential damages, they produce

information about the firm in order to successfully complete the transaction process.

9 The issuance of IPO being under the very high influence of SEC compliance and face a heavy plenty incase of failure to comply.

41

This may amplify the chances of post-offering sue filings by the outside investors and

the possible losses for the issuer firms. To keep safe from this situation, the issuers

pay a large amount to make sure the quality of underwriter valuations and charge a

mutually decided fee cuts when it found to be dodging. If underwriters could

formulate the provable standards for a due diligence examination then the cost of

insurance policy could contain a premium for the moral hazard. Tinic argues that in

the absence of the legal protection against lawsuits, deliberate underpricing is an

efficient measure and the incomplete information disclosure in the offering documents

for both the underwriters and the issuers. Finally, deliberate underpricing reduces the

chance of the legal action and the potential damages in the situation of an adverse

evaluation.

Hughes & Thakor (1992) argue that the issuer firm hire underwriter to set the

preliminary offer price which acts as certify financial advisor and fully responsible

aftermarket lawsuits. They also argue that the underwriters set offer prices based on

their professional expertise and the subjective judgments, and would be litigated in

the future if it is observed the evidence of mispricing. The investment banks

deliberately underprice the IPOs to reduce the probability of litigation because of

legal action apparently expensive to underwriters and damage their reputation too.

They also suggest that there is a trade-off between minimizing the chance of a lawsuit

and maximize the IPO proceeds. In their proposed model, they suppose that the

reduction in litigation likelihood increases the IPO preliminary offer price, means the

positive association between the overpriced an IPO and the probability of a litigation.

Furthermore, deliberate underpricing reduce the probability of a litigation, adverse

ruling conditional being filed by investors and the underwriter reputation in case of an

unfavorable ruling.

In order to investigate the lawsuit-avoidance hypothesis, Tinic used 204 US

IPOs and use the year 1933 as a cut-off point in time about the Securities Act of 1933.

She divides the sample period into two categories. The first pool consists of 70 IPO

firms listed during 1923-1930, and the second pool consists of 134 IPO firms listed

during 1966-1971. Before the legislation of SEC Act of 1933, the principle of caveat

emptor filed for listing in an almost open way, the underwriter and issuer do not face

lawsuit uncertainty. Since 1933, the pace of underpricing has gone up with

simultaneously to amplify the probability of the future lawsuit. The evidence sustains

42

the proposition that the underwriters deliberately underprice their IPOs to avoid

litigation for misstatements in the offering documents. Drake and Vetsuypens (1993)

investigate 93 IPO firms who were subsequently sued under the legislation of SEC

Act of 1933 during the period 1969 to 1990. They explored that the buyers of

overpriced IPOs are just as probably sued as of underpriced ones. Therefore, they find

that the underpricing does not control the probability of a lawsuit-avoidance and also

argue that the deliberate underpricing is a costly form to avoid future litigations.

As compared to the lawsuit-avoidance hypothesis, Ruud (1993) argue that the

underwriters underprice their IPOs to support aftermarket trading to fall below the

certain level instead to avoid future litigations. The investment banker supports the

aftermarket share prices through the repurchasing of a sizeable number of shares

when it reaches below the offer price and resulting the price goes up higher than the

initial offer price and leave a good taste to investors. Jenkinson (1996) supports the

Ruud hypothesis that the underwriters deliberately offered a discount in the offering

price to leave a good taste to investors in the post-IPO period. Prabhala and Puri

(1999) present the evidence against the arguments proposed by Tinic. They compare

the firms listed before 1933 and firms listed during 1985 to 1994 and assume that the

firms listed prior 1933 should be riskier than the firms went public during 1985 to

1994 indicating that the underwriters faced more lawsuit uncertainty than the firms

went public during 1985 to 1994which used the IPO offer prices, the proportion of

shares offered and SD of initial returns as proxy for IPO risks and explored that the

firms which went public during 1985 to 1994 were riskier than the firms went public

before 1933. Finally, they argue that the differences in underpricing before 1933 and

during 1985 to 1994 are possibly on the basis of differences in the IPOs risk instead of

the risk obligatory by SEC Act of 1933. Lowry and Shu (2002) employ methodology

comparable with (Drake and Vetsuypens, 1933), but identify that they ignore the

endogeneity bias of the relationship between the underpricing and the risk of a

lawsuit. They empirically examine the lawsuit-avoidance hypothesis by investigating

the association between litigation uncertainty and underpricing. They supposed that

firms having higher chances of lawsuit produce higher underpricing to avoid legal

obligations and firms having large underpricing to control the chance of being sued.

They used the sample of 1,841 newly listed firms during 1988-1995 and used

43

simultaneous equation system to test proposed hypotheses. Their results provide the

evidence of a lawsuit-avoidance hypothesis.

Hao (2007) test the lawsuit-avoidance hypothesis using the IPOs listed

between 1996 and 2005 and finds no evidence for litigation risk hypothesis. She

suggests two possible rationales about results. First, the probability of being sued

about directors & officers, and errors & omissions has been replaced with IPO

underpricing as an alternative measure to control the potential damages of future legal

obligations. Second, she identifies that the Private Securities Litigation Reform Act of

1995 and the Securities Litigation Uniform Standards Act of 1998 have substantially

minimized the risk of the lawsuit by the US investment bankers, to control the

requirement to purchase litigation insurance in the course of underpricing. Lin,

Pukthuanthong and Walker (2013) investigate that the pre-IPO characteristics

including the initial excess returns have a minor effect on the litigation uncertainty

and the risk of being sued is commonly determined based on the ex-post activities

including comparable firms’ downturns. Hanley and Hoberg (2010) used recent IPOs

data and find that the issuer firms deliberately share more information about the

utilization plan of the IPO proceeds and the potential risk factors involved in the

ongoing business operations. This deliberate disclosures possibly reduce the

probability of litigation risk.

2.2.5 The Prospect theory

The prospect theory of Kahneman and Tversky (1979) argue that the

individuals devise options under uncertainty maximizing a value function other than

an expected utility function that set priority according to the extent of expected utility.

Loughran and Ritter (2002) proposed a prospect theory, based on the investor

behavior to explain why issuers are willing to leave money on the table. Theory

suggests that issuer firms only concentrate on changes in their wealth instead of the

lost of wealth in the underpricing. The assumption of this theory explains that the

issuers leave a lot of money on the table because they are expecting a higher price in

the initial days, resulting to offset the loss of wealth in underpricing and the net gains

in their corporate wealth. They explore that the initial excess returns are mainly

dependent on the degree of underpricing when preliminary prices increased as

compare to price bands. They unfold the explanatory power of initial returns which

44

primarily based on the information disclosed in the offering documents. Moreover,

they explore that the filing range of offer price does not completely represent the

public information. Loughran and Ritter build the arguments from the prospect theory

that the issuer could consider the opportunity costs of benefits and the losses varying

relative to expected IPO net proceeds. They proposed that the issuers are reluctant to

make guarantees in case of ex-post surprises due to unswerving asymmetry with

prospect theory. Loughran and Ritter further interpreted the prospect theory that the

degree of initial excess returns depends on competitive restraint. The competitive

forbearance particularly plausible in Japan with having a track record of

governmentally engineered and insist on cartels. On the other hand, in the US, they

find that the style of partial adjustment for low market share investment banks is same

as the other industry leaders.

Loughran and Ritter (2002) investigate that the public information

incorporated partially based on the willingness of both the issuers and underwriters

“higher than necessary” underpricing. They proposed a behavioral explanation that

IPO issuing firm care regarding the change in wealth than the extent of underpricing.

The initial sponsors’ wealth affect represents the sum of two elements: money left in

shape of underpricing and the dilution effect of shares offered in an IPO, plus money

gained as shares retained by initial shareholders. If the difference between two

elements more than the loss from first element then according to the prospect theory,

initial shareholders will be content.

2.2.6 An International Empirical Evidence

The extant literature on initial public offerings aftermarket performance has

provided support to the underpricing phenomenon approximately exists in across the

world markets. The various theoretical explanations of underpricing is already

discussed in the previous sections and this study document some empirical evidence

of this phenomenon from around the globe. In the US market, during 1980-1990

periods, underpricing was approximately 7%, but during 1990-2000 periods, it

becomes double around 15%. Liu and Ritter (2010) investigate the underpricing using

the sample period of from 2001 to 2008 and found the underpricing of 12%. These

studies point out that the degree of initial excess returns has changed over time.

45

Chambers and Dimson (2009) used UK IPOs data listed during 1989-2007 periods

and found the underpricing of 19%.

Perera and Kulendran (2012) study the Australian primary market and found

the underpricing of 25.47% on the first day and 23% on the 10th day using the

cumulative abnormal returns method. In a study of the New Zealand market,

Alqahtani and More (2012) investigate initial returns of IPOs and found the

underpricing of 9.16% due to lower risk of issuing firms. Falck (2013) tests various

theories of underpricing by the Norwegian IPOs data and document an underpricing

of 3.14%, their empirical findings support the information revelation theory was a key

explanation of underpricing phenomenon. Kucukkocaoglu (2008) investigate the

initial returns on early trading days using the IPOs data. He argues that the initial

returns were higher when investment banks used fixed price and book-building

mechanisms to launch the IPOs. He identified that the underpricing on the 1st trading

day and for the 1st month were 11.73% and 14.12% respectively. Bernnan and Franks

(1997) have investigated the dilution of ownership and the control of management

evolves in the IPO process in order to analyze and retain the post-IPO ownership due

to the IPO underpricing. They used 69 IPOs data from the London Stock Exchange

newly listed firms during the 1986-1989 period. Their empirical findings highlight

that the initial underpricing is used to reach over subscriptions which allow initial

promoters to differentiate against big bids to avert block holdings. They argue that the

initial promoters release their 2/3rd of their ownership in succeeding offerings of 7

years while other executive directors hold only docile shares holding. Field and

Sheehan (2004) test the hypothesis that the managers deliberately underpriced their

IPOs to dissolve the ownership structure to attain the private benefits from less

scrutinized by the other stakeholders. They used the sample of 953 IPOs data and the

binary logit model to empirically test the proposed hypothesis and conclude that there

is no significant association between the initial returns and the outside block holdings.

Scultz and Zaman (1994) used the sample of 72 IPOs data listed on NASDAQ and

examined the underwriter’s aftermarket stabilization activity for the first three trading

days since the issue of IPOs. They support the evidence of underwriters aftermarket

stabilization activities and the answer of deliberate underpricing. They find that the

underwriters support aftermarket share prices through the buying when the price fell

below a certain level for both issues of hot and cold, eventually the IPO price

46

amplifies over the offer price. Yuhong (2010) examine the initial underpricing of

IPOs data listed during the internet boom period from 1999 to 2000 and dividing the

sample firms into two categories; (1) internet IPO firms and, (2) non-internet IPO

firms. His results highlight the underpricing of internet IPO firms was 88.60 % as

compared with the underpricing of non-internet IPO firms to be 44.70 %.

Table 2.2 presents the overall summary of initial underpricing on the first

trading day across the world capital markets but the level of underpricing varies from

country to country. This table provides underpricing from both developed and

emerging markets literature. Existing literature from emerging markets on IPOs

aftermarket performance has documented the higher underpricing relative to the

developed markets (Loughran et al., 1994). During the 2000 to 2010 decade, Sohail

and Nasr (2007) used the sample of 50 IPOs listed from 2000 to 2005 and employ

market adjusted abnormal returns method to measure the initial excess returns in the

Pakistani primary market and, found on average underpricing of 35.66% on the first

trading day. They also investigated that the ex-post uncertainty, offer size, over-

subscription and the market capitalization at 5th trading day were the significant

determinants of the underpricing. Hassan and Quayes (2008) investigate the initial

excess returns on the few early days using the IPOs data of Bangladesh and document

that the underpricing on the first day and 21st trading day are 108% and 119%

respectively. Sahoo and Rajib (2010) used the sample of 92 IPOs listed on the Indian

primary market during the 2002-2006 periods and found the underpricing on the first

trading day was 46.55% which mainly attributed to the investors over expectations.

Samarkoon (2010) documented the initial excess returns on the first listing day was

33.50% in the Sri Lanka. In the Nigerian market, Adjasi et al., (2011) investigate the

underpricing anomaly using the sample of 77 IPOs listed during the 1990-2006 period

and found that the underpricing of 43.10% at the first trading day. In the Malaysian

primary market, Abubakar and Uzaki (2012) reported the initial underpricing of

35.87% using the sample of 476 IPOs listed from 2000 to 2011. Deb (2009)

investigates the relationship between, (1) the initial underpricing and the ex-ante

uncertainty as compared with the Beatty and Ritter (1986), and (2) the initial

underpricing and the ex-post uncertainty as compared with Ritter (1984). He found a

strong positive association of both uncertainties with the initial underpricing.

47

Table 2. 2: IPOs Initial Excess Returns Studies From International Literature

Sources: Table in Loughran, Ritter, and Rydqvist (2010)

48

Lamberto and Rath (2010) examined the effect of prospectus information on

the survival of Australian IPOs listed during the 1995-1997 period. Their results show

the positive association between the survival of IPO firms with predicted dividend

yield and the IPO offer size whereas, negative association with the ex-post

uncertainty. Their results reveal that the IPO osffer size and the predicted dividend

yield are significant determinants for the survival of IPO firms. Islam et al., (2010)

studied the relationship of the underpricing and its driving factors by undertaking the

sample of 191 IPOs of Bangladesh listed during the 1995-2005 period. Their findings

reveal the positive association of the initial returns with the firm age and firm size,

whereas, negative association with the offer size and industry type while premarket

sentiments is an insignificant factor.

Datar and Mao (2006) reported the highest degree of underpricing in the

China. They investigate the initial returns of the Chinese market by taking the sample

of 226 IPO firms listed during the 1990-1996 period and found that firms were on

average underpriced at 388%. Hoque and Mousa (2001) also presented the highest

level of initial excess returns in the Bangladesh. They examined the initial

underpricing using the sample of 113 IPOs data listed from 1984 to 2001 and found

that the firms were on average underpriced at 285%. Borges (2007) analyzed the

underpricing of IPO firms before and after the 1988 financial crisis using the sample

of 98 newly listed firms. He divides the sample into two sub-samples, by taking 51

IPO firms before 1988 and post-crisis 41 IPO firms. He observed the higher

underpricing of 87.50 % in the pre-crisis period while the lower underpricing of 11.10

% was estimated in the post-crisis period. He also compares the underpricing of IPOs

offered by fixed-price and the bookbuilding mechanisms, the results reveal that the

IPOs launched by bookbuilding mechanism underpriced more than the launched by

the fixed-price mechanism.

By having a look on international literature of initial underpricing, this study

supports the evidence of above 100% of underpricing is observed in the China,

Jordan, Bangladesh, Malaysia and Saudi Arabia, the initial underpricing from 50% to

100% is observed in the India, Greece, Thailand, Brazil, Portugal and Korea. The

table also documented the lowest underpricing such as less than 20% observed in the

Hong Kong, Russia, Austria, Egypt, Israel, Norway, Canada, Chile, Argentina, US &

Denmark.

49

2.2.7 Underpricing in Pakistan

An extant literature on Pakistani IPOs, only a few researchers try to explore

the initial underpricing and its predicting forces. The underpricing phenomenon has

been unfolded by various researchers after the initiative of Sohail & Nasr (2007) and

Rizwan & Khan (2007).

Table 2.3 presents the overall look of IPO literature regarding the initial

excess returns in the Pakistan by undertaking various methodologies, sample sizes and

the sample periods. The major issue of Pakistani capital market is the slow pace of

new listings on the PSX market as compared to the high pace of new offerings in the

same regional exchanges such as the India, Bangladesh, Malaysia, Saudi Arabia and

Bangladesh.

Sohail and Nasr (2007) first time in Pakistan examined the aftermarket

performance of IPOs and dynamics of their initial returns by taking the sample of 50

IPOs floated during 2000-2006. They used the methodology as comparable with

Aggarwal, Leal & Hernandez (1993) for post-issue price performance and found that

the Pakistani IPOs are on average underpriced by 35.66%. They also undertook the

regression models in order to understand the magnitude and driving factors of

underpricing. They found that the oversubscription at the time of launching IPOs, the

market capitalization of IPO firm at a 5th trading day and ex-ante uncertainty are

statistically significant determinants and positive relationship with the underpricing

while the offer size has a negative association with the level of underpricing and

confirm the asymmetry information hypotheses. On the other side, market volatility,

the proportion of shares offered and the P/E ratio have a little explanatory power of

underpricing. Sohail and Raheman (2009) broaden their analysis in the context of

aftermarket performance comparison between the financial sector and non-financial

sector IPO firms consistent with the Sohail and Nasr (2007). The sample contains 25

IPO firms from financial and non-financial sectors each. The findings reveal that the

non-financial firms were slightly more underpriced than the financial firms and their

explanatory variables were also different from each other. They reported that the

financial and non-financial sector firms were on average underpriced by 34.52% and

36.8% respectively. They also documented the reasons of initial return and found that

50

the oversubscription, offer size, ex-ante uncertainty and the ex-post market

capitalization on the early trading days has more explanatory power.

Table 2. 3: IPOs Initial Excess Returns Studies From Pakistani Literature

Sr. No Source Sample Size Sample Period Underpricing (%)

1 Sohail & Nasr (2007) 50 2000 - 2006 35.66

2 Rizwan & Khan (2007) 35 2000 - 2006 36.48

3 Javid (2009) 50 2005 - 2006 32.45

4 Sohail & Raheman (2009) 50 2000 - 2006 35.66

5 Sohail & Raheman (2010) 73 2000 - 2009 42.17

6 Kayani & Amjad (2011) 59 2000 - 2010 39.87

7 Afza, Yousaf & Alam (2013) 55 2000 - 2011 28.03

8 Mumtaz & Ahmed (2014) 75 2000 - 2011 30.30

9 Usman. (2014) 55 2001 - 2012 29.14

10 Yar & Javid (2014) 59 2000 - 2012 51.57

11 Kafayat & Rafey (2014) 30 2006 - 2013 68.22

12 Waseemullah &Sohail (2015) 26 2002 - 2011 35.49

13 Mumtaz, Smith & Ahmed (2016) 80 2000 - 2013 22.08

14 Mumtaz & Ahmed (2016) 90 1995 - 2010 15.27

15 Javid & Malik (2016) 72 2000 - 2015 23.32

16 Mumtaz, Smith & Ahmed (2016) 57 2000 - 2010 31.96

17 Sohail, Bilal, Rukh & Fatima (2018) 26 2010 - 2015 3.52

Sources: Compiled by author from various Pakistani studies

Rizwan and Khan (2007) examined and compared the aftermarket

performance of public vs. private sector IPOs and their explanatory components. They

used the sample of 35 IPOs listed during the 2000-2006 period and the sample was

consisted of 7 public sector IPO firms and 28 private sector IPO firms. They used the

methodology as comparable with the Asussenegg (2000) to estimate the short-run

aftermarket performance and found that the public sector IPOs were more underpriced

as compared to the private sector IPOs. The results of their finding of the

underpricing of the overall sample, the privatized firms and the private firms were

36.48%, 74.33% and 26.66% respectively. They also find that the proportion of shares

offered in an IPO and offer size are positive and significant determinants of the level

of initial underpricing. They argue that the Government of Pakistan provides support

to privatization commitments, offer benefits to small investors and to promote capital

market activities through the selling of large and well-renowned state-owned

enterprises at lower prices.

Sohail and Raheman (2010) investigate the initial returns on the 1st trading

day, 5th trading day, 10th trading day, 15th trading day & 20th trading day under the

51

normal, boom & the recession state of economies. They took the sample of 73 IPOs

listed on KSE from 2000 to 2009 period. They used the market adjusted return model

to estimate the short-run performance and found that the initial returns on the first

trading day in the overall sample period, normal, boom & the recession state of the

economy were 42.17%, 36.75%, 55.19% & 32% respectively. They used wealth

relative model as a sensitivity analysis & a robustness measure to compare the results

with the market adjusted returns method. The results of initial underpricing by wealth

relative model are consistent with the market adjusted returns. They also perform the

sectoral analysis and found that the Chemical, Engineering & the Oil and Gas

marketing IPO firms underpriced more than 100% on the first trading day. They argue

that the investors can earn abnormal profits if they buy stocks in the IPO process and

sell them on the first trading day closing prices.

Kayani and Amjad (2011) investigate the association between the level of

underpricing, before IPO investor’s interest and post-IPO trading volume using the

sample of 59 IPOs listed during the 2000-2010 period on KSE and provides the

evidence of initial underpricing of 39%. The number of shares subscribed by the

investors over the number of shares offered in the IPO ratio and the daily trading

volume over the number of shares offered in the IPO ratio was used as a proxy for

pre-IPO investor’s interest and post-IPO investor’s interest respectively. Their results

reveal that the initial returns are positively linked with the higher subscription ratio

and higher trading volumes over the few early trading days. They also used the cross-

sectional regression analysis to determine the reasons of the initial underpricing and

found that the firm size, offer size, Proportion of shares offered in the IPO ratio and

the ex-ante uncertainty were major predictors of the initial underpricing, whereas their

findings were consistent with the preceding studies of underpricing.

Afza, Yousaf and Alam (2013) examined the impact of information

asymmetry and the corporate governance (i-e the ownership structure and the board

composition) on the level of underpricing using the sample of 55 IPO firms who went

public from 2000 to 2011 period. This study also investigated the impact of corporate

governance as a moderating factor in the relationship between the initial underpricing

and the information asymmetry. They found that the Pakistani IPOs were underpriced

of 28.03% and corporate governance determinants add value to control the magnitude

of underpricing. They also documented that the information asymmetry is positively

52

linked with the degree of initial underpricing. They argue that CEO duality and the

institutional investment have an effect on the degree of initial excess returns.

Mumtaz and Ahmed (2014) employs the methodology as comparable with the

Sohail & Raheman (2010) and Ljungqvist et al., (2006) to estimate the initial

underpricing of 75 IPO firms listed during 2000-2011 period and found that the IPOs

underpriced on the first trading day and the thirtieth trading day was 30.30% and

24.17% respectively. They also used Extreme Bound Analysis (EBA) as comparable

to Levine & Renelt (1992) to examine the sensitivity and the robustness analysis of

the independent factors of initial underpricing. They found that the oversubscription,

ex-post uncertainty, financial leverage and the IPO offer prices were the true

predictors of the IPO underpricing.

Yar and Javid (2014) enlighten the underpricing phenomenon through the

relationship of initial returns, aftermarket liquidity and the ownership structure using

the cross-sectional data of 59 newly listed firms on KSE during the 2000-2012

periods. They found that the IPOs underpricing on the first trading day was 57.57%

and employ the methodology of logit model initially formed by Amemiya (1981) to

estimate the determinants of underpricing. They also employ the methodology as

comparable with the Booth and Chua (1996) to diffuse the ownership effect on the

IPO underpricing. They investigated that the M/B ratio, oversubscription, and the total

assets were the significant determinants of initial adjusted returns. They also expand

their results and found that the underpricing and the total assets were the significant

factors of the ownership structure. They also investigated that the underpricing, firm

size, ex-post uncertainty and the oversubscription were the major significant

determinants of aftermarket liquidity.

2.3 The IPO Long-run performance

The long-run poor performance is another anomaly observed in the domain of

public equity aftermarket performance analysis. Aggarwal and Rivoli (1990) first time

uncover the confirmation of underperformance over the longer periods. They called

this underperformance stance is a fad. The findings advocate that if private equities

methodically overvalued in the early trading days, market participants who acquired

shares on first trading day closing prices and hold them for longer time horizons

underperform the market. Ritter (1991) proposed various implications under the

53

findings of this study that why long-run returns analysis is noteworthy in corporate

finance. First, from the market participants’ point of view, the presence of returns

behavior fashions may offer investment alternatives for active trading strategies to get

abnormal returns. Second, the aftermarket return patterns are not constant over the

numerous time horizons and raised the question on the informational efficiency of

IPO product market. Third, the large variation in the volume of IPOs has been

observed over time. Finally, the cost of external equity capital raised by undertaking

the initial public offerings not only depends on the transaction cost take place in IPO

privatization and left money on the table in terms of underpricing. Many other studies

are motivated by Ritter’s study and has been attempting to assess the long-run

performance in many developed and emerging markets. The proposed hypotheses and

an extant literature have conversed below.

2.3.1 Fad Hypothesis

Aggarwal and Rivoli (1990) initially proposed this hypothesis based on the

evidence of long-run underperformance. They unable to defined the implications of

this phenomenon called as a fad in the primary market. Ritter (1991) investigates the

IPO valuation and the aftermarket long-run performance using the sample of 1,526

US newly listed firms during 1975-1984. They evaluate their three years aftermarket

performance and then compare the performance of firms from the similar industry and

market capitalization. This study finds that mostly firms went public when share

prices of comparable firms were at peak. He also observes negative returns as

compared to investing in comparable firms from the similar group based on the

industry and the capitalization listed in the US market. Therefore, it can be concluded

that the IPO is a profitable opportunity only if invest on the floatation prices and

release on the early days of listing. He suggests three different implications for long-

run underperformance, namely; the risk mis-measurement, the bad-luck and fads.

Though, the pragmatic finding does not hold the risk-measurement and the bad-luck

explanations. He presents the evidence of hot issues as firms decide to go public when

market sentiments are bullish and investors willing to pay higher prices. The IPO

share prices adjust their equilibrium as more information turns out to be available

publicly.

54

Loughran and Ritter (1995) motivated and broaden the Ritter’s study and

argue that firms went public when they observe firms from similar industry trading at

a high P/E and P/BV multiples. This results in the positive bias on investor initial IPO

valuations about upcoming offerings. The investors took valuation on the basis of

rapid growth in the trailing earnings which often disappear after the offering. They

advocate that the rational investors cannot beat the other investors’ over-valuations

about new offerings. They suggest that when underwriters leave the control of IPO

share prices to market forces then the share prices adjust their origin and this results in

underperformance of IPOs.

Lowery (2003) concludes that long-run underperformance of hot market IPOs

is sensitive to test specification by using the IPOs data of US floated firms during

1973-1996. She estimates the abnormal returns of IPOs as comparable with matched

size and BTM portfolio benchmarks. She observed that the negative association

between the IPO activity and long-run performance is strongest when using raw

returns, and weakest when using matched-size and BTM portfolio benchmarks. She

finds that the issuer firms more likely to go public when comparable valuation

multiples are high.

In sum, the long-run underperformance entails that the total cost of external

equity capital raised through public offerings is not excessive for private equity

issuers.10 The more established firms face a larger cost of external equity capital than

the smaller firms who want to expand their operations considered to be a risky

investment.

2.3.2 Heterogeneous expectations hypothesis

This hypothesis was initially proposed by (Miller, 1977). He theoretically

enlightens the unwind supposition of homogeneous expectations of investors;

therefore, a divergence of opinion arises among the market participants in the IPO

market. This study also argues that the short selling is restricted in the IPO shares;

hence the prices are defined by the optimistic investors. In the long run period, IPO

shares shifted in the normal regime of market regulations and more information

10 The major part of the transaction cost of raising equity capital is offset by the realized long run underperformance, for those firms who decide to go public when share prices of the comparable firms are at peak and the investors are optimistic about the future prospects.

55

become available then IPO share prices adjust their equilibrium. Therefore, this study

proposed the conjecture that the initial overvaluation is translated due to divergence of

opinion among initial investors and as a result long-run underperformance.

Houge et al., (2001) investigate the relationship between the degree of

variation of opinion among investors and the long-run performance of 2,025 newly

listed firms in the US from 1993 to 1996. They employ the time of the first trade, the

%opening bid-ask spread, and the flipping ratio as a proxy for the divergence of

opinion among investors perceived as the uncertainty bear by the number of IPO

market makers and tend to lead to opinion divergence. They find that the larger bid-

ask spread, long delay of first trade and the larger flipping activity impact the long-

run poor performance over three years. They conclude that the extent of uncertainty

faced by the IPO market makers, significantly influence the long-run poor

performance.

2.3.3 Agency Hypothesis

Carter et al., (1998) used several proxies to investigate the underwriter

reputation using the sample of US IPO firms listed during 1979-1991. They employ

the regression model to explain the explanatory power of underwriter reputation by

taking initial returns as a dependent variable and the long-run performance as a

dependent variable in the second model. They extract underwriter reputation measures

from (Johnson and Miller, 1988; Megginson and Weiss, 1991; Carter and Manaster,

1990) in both models individually as well as all together. The results of the initial

return model, each underwriter reputation measure are significantly linked with the

initial returns. On the other side, only Carter et al., reputation measure stay significant

when estimated at the same time. The results of long-run returns analysis, produce

relatively less poor that bring by prestigious underwriters. They conclude that the

long-run underperformance is directly linked to the extent of underwriter reputation.

Logue et al., (2002) find that despite the impact of underwriter reputation, the

underwriter activities in the process of IPO are significantly bonded with the IPO

long-run returns.

Brav and Gompers (1997) also investigate the role of agent regarding the long-

run underperformance of 934 venture-backed IPOs and 3,407 non-venture-backed

IPOs listed from 1972 to 1992. They document that the venture-back IPO firms

56

outperform the market while non-venture-backed IPOs returns remain constant over

time in the long run. They extend their analysis by undertaking numerous comparable

firms’ benchmarks, and the Fama and French 3 factor model. They point out that the

venture-backed IPOs do not negatively perform in the long run while non-venture-

backed smaller IPOs do underperform. On the other side, the negative performance in

long-run is not due to as observed that the comparable non-IPO firms from the same

size and the book value-to-market price ratio also underperform as the IPO firms.

In sum, the venture-capitalists and lead underwriters play an important role in

the IPO valuation oversight by initial investors and also affect the IPO valuation in the

long run.

2.3.4 Signaling Hypothesis

As discussed in the earlier sections, the signaling hypothesis exhibits that the

underwriters and the issuers produce some signals to the investors before the formal

listing in the market to reveal the fair value of IPO shares. Even it is inevitable to

define the underpricing anomaly during the initial trading days, there are some

implications for long-run performance as well.

As the signaling hypothesis suppose that the initial public offerings are

followed by seasoned equity offerings. Jegadeesh et al., (1993) demonstrates several

implications of the long-run performance of newly listed firms. First, they argue that

the firms raising additional equity capital after the fund raised in the IPOs are high

value; therefore these firms outperform the market over long period. Second, firms

having abnormal returns in initial trading days outperform the market in the long run

as well. Third, the quality IPOs hold a higher proportion of shares in early days and

leave a more money for the initial investors in the market to perform better in the long

run. Welch (1989) investigates the relationship between the initial returns and the

long-run performance. He finds that the firms offer abnormal returns in the early

trading days outperform the market of non-issuing firms. Ritter (1991), Ljunqvist

(1996) and, Jain and Kini (1994) find that the firms who produce abnormal returns in

the initial days do not demonstrate superior post-listing returns, in the long run,

relative to those that do not. Koh et al., (1996) investigate the relationship between the

shares retained by old shareholders and the aftermarket long-term returns using the

data of newly listed firms in Singapore. They find that firms having a major portion of

57

shares retained by old shareholders outperform the market in the long run. These

results are inconsistent with the findings of Ljungqvist (1996).

2.3.5 An International Empirical Evidence

Ritter (1991) used a sample of 1,526 US IPO firms listed during 1975-1984

and found a significant underperformance in the long run. This study used CAR by

adjusting the size portfolio as the benchmark and found the underperformance of (-

10.20%) and (-29.10%) for one- and three-years respectively. In addition, he swapped

the benchmark with NYSE index and found the presence of muscular

underperformance using the same sample. He argued that the small and medium

enterprises experience more underperformance than the larger enterprises. Loughran

(1993) used the data of US IPO firms and the Nasdaq index used as a benchmark to

estimate the long-run abnormal returns. He found a significant underperformance of (-

60.00%). Levis (1993) first time investigate the long-run performance in the UK using

a sample of 712 IPOs listed from 1980 to 1988. He points out the value of size-effect

and presents the long-run adjusted returns on the basis of Hoare Govett Smaller

Companies (HGSC) index, Financial Times Actuaries (FTA) index and All Shares

Equally-weighted Index as benchmarks. He finds the post-IPO underperformance

over three years of between (-8.0%) and (-23.0%) depending on the benchmark used.

Loughran and Ritter (1995) examined the long-run underperformance using

the sample of 4,753 US IPOs listed during the 1970-1990 period. They find that the

underperformance was observed (-26.90%) over three years while the

underperformance has increased to (-50.00%) when estimated for five years. They

point out that the long-run performance was worse during the high IPO activity

periods as compared with the low IPO activity periods. Hwang and Jayaraman (1995)

estimate the long-run IPOs performance of 182 Japanese newly listed firms and

conclude that the equal-weighted CAR is significantly negative (-14.98%), however,

at the same time value-weighted CAR is significantly positive (16.44%).

Barber and Lyon (1997) examine the long-run performance using the CAR

and BHAR methodologies of same IPOs data. They point out that the results of long-

run performance are not in line with both techniques. They preferred the BHAR

approach by justifying the cause that CAR method does not watch the investment

strategy of market participants if the securities held for longer time periods. On the

58

other hand, they also criticized the BHAR method due to skewness problem when

compounded abnormal returns are estimated on the monthly basis. To cope the issue

of skewness, Barber, Lyon and Tsai (1999) suggested the skewness adjusted model to

estimate the long-run abnormal returns.

Espenlaub et al., (2000) re-investigate the longer period returns of the UK

newly listed firms during 1985-1995 and they compare the long-run performance on

the basis of several methods: the CAPM, Fama-French three-factor, size control

portfolio, Ibbotson Returns Across Securities and Times (RTA) and the value-

weighted multi-index using the HGSC index. They found the long-run

underperformance over five years for CAPM, Fama and French three factors, size

control portfolio and the RATS while insignificant abnormal returns were found when

using the HGSC index as a benchmark.

Ritter and Welch (2002) estimate the long-run performance using the two

different alternatives: market and matched firms benchmarks (market to book ratio

and market capitalization). They estimate the long-run underperformance by -23.40%

when the market index was used as a benchmark while matching firms’ benchmark

produces underperformance -5.10%.

In the study of Gompers and Lerner (2003), measure the long-run performance

over five years of 3,661 US IPOs floating during 1935-1972 and compare the long-

run performance based on the different methods: BHAR, CAR, CAPM and Fama-

French three factor model. They found the long-run underperformance when BHAR

on the value-weighted basis was applied. They also observed that the IPOs

outperformed over five years when BHAR and CAR on the equally-weighted basis

were applied. Furthermore, no underperformance was examined by the CAPM and

the Fama-French methodologies.

Kooli and Suret (2004) also confirmed the long-run underperformance in the

Canadian IPO product market. They investigate the long-run abnormal returns over

three to five years by selecting the sample of 445 IPO firms listed from 1991 to 1998

and non-issuing matched firms used as a benchmark. They documented that the non-

issuing matched firms adjusted performance of -19.96% and -26.5% over the period

of three- and five-year respectively. Eckbo and Norli (2005) used Fama-French model

with modification by selecting a rolling portfolio strategy and found variations in the

59

long-run underperformance. The Jensen’s alpha explored to be insignificant to dictate

the long-run underperformance by adjusting the risk factors of value, size and market.

In the Malaysia, Ahmad-Zaluki (2007) select the sample of 454 newly listed

firms during 1990-2000 on the main board and the second board. They investigate the

long-run abnormal returns using the BHAR, CAR and the Fama-French three factor

model. They observed that the IPOs outperform the market under the approach of

BHAR and CAR. Though, the Fama-French three factor model produces negative

abnormal returns.

In the UK, Gregory et al., (2009) discussed the problems with various

benchmarks to examine the long run abnormal performance by choosing the large

sample of 2,499 UK IPOs floating during the 1975-2004 period. They found the

underperformance of -12.60% when a value-weighted portfolio is used as a

benchmark for the period of three years. They also observed that the

underperformance increased by -31.60% when an equally-weighted portfolio is used

as a benchmark for the period of five years.

Govindasamy (2010) documented the long-run underperformance using the

sample of 229 South Africa IPOs floating during 1995-2006 and used All Shares

Index of JSE as a benchmark. He found the long-run underperformance of -50.0%

after the period of 36 months. These results are in line with the other emerging market

literature. In India, Sahoo and Rajib (2010) document the long run abnormal returns

using the sample of 92 IPO firms listed during the 2004-2006 period on the Bombay

and National Stock Exchanges. Their results of long-run underperformance were not

in line with other Asian countries like the Sri Lanka, Bangladesh and the Pakistan.

Gopalaswamy et al., (2008) investigate the long-run performance using the data of

fixed price issues and the Bookbuilding issues. They conclude that the IPOs issued by

Bookbuilding process perform better after the one, two and three years than the IPOs

issued by Fixed price method.

Anton et al., (2011) examined the aftermarket pricing performance using the

data of Spanish IPOs listed during the period of 2000-2010 in the short and medium

terms. They point out that the Spanish IPOs outperform the market in the short-run

whereas the medium term performance was observed to be worse.

60

Table 2. 4: IPOs Long-run Performance Studies From International Literature

Authors Country Sample

Period

Sample

Size

Abnormal

Returns-%

Underperformance

for the period

Jewartowski and Lizinska (2012) Poland 1998-2008 142 -22.62 36 – Months

Komenkul et al., (2012) Thailand 2001-2012 136 -16.60 36 – Months

Belghitar and Dixon (2012) UK 1992-1996 335 -14.0 36 – Months

Bossin and Sentis (2014) France 1991-2005 207 -28.85 36 – Months

Islam et al., (2012) Bangladesh 1992-2006 163 -38.4 44 – Months

Thomadakis, Nounis and

Gounopoulos (2012)

Greece 1994-2002 254 -16.12 36 – Months

Brau (2012) US 1985-2003 3,547 -17.10 36 – Months

Su et al., (2011) China 1996-2005 936 8.6 36 – Months

Sahoo and Rajib (2010) India 2002-2006 92 41.91 36 – Months

Govindasamy (2010) South Africa 1995-2006 229 -50.00 36 – Months

Chi, Wang and Young (2010) China 1996-2002 897 9.69 36 – Months

Chi, McWha and Young (2010) New Zealand 1991-2005 114 -27.81 36 – Months

Chorruk and Worthington (2010) Thailand 1997-2008 141 -25.39 36 – Months

Gregory et al., (2009) UK 1975-2004 2499 -12.60 36 – Months

Goergen, Khurshed and

Mudambi (2007)

UK 1991-1995 240 -21.98 36 – Months

Zaluki et al., (2007) Malaysia 1990-2000 454 0.04 36 – Months

Campbell and Goodacre (2007) Malaysia 1990-2000 454 -2.01 36 – Months

Drobetz, Kammernmann and

Walchli (2005)

Switzerland 1983-2000 53 -173.46 120 – Months

Kooli and Suret (2004) Canada 1991-1998 445 -20.70 60 – Months

Gompers and Lerner (2003) US 1935-1972 3,661 -33.40 60 – Months

Ritter and Welch (2002) US 1980-2001 6,249 -23.40 36 – Months

Espenlaub, Gregory and Tonks

(2000)

UK 1985-1992 588 -21.30 60 – Months

Stehle et al., (2000) Germany 1960-1992 562 -9.01 36 – Months

Allen, Morkel-Kingsbury and

Piboonthanakiat (1999)

Thailand 1985-1992 143 10.02 36 – Months

Hwang and Jayaraman (1995) Japan 182 -14.98 36 – Months

Loughran and Ritter (1995) US 1970-1990 4,753 -50.0 60 – Months

Levis (1993) UK 1980-1988 712 -22.96 36 – Months

Ritter (1991) US 1975-1984 1,526 -29.10 36 – Months

Source: Compiled from various international articles

From the literature of China, Su et al., (2011) found the positive abnormal

returns by selecting the sample of 936 IPOs listed during the 1996-2005 period on the

Shenzhen and Shanghai Stock Exchanges using the matched firms as a benchmark.

The results reveal that the matched firms’ adjusted abnormal returns outperform the

market by 4.60% over the period of 24 months and these returns increases to 8.60%

over the period of 36 months. They concluded the misspecification of evaluation

61

methods with respect to the choice of benchmarks and confirmed the previous studies

of misspecification of the model.

Table 2. 5: IPOs Long-run performance Studies using the Event-Time Approach

Author(s) Country Period Sample

Size

Methodology Abnormal Returns over -%

1-Year 2-Year 3-Year 5-Year

Aggarwal, Leal and

Hernandez (1993)

Chile 1982-1990 36 MAR +1.1 -2 -23.7 --

Aggarwal, Leal and

Hernandez (1993)

Mexico 1987-1990 44 MAR -19.6 -- -- --

Aggarwal, Leal and

Hernandez (1993)

Brazil 1980-1990 62 MAR -9 -34.9** -47.0** --

Levis (1993) UK 1980-1988 712 CAR VW +1.6 -5.2 -11.4 --

CAR EW -7.2*** -17.3*** -23.0*** --

Lee, Taylor and Walter

(1996)

Australia 1976-1989 266 CAR -13.5 -31.0*** -51.3*** -30.9***

Allen, Morkel-Kingsbury

and Piboonthanakiat

(1999)

Thailand 1985-1992 150 CAR EW -5.4 -10.5 +10.0 --

CAR VW +1.4 +2.2 +27.5 --

Jakobsen and Sorensen

(2000)

Denmark 1984-1992 76 BHAR market -1.8 -23.5*** -30.4***

BHAR matching -6.6 -21.6*** -13.1*

Stehle, Ehrhardt and

Przyborowsky (2000)

Germany 1960-1992 187 BHAR VW +1.2 -7.0** -5.0

BHAR EW +2.4 -4.4 +1.5

Arosio, Giudici and

Paleari (2001)

Italy 1985-1999 150 BHAR -7.5 -12.5** -11.5

Lyn and Zychowicz

(2003)

Hungry 1991-1998 33 MAR -3.3 +1.2 -4.9

Lyn and Zychowicz

(2003)

Poland 1991-1998 108 MAR -4.1 +3.4 -24.4

Kooli and Suret (2004) Canada 1991-1998 445 CAR EW -10.8** -12.4 -16.9 -25.7

CAR VW -6.84** -8.7*** -9.4*** -19.2***

Alvarez and Gonzalez

(2005)

Spain 1987-1997 52 BHAR +6.1 -- -28.2*** -21.0**

Drobetz, Kammermann

and Walchli (2005)

Switzerland 1983-2000 109 BHAR -2.1 +0.9 -1.7 -26.2

CAR -8.5* -8.3 -7.5 -31.2***

Cheng and Shiu (2005) Taiwan 1988-2002 917 BHAR EW -3.4*

BHAR VW -10.0***

CAR EW -9.4*

CAR VW -22.7***

Bildik and Yilmaz (2007) Turkey 1990-2000 244 CAR EW +2.3 -13.0 -84.5

CAR VW +0.7 -4.0 -24.0

Thomadakis, Nounis and

Gounopoulos (2007)

Greece 1994-2002 254 MAR 15.7*** -8.1** -31.4**

Ahmad-Zaluki, Campbell

and Goodacre (2007)

Malaysia 1990-2000 454 CAR small EW +5.4 +2.5 +0.4

CAR small VW +0.04 -5.8 -8.2

BHAR mkt EW 11.6*** +21.1*** +17.9***

BHAR mkt VW +1.7 +3.9 -14.2***

***Significant at the 1% level, **Significant at the 5% level and *Significant at the 10% level

Source: Compiled from Choi, Lee and Megginson (2010)

In the developed market of France, Bossin and Sentis (2014) noted the long-

run underperformance by selecting the sample of 207 France IPO firms listed from

1991 to 2005 using the size and book to market as benchmarks. They documented that

62

the long-run adjusted performance of -28.85% and -68.10% under the benchmarks of

size and book to market respectively.

In Thailand, Komenkul et al., (2012) selected 136 Thailand IPOs listed from

2000 to 2012 and support the presence of long-run underperformance using the event-

study methodologies. They found the underperformance of -16.60% and -19.60%

using the BHAR and CAR respectively. In the study of Bangladesh, Islam et al.,

(2012) used a sample of 163 IPOs listed from 1992-2006 and found the long-run

underperformance of 38.40% at the end of the 44th month as compared with the

industry benchmark indices. In addition, they used size as a benchmark and found the

severe long-run underperformance in the smaller issues than the larger issues.

Bossin and Sentis (2014) examined the long-run performance of French IPOs

floated during 1991-2005. They categorized the sample into two groups as (1) orphan

IPOs (without carrying recommendations from the financial analysts) and, (2) non-

orphan IPOs (carrying recommendations from the financial analysts). They used both

the event-study and calendar-time approaches to estimate the long-run performance of

both clusters. They observed poor performance by the both types of IPOs in the long

run relative to the market portfolio during the sample period. They argue that the

financial analysts’ recommendations are significant for the initial year 1, however, for

the year 3 to the year 5, analysts’ recommendation unable to drive the long-run

performance of IPOs.

This draft finds only one study, which used Fama-French 5-factor model (the

most recent multifactor asset pricing model proposed by Fama & French (2015)) to

estimate the long-run performance of IPOs. In Sri Lanka, Ediriwickrama and Azeez

(2016) investigate the IPOs long-run underperformance anomaly using the numerous

asset pricing models (known as calendar time approaches). They employ Sharpe-

Lintner CAPM, Zero Beta CAPM, Fama-French 3-factor model, Carhart 4-factor

model and Fama-French 5-factor model to investigate the long-run performance of 51

IPOs floated in the Colombo Stock Exchange during 2000-2012. Based on the

constant coefficient (Jensen’s Alpha), they found that the IPO portfolios

underperform in the long run as compare to market benchmarks using the value- and

equally-weighted methods. They argue that the value-weighted models are more

63

appropriate to jointly explain the variation in the IPO long-run performance than the

equally-weighted models.

2.3.6 Difficulties with Long-term Returns Measurement

An accurate measure of long-term returns and the variation in the aftermarket

performance for longer time horizons may be attributable to several reasons. The

initial literature on long-run performance demonstrates that there are two methods to

estimate the long-run performance; the Cumulative Abnormal Returns (CAR) and the

Buy-and-Hold Abnormal Returns (BHAR).

The first issue with long-run performance measurement is the biasness. Barber

and Lyon (1996) investigate the accuracy of both CAR and BHAR methods to

measure long-term returns. They find that the CAR methodology undergoes from the

measurement bias because CAR is a biased predictor for BHAR. As a result, they

support the use of the BHAR to assess long-term abnormal returns. Fama (1998)

findings varied to Barber and Lyon (1996) and favored the cumulative abnormal

return model to estimate the long-run performance of IPOs. They point out that the

CAR is easy to scrutinize the linearity behavior of averages in the longer time

horizons. He preferred the CAR model which is a good estimator for multi-periods as

the CAR averages amplify linearly and the standard-error amplify with the square

root. At the same time, he criticized the buy-hold abnormal returns model is not in

shape to estimate multi-period returns because BHAR grows exponentially instead

linearly that finally raise the measurement problem.

The second issue with the long-run performance measurement is the selection

of market benchmarks. An Extant literature presents three different benchmarks to

estimate the long-run performance; the market indices, comparable firms from the

similar industry and the Fama-French three factor model. Barber and Lyon (1997)

identify few biases with using of market indices as the benchmark are; rebalancing

bias, skewness bias and newly listed firms’ bias. They suggest that the portfolio of

comparable firms selected on the basis of firm-specific characteristics is a good

benchmark. Whereas, Kothari and Warner (1997) disagree with the use of the

portfolio of comparable firms as a benchmark referred to as pre-event survivorship

bias. Ritter and Welch (2002) examine the accuracy of long-run performance using

two benchmarks; market and matched firms. They find that the market adjusted long-

64

run returns after three years was assessed to be -23.40%, while long-run performance

using the matched firms was observed to be -5.1%. Gompers and Lerner (2003) used

Fama-French three-factor model as a benchmark to estimate the long-run performance

and their results support the evidence of long-run underperformance. Eckbo and Norli

(2005) using the Fama-French three-factor model and Jenson’s alpha to estimate the

long-term performance. They point out that the results are not constant for longer time

horizons when they used Fama-French model with the modification of rolling

portfolio strategy. This study also finds that the Jenson’s alpha was insignificant to

demonstrate the long-run underperformance after adjusting the risk factors of value,

size and the market. Choi, Lee and Megginson (2010) also focused on the

methodology problem for long-run performance using the sample of 241 IPOs from

42 countries using the matched firms based on the size, book-to-market and the Fama-

French three-factor model benchmarks to assess the long-term returns. They find that

the IPOs under the Fama-French methodology is significant and outperform the

market in the several longer time horizons. However, the average returns of BHAR by

adjusting the size and the book-to-market multiples are not statistically significant and

outperform the market as well.

Lastly, few researchers also talk about the power of statistical tests used for

the long-term performance analysis. Loughran and Ritter (1995), and Brav (2000)

argue that the test statistics absence from the independence of observations as the

long-run returns of IPO firms may be correlated in the calendar time. Jenkinson and

Ljunqvist (2001) used the sample of internet IPOs listed during the bubble period and

verify this stance by showing the reduction in cross-sectional variance in the long-run

abnormal returns.

65

Table 2. 6: IPOs Long-run performance Studies using Calendar-Time Approach

Author(s) Country Period Methods

Espenlaub, Gregory, & Tonks

(2000)

UK 1985-1992 CAR & FF3F

Gompers and Lerner (2003 USA 1935-1972 CAR, BHAR, CAPM

& FF3F

De Silva Rosa, Velayuthen &

Walter (2003)

Australia 1991-1999 WR, BHAR & FF3F

Boabang (2005) Canada 1990-2000 CAR & FF3F

Ahmad-Zalukai, Campbell &

Goodacre (2007)

Malaysia 1990-2000 CAR, BHAR &

FF3F

Pukthuanthong-Le & Varaiya

(2007(

USA 1993-2002 BHAR &FF3F

Chi, Wang & Yong (2010) China 1996-2002 CAR, BHAR &

FF3F

Moshirian, Ng & Wu (2010) China, Malaysia, Japan,

Hongkong, Korea, Singapore

1991-2004 BHAR &FF3F

How, Ngo & Verhoeven (2011) Australia 1992-2004 CAR, BHAR &

FF3F

Brau, Couch & Sutton (2012) USA 1985-2003 BHAR &FF3F

Thomadakis, Nounis, &

Gounopoulos (2012)

Greece 1994-2002 CAPM, FF3F & C4F

Liu, Uchida & Gao (2012) China 2000-2007 WR, BHAR & FF3F

Boissin, & Sentis (2014) France 1991-2005 BHAR & FF3F

Mumtaz, Smith & Ahmed (2016) Pakistan 2000-2010 CAR, BHAR &FF3F

Ediriwickrama, & Azeez (2016) Sri Lanka 2000-2012 CAPM, Cohart4F,

FF3F & FF5F

Ediriwickrama, & Azeez (2017) Sri Lanka 2003-2015 FF3F, proposed 4-

Factor model

Source: compiled from various international articles

2.3.7 Long-run IPOs Performance in Pakistan

An extant literature on Pakistani IPOs, only a few researchers has try to unfold

the long-run underperformance anomaly for three to five years after the initiative of

Sohail & Nasr (2007) and Rizwan & Khan (2007).

Table 2.7 presents the summary of long-run underperformance of Pakistani

IPOs gauged by various researchers. Sohail and Nasr (2007) evaluate the one-year

pricing performance of 50 newly listed firms during 2000-2006 and document the

market adjusted returns using the CARs and BHARs by -19.67% and -38.10%

respectively. Rizwan and Khan (2007) estimate the long-run performance of 35

privatization and the private-owned IPOs from 2000 to 2006 period. They find that

the market adjusted returns using the buy-and-hold returns for one and two years are -

11.26% and -23.68% respectively. They also document that the aftermarket two-year

66

performance of privatization and private-owned IPOs by 12.69% and -33.11%

respectively. They argue that the privatization IPOs outperform the market than the

private-owned IPOs because privatization firms mature enough to overcome the

market uncertainties and perform well in the long run.

Table 2. 7: IPOs Long-run Performance Studies From Pakistani Literature

Authors Sample

Period

Sample

Size Methodology

Abnormal Returns over %

Year1 Year2 Year3 Year5

Sohail & Nasr (2007) 2000-2006 50 BHAR -38.10 -- -- --

CAR -19.67 -- -- --

Rizwan & Khan

(2007)

2000-2006 35 BHAR -11.26 -23.68 -- --

Mumtaz & Smith

(2015) 1995-2008 35 BHAR -- -- -29.50 -69.70

Mumtaz & Ahmed

(2016)

1995-2010 90 CAR VW -24.60 -18.90 -23.40 --

BHAR VW -15.20 -7.40 -19.80 --

CAR EW -19.90 -18.60 -23.20 --

BHAR EW -19.00 -15.30 -24.20 --

FF3F Y3EW -0.068 Y3VW -0.105

Carhart4F Y3EW -0.034 Y3VW -0.118

Javid & Malik (2016) 2000-2015 72 BHAR 12.76 -18.00 -42.49 -65.54

Mumtaz, Smith &

Ahmed (2016)

2000-2010 57 CAR VW -22.80 -19.30 -22.50 --

BHAR VW -11.00 +7.70 7.70 --

CAR EW -27.40 -16.60 -17.90 --

BHAR EW -26.30 -22.90 -32.70 --

Source: compiled from various Pakistani articles

Mumtaz and Smith (2015) examine the long-run performance over three and

five years using the sample of 35 IPOs from 1995 to 2008. They find that the IPO

firms underperform over three and five years using the buy-and-hold abnormal returns

by -29.50% and -69.70% respectively. They exclude the first month returns to avoid

the potential bias from the price adjustment of initial excess returns and once more

estimate the longer period’s performance over three and five years using the BHAR,

IPOs underperform by 22.80% and 61.70% respectively. They argue that the results

are consistent with the existing literature and investment in Pakistani IPOs is not

valuable over longer period horizons.

Mumtaz and Ahmed (2016) estimate the long-run pricing performance using

the BHAR and CARs of sized-based matched firms and the equal-weighted matched

firms. They used the sample of 90 IPOs listed from 1995 to 2010 in the KSE and

support the presence of underperformance phenomenon. The higher percentage of

underperformance is recognized when compared with sized-based matched firms

approach. Javid and Malik (2016) also validate the long-run underperformance of

67

privatization and private-owned firms using the sample of 72 IPOs during 2000-2015.

Mumtaz, Smith and Ahmed (2016) investigate the aftermarket performance of newly

listed firms using the event-study and calendar-time approaches by adjusting the

sized-based matched firms benchmark over three years. They point out that the long-

run underperformance is greater when using the CARs, exemplify significantly

negative abnormal returns. In the Calendar-time approach, the constant coefficient

(Jensen’s Alpha) related to Fama-French 3-factor and Carhart 4-factor models appear

to be negative in the equally- and value-weighted portfolios respectively.

By having a look on Pakistani literature of long-run returns analysis, this study

supports the evidence of long run underperformance is observed in the PSX, the long

run underperformance from -18% to -69% is observed in three to five years using

BHAR (Javid & Malik, 2016; Mumtaz & Smith, 2015). The most Pakistani studies

used BHAR and CAR methodology to estimate long run abnormal return while only

Mumtaz & Ahmed (2016) first time used Fama-French 3-factor and Carhart’s 4-factor

models to validate the long-run underperformance. Only Mumtaz, Smith & Ahmed

(2016) and Javid & Malik (2016) estimate the determinants of long run performance.

They find that financial leverage, offer size, firm beta and percentage of shares

offered are the key driving forces of long run abnormal returns.

68

3. Chapter 3

3. Research Methodology

69

This chapter explains the sample description and research methodology employed to

investigate the choice and accuracy of the IPO valuation methods and the

determinants of aftermarket performance of IPOs listed in Pakistan. A number of

valuation models have been conversing about IPO performance in the extant

literature. The discussion about ex-ante valuation and ex-post performance includes

the theoretical foundation of valuation models, initial excess returns models, and the

long-run performance models. This chapter as a whole describes the variables used in

empirical models and related hypotheses on valuation and aftermarket performance.

Section 3.1 provides the details of sample selection criteria and description of the

sample. Section 3.2 & 3.3 provides details of theoretical basis of research

methodology employed to examine the choice, bias, and accuracy of each valuation

method for fair-value estimates disclosed in prospectus documents. Section 3.4

provides the comprehensive explanation of the valuation methods used in IPO and

non-IPO studies, and formation of multivariate regression models to determine the

factors that impact on offer price valuation. Section 3.5 & 3.6 provide the

methodology and related hypotheses to explore the effect of prospectus information

on the short-run underpricing returns and long-run performance respectively.

3.1 Data and Sample Description

This study starts with all newly listed firms on the Pakistan Stock Exchange

previously known as Karachi Stock Exchange during 2000-2016. A total of 126 firms

went public on PSX during the sample period. As similar to an extant literature, this

study excludes 38 firms from the population as: excludes 16 firms that went public

without publishing prospectus documents because these firms get listed due to Specie

Dividend announced by their parent firms to their existing shareholders, merger and

demerger events, excludes 16 firms listed as closed-end mutual funds because their

reporting environments are not comparable with the other sector IPO firms and six

prospectus documents are missing in the data. These sample selection criteria result as

a sample of 88 (70%) firms that went public. Table 3.1 presents the summary of IPOs

and sampling selection criteria stretches across the years during sample period and

indicates that most IPOs went public during 2004-2008. Table 3.2 presents the

summary of IPOs across the sectors in which they are operating. The findings

highlight that most IPOs offered by investment securities & banks, commercial banks

and power generation & distribution sectors.

70

Table 3. 1: Sample Selection Criteria and Description

Sr. No Year Total Listed

Firms

Restrictions Sample

IPO

Firms Without IPO

Listings

Mutual

Funds

Missing

IPOs

1 2000 3 0 0 0 3

2 2001 3 0 0 1 2

3 2002 4 0 0 0 4

4 2003 6 2 0 1 3

5 2004 17 1 6 2 8

6 2005 19 1 4 0 14

7 2006 10 3 4 1 2

8 2007 15 2 2 1 10

9 2008 10 1 0 0 9

10 2009 4 1 0 0 3

11 2010 6 0 0 0 6

12 2011 4 0 0 0 4

13 2012 4 1 0 0 3

14 2013 3 2 0 0 1

15 2014 6 1 0 0 5

16 2015 8 1 0 0 7

17 2016 4 0 0 0 4

Total Firms 126 16 16 6 88

Source: Equity Listing History section on PSX data portal

Figure 3. 1: Year-wise Number of IPOs in the Sample

0

2

4

6

8

10

12

14

Year-wise Listed IPOs on PSX

71

Table 3. 2: Sector-wise IPO firms in the sample

Sr. No. Sector Name Listed firms

1 Automobile & Electrical Goods 4

2 Cement 5

3 Chemicals 6

4 Commercial Banks 10

5 Engineering 7

6 Fertilizers 2

7 Food & Allied Products 2

8 Insurance & Leasing 1

9 Investment Securities & Banks 12

10 Modaraba 3

11 Oil & Gas 6

12 Power Generation & Distribution 7

13 Property & Investment 3

14 Technology & Communication 12

15 Textile 6

16 Transportation & Communication 2

Total Listed Firms 88

Source: Equity Listing History section on PSX data portal

Figure 3. 2: Sector-wise Number of IPOs in the Sample

0

2

4

6

8

10

12

Sector-wise Listed IPOs on PSX

72

Figure 3.1 presents the year-wise distribution of the number of IPOs in the

sample period which shows that most private firms went public during 2004 to 2008

because of high market valuations, high GDP growth, low inflation and large pace of

FDI during 2004 to 2008 (Source: Pakistan Economic Survey (2004-05, 2005-06,

2006-07, 2007-08) and annual reports of SBP, and (Sohail and Raheman, 2010)). It

also shows that the rate of new floatation went down during the Internet Bubble crisis

and US Subprime mortgage crisis periods because most world markets dented the

relative valuations for the new placements and PSX also confront similar issues

during crisis periods. Figure 3.2 presents the sector-wise distribution of the number of

IPOs in the sample period which shows the sector-wise development trend in the

country. Nearly all unlisted commercial banks went public during the sample period

due to tight SBP regulations put into action that functional banks must register

themselves in the capital market within the six months from their date of

incorporation. Many of the investment companies and banks got registered and raised

equity capital because of attractive market valuations, the high pace of foreign

portfolio investment in the PSX, increased market transparency due to capital market

reforms, succeed world best performing market on different time of periods and

historically peaked the bench market index.

This study used secondary data for the pre- and post-IPO valuation analysis.

The data used in the choice and accuracy of underwriter’s valuation approaches is

hand collected from the prospectus documents that were published at the time of

listing on the PSX. The IPO share price data is collected from the official websites of

PSX and business recorder. The unique feature of this study is the cash and stock

dividends adjusted share prices data that has been used to estimate the long-run

performance of IPOs using the event-time and calendar-time approaches. The data

used in the different asset pricing models have been extracted from the annual reports

of 225 non-IPO firms that remain listed during the sample period on the PSX.

3.2 Theoretical Background

IPO process usually takes a long time and the uptight process for a private

firm that wants to raise long-term capital through selling ordinary shares to the

general public. To get permission for going public, firms need to fulfill a list of

requirements by market regulators. Firms that want to list on the capital market need

73

to submit a comprehensive document after the due diligence is called a prospectus.

This document must include all relevant information such as the structure of share

capital, the purpose of IPO proceeds, future prospects, valuation methods used to

estimate fair value estimates, history of IPO firms, preceding financial statements

before the IPO and the profile of the management team and detail of associated

companies.

Loughran and Ritter (1995), Lowery (2003), and Colak and Gunay (2011)

argue that as more information becomes available in the long run, the relationship

between IPO pricing and ex-ante risk factors disappears slowly as discussed earlier.

Therefore, ex-ante risk factors are positively correlated with expected returns. This

study also checks the relationship between signaling factors and the IPO pricing as

well as aftermarket performance. As the practice, stock prices are determined by the

current fundamentals, future prospects and discount expected risk factors. Peterle and

Berk (2016) and Agathee et al., (2012a) inside investors know more information

about future prospects than outside investors. Inside investors disclose firm’s

prospects as e.g., signals to forthcoming investors. If outside investors incorporate

signals in their decision to participate in an IPO then it could reduce the mispricing

and IPO firms could get utmost proceeds than they expected to be.

3.3 The Choice, Bias and Accuracy of Valuation Methods

This section constructs the methodology based on the theoretical basis and empirical

evidence to explain the selection of several valuation methods, bias and accuracy of

each valuation technique reported in the prospectus documents.

3.3.1 Enlightening the choice of valuation methods

In this part, this study constructs an econometric methodology to enlighten the

choice for a particular valuation model employed by the lead underwriters in the

domain of new offerings. This study employs the binary logit regression model

comparable with Roosenboom (2012, 2007) and Deloof, Maeseneire and Inghelbrecht

(2009) to examine the cross-sectional determinants of the selection of the valuation

methods used by the lead underwriters. The binary logit model is used, due to binary

outcomes of each valuation method variable (dummy variable), to predict the

probabilities of binary outcome of a given predictor variables. This study estimates

the following models.

74

Model 1: Based on the valuation method selected

𝑉𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛_𝑀𝑒𝑡ℎ𝑜𝑑

= 𝛽0 + 𝛽1𝐿𝑛𝑆𝑖𝑧𝑒𝑖 + 𝛽2𝐿𝑛(1 + 𝑎𝑔𝑒)𝑖 + 𝛽3𝐴𝐼𝑃𝑖 + 𝛽4𝑃𝑅𝑂𝐹𝑖

+ 𝛽5𝐺𝑅𝑂𝑊𝑖 + 𝛽6𝐷𝐼𝑉𝑖 + 𝛽7𝑇𝑒𝑐ℎ𝑖 + 𝛽8𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽9𝑆𝐷𝑖 + 𝛽10𝑈𝑅𝑒𝑝𝑖

+ 𝛽11𝐷𝑖𝑙𝑢𝑡𝑖𝑜𝑛𝐹𝑎𝑐𝑡𝑟𝑖 + 𝜀𝑖

Equation (1)

In the preceding econometric equation, the dependent variable

(Valuation_Method) is a dummy variable that equals one if underwriter choose

comparable multiples valuation method for IPOs valuation and zero otherwise. On the

same pattern, if the underwriter choose discounted cash flow method then the

dependent variable equals one and zero otherwise and so on for other methods. The

peer group multiples valuation method is used for comparative valuation while DCF

and DDM valuation models are direct valuation techniques.

The firm size is estimated by the natural logarithm of total assets as Log(Total

Assets) from latest financial year statements disclosed in the prospectus prior to IPO.

This study employed natural log of total assets to normalize the distribution of data

and to control the ‘scale effect’ issue. Beatty and Ritter (1986), and Ritter (1984)

argue that the larger IPO firms can be easily estimated as they are more stable in

terms of market share, revenue growth, payout history and forecasted cash flows. This

helps to employ direct valuation models such as DCF and DDM more probably. The

firm age is estimated using natural logarithm of one plus firm age i.e. Log(1+Age) is

considered as an ex-ante uncertainty. Ritter (1984) argues that the degree of

uncertainty inversely associated with the age of the firm. Kim and Ritter (1999) point

out that it is complicated to predict forecasted cash flows and payouts for young firms

without creating financial statements track record because many of their estimations

based on the expectations regarding future growth rates, which significantly differ

from in each case. Therefore, mature firms are probably to be priced using direct

valuation models. This study estimates asset tangibility (AIP) as the ratio of property,

plant and equipment to total assets of the latest preceding year disclosed in the

prospectus. Lev (2001) argues that the accounting numbers supposed to be a good

estimator of the firm value of the tangible assets than the intangible assets. Therefore,

the firms with high asset tangibility increased the use of accounting-based valuation

75

methods such as economic value added method. This study theorizes that the lead

underwriters probably employ comparable multiples to value IPOs when they predict

that the issuer firm is comparatively profitable (PROF) in the year of issue. If the lead

underwriters are predicting that the issuing firm expected to be marginally profitable

or documenting loss on the basis of a due diligence report, IPO valuation estimates

and management prospects about future cash flows, they prefer to use other valuation

methods than the multiples valuation because applying the P/E ratio in these situations

results negative or low multiples valuation.

H1: The lead underwriters are more likely to use direct valuation methods

such as dividend discount model and discounted cash flow method when

valuing large firms.

H2: The lead underwriters are more likely to use direct valuation methods

when valuing older firms.

H3: The lead underwriters are more likely to use accounting based valuation

methods when valuing firms having higher asset tangibility.

H4: The lead underwriters are more likely to use multiples valuation method

for those that comparatively profitable in the year of issue.

In this study, predicted growth in sales in the IPO year is used as a proxy for growth

opportunities. The rapidly growing firms face challenges of cash imbalances in the

short to medium term because the capital investments are more than the cash inflows.

Penmen (2001) argue that the discounted cash flow model treat capital investment as

a loss of value and free cash flow model unable to recognize the value that does not

engage cash flows. In addition, the rapidly growing firms more likely to keep earnings

as capital reserves than to offer cash dividends. These firms are valued by comparable

multiples because of persistent investment in growth opportunities. Therefore, the

study hypothesize that the rapidly growing firms more likely to be valued using

comparable multiples than the direct valuation methods. One of the most important

features of our data is that Pakistani IPO firms typically report their historical payouts

(DIV) and/or payout pattern of associated companies in the offering document. Firms

that have a track record to announce high dividend are perceived as quality firms.

Bhattacharya (1979) argues that the high-quality firms only announce dividends to

76

their shareholders to signal their quality. In his theoretical explanations, dividends

tend to be a non-recoverable expense and visible signal to stakeholders while low-

quality firms prefer to use internal financing instead of expensive outside financing. A

trend of consistent payout (DIV) in the preceding years compels underwriters to value

IPO firms using the dividend discount model. Damodaran (1994) points out that the

dividend discount model is the best measure for valuing stable and high dividend

paying firms. Therefore, it is hypothesized that the underwriters are more likely to use

the dividend discount model during setting the fair value estimate for IPO firms that

pay a large portion of their earnings as the dividends in the past.


when valuing rapidly growing firms.

H6: The lead underwriters are more likely to use dividend discount model for

those that pay a large portion of their earnings as dividends in the past.

Bartov et al., (2002) argue that technology-based companies are more difficult to

assess their fair value estimate because a major division of their fair value comes from

their growth opportunities. Therefore, it is anticipated that the technology firms are

probably to be assessed using the multiples valuation than the direct estimation

methods such as DCF and DDM models because these models do not incorporate the

value of growth options in the fair value estimates. A dummy variable is used to

control the impact of technology companies (Tech) that equals to one if the IPO firm

is high-tech firm and zero otherwise. Discounted cash flow estimates are sensitive to

forecasted cash flows and discount rates as estimated by underwriters to value IPOs.

DeAnglo (1990) points out that it is not very convincing to external shareholders due

to the sensitivity of the value estimates made by lead underwriters, investors are more

likely to go for those IPOs in which window of opportunity prevails. A persistent rise

in aggregate stock returns may point out the window of opportunity. For that purpose,

this study adds market returns (MktRet) during a six-month interval from 185 trading

days prior to IPO and 5 days before the formal listing of IPO firm. This study assumes

that the likelihood of employing the discounted cash flow method increases when the

aggregate stock market returns before going public are high. The assumption of the

DDM method contrasts with the DCF model because DDM is used when aggregate

stock market returns are poor. Baker and Wurgler (2004) proposed a theory that the

77

investors pay more attention to buy those stocks that pay dividends on a regular basis.

Therefore, it is anticipated that during the bearish momentum, investors are more

likely to buy dividend-paying stocks when aggregate stock market returns are

declining.


when valuing technology firms.

H8a: The lead underwriters are more likely to use the discounted cash flow

method when aggregate stock market returns before the IPO are high.

H8b: The lead underwriters are more likely to use the dividend discount model

when aggregate stock market returns before the IPO are low.

Roosenboom (2007) argues that the investors are uncertain about the fundamental

values of securities when aggregate market returns are highly volatile. In this study,

standard deviation (SD) of the benchmark index is included during a six-month

interval from 185 trading days prior to IPO and 5 days before the formal listing of

IPO firm. The investment bankers choose a direct valuation method to cater the

investors demand by controlling the impact of market volatility. Therefore, It is

anticipated that the direct valuation methods are to be used more often when market

returns are more volatile beforee IPOs. In this model, underwriter reputation is

included as a control variable. This study employs underwriter market share as a

proxy for underwriter reputation (URep). Following Roosenboom (2012) and

Ljungqvist & Wilhelm (2002), underwriter reputation is used as a dummy variable

and take a value of 1 for prestigious underwriters and 0 for less reputed underwriters.

Carter and Manaster (1990) suggest that the prestigious underwriters are considered to

be more experts in valuing IPO firms. The magnitude of underwriter reputation may

impact the choice of valuation method although this study is unable to find any

particular prior prediction regarding the valuation model by prestigious underwriters.

The rapidly growing firms offer a large portion of their shares in general public

offerings to finance their future expansions and investments while mature firms small

part in the public offering. Therefore, if the value of dilution factor (DilutionFactr) is

large then underwriters more likely to use comparable multiples and direct valuation

methods for small dilution factors.

78

H9a: The lead underwriters are more likely to use direct valuation methods

when aggregate stock market volatility is large before the IPO.

H9b: The lead underwriters are more likely to use multiples valuation methods

when aggregate stock market volatility is low before the IPO.

H10a: The lead underwriters are more likely to use direct valuation method

when valuing IPOs that offered a large proportion of the outstanding shares in

the IPO.

H10b: The lead underwriters are more likely to use multiples valuation

method when valuing IPOs that offered a small proportion of the outstanding

shares in the IPO.

3.3.2 Bias and Accuracy of Valuation Methods

Numerous studies deal with the valuation bias and accuracy of each method

used by underwriters during setting the IPO value estimates. Boatsman and Baskin

(1981) examine the peer group multiples valuation method and found that the

valuation process produces improved results when matched firms are selected on the

basis of same industry and trailing earnings growth. Alford (1992) investigates the

valuation accuracy of earnings per share (EPS) when firms are chosen based on

earnings growth, same industry, size and leverage. He shows that valuation errors

decline when matched firms choosen from one-digit SIC code ot two and three.

Kaplan and Ruback (1995) compare the valuation accuracy of peer group multiples

with the discounted cash flow model and show that the estimates determined on the

basis of comparable multiples underestimate the transaction value. Bakar and Ruback

(1999) investigate the valuation accuracy using the harmonic mean of multiples

chosen on the basis of sales, EBIT and EBITDA and reported that the performance

adjusted by EBITDA carry out better estimation than sales and EBIT. Bhojraj and Lee

(2002) put extra focus on the collection of matched firms used for valuation accuracy

and employ a linear regression model that predict the warranted multiples for each

focused firm. Liu et al., (2002) found that forecasted and trailing earnings offer the

best estimates followed by the book value of equity and multiples based on cash flow

measures. Liu et al., (2004) provide evidence under the findings of international

79

markets that multiples on the basis of price-earnings perform best for valuation

accuracy.

Several studies of valuation accuracy defined the prediction errors on the basis

of market value (Roosenboom, 2012, Deloof et al., 2009, Liu et al., 2002; Francis et

al., 2000). This study estimates the prediction errors based on the market values

instead of IPO offer values because the lead underwriters sometimes involve, on the

same time, valuing fair-value estimates and a decision process of IPO offer prices. For

instance, if the underwriters want higher offer prices, then they may choose

comparable firms with high multiples and if the lead underwriters want lower offer

prices, then they may select comparable firms with low multiples (Kim and Ritter,

1999). The key insight of this approach, the prediction errors should contain the

underwriters’ deliberate offer price discounts that they use to set preliminary offer

prices and as a marketing tool to create excess demand from investors in primary

auction. These prediction errors assist underwriters to decide which valuation method

is better to value IPOs and which is not.

This study follows the methodology to estimate bias and accuracy of valuation

methods as used in Kim and Ritter (1999), Francis et al., (2000), Deloof et al., (2009)

and Roosenboom (2012). In this study, first, this section estimate the signed

prediction errors (SPE) of each valuation method as

𝑆𝑖𝑔𝑛𝑒𝑑 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑒𝑟𝑟𝑜𝑟𝑠 = (𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑉𝑎𝑙𝑢𝑒𝑖,𝑝𝑟𝑒𝐼𝑃𝑂 − 𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒𝑖)

𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑒𝑖

Equation (2)

Where Estimated Valuei,preIPO is the value estimate determined using the lead

underwriter’s valuation methods when valuing IPO firm ‘i’ and Market Value is the

closing price of the first trading day in the stock market. In this study, both first

trading day closing prices and IPO offer prices as market values are used to estimate

the signed prediction errors to better understand the biases linked to each valuation

method. The signed prediction errors based on offer prices are important because, in

most of the cases, underwriters at same times are involved to estimate fair value

estimates and to set IPO offer prices. For instance, if the underwriters desire higher

offer prices, then they choose comparable firms having high multiples and if the

underwriters want to control the overpricing then they may choose comparable firms

80

with low multiples (Kim and Ritter, 1999). In this part, the Wilcoxon sign rank test

would apply to the median values of signed prediction errors to examine the bias of

each valuation method used by the lead underwriters during the evaluation process.

To compare and analyze the performance of several valuation methods used

by the lead underwriters in terms of valuation accuracy, this study inspects the

measures of dispersion for the pooled distribution of absolute prediction errors.

According to Schreiner & Spremann (2007), Cassia et al., (2004), Purnanandam &

Swaminathan (2004) and Kim & Ritter (1999) the important measures of valuation

accuracy are the mean absolute prediction errors (APE) and the percentage of signed

prediction errors below 15% of actual market values. By doing this, the findings turn

into comparable with related studies follow these measures to draw inferences.

Absolute prediction errors capture the valuation accuracy of each valuation method

and can be estimated as

𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝐸𝑟𝑟𝑜𝑟𝑠 = |(𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑉𝑎𝑙𝑢𝑒𝑖,𝑝𝑟𝑒𝐼𝑃𝑂 − 𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒𝑖)

𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒𝑖|

Equation (3)

The absolute prediction errors are the absolute values of the signed prediction

errors. The percentage of signed prediction valuations within 15% of the actual

valuation method is estimated as

[log(𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑉𝑎𝑙𝑢𝑒𝑖,𝑝𝑟𝑒𝐼𝑃𝑂) − log(𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒𝑖)] < 0.15

Equation (4)

In this part, the study examines OLS regression to test the valuation relevancy

and predicting the power of each valuation method by conducting a Wald-test to

probe whether the intercept term is statistically different from zero and the slope

coefficient statistically different from one. This methodology assists to examine the

ability of value relevancy to explain cross-sectional variation in the market values.

The valuation theories conjecture that the market value of IPO firms is directly

proportional to the pre-IPO value estimates examined by the investment banks. If the

valuation models give unbiased estimates of market values then the intercept would

equal zero and the slope coefficient equals one. According to Cassia, Paleari &

81

Vismara (2004) for the purpose of valuation relevancy, a more broad-spectrum

regression model may be used as:

𝐿𝑜𝑔(𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒𝑖) = 𝛽0 + 𝛽1𝐿𝑜𝑔( 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑉𝑎𝑙𝑢𝑒𝑖,𝑝𝑟𝑒𝐼𝑃𝑂) + 𝜀𝑖

Equation (5)

Where Estimated Valuei,preIPO is the value estimates determined by lead

underwriters to use valuation methods when valuing IPO firm ‘i’ and Market Value is

the closing price of the first trading day in the stock market, β0 and β1 are the intercept

and slope of a regression model respectively and εi is the pricing error. In equation

(5), the natural logarithm of Market Value is used as a dependent variable and the

natural logarithm of Estimated Value calculated through several valuation models

used as the independent variable as discussed in the earlier.

This study employed cross-sectional ordinary least square regression models

as comparable with Roosenboom (2012), the first time in the literature, to estimate the

association between these bias and accuracy estimates with the IPO firms’

characteristics as discussed in the earlier section.

Model 2: Based on the bias estimates

𝑆𝑃𝐸 = 𝛽0 + 𝛽1𝐿𝑛𝑆𝑖𝑧𝑒𝑖 + 𝛽2𝐿𝑛(1 + 𝑎𝑔𝑒)𝑖 + 𝛽3𝐴𝐼𝑃𝑖 + 𝛽4𝑃𝑅𝑂𝐹𝑖 + 𝛽5𝐺𝑅𝑂𝑊𝑖

+ 𝛽6𝐷𝐼𝑉𝑖 + 𝛽7𝑇𝑒𝑐ℎ𝑖 + 𝛽8𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽9𝑆𝐷𝑖 + 𝛽10𝑈𝑅𝑒𝑝𝑖


Equation (6)

Model 3: Based on the accuracy estimates

𝐴𝑃𝐸 = 𝛽0 + 𝛽1𝐿𝑛𝑆𝑖𝑧𝑒𝑖 + 𝛽2𝐿𝑛(1 + 𝑎𝑔𝑒)𝑖 + 𝛽3𝐴𝐼𝑃𝑖 + 𝛽4𝑃𝑅𝑂𝐹𝑖 + 𝛽5𝐺𝑅𝑂𝑊𝑖

+ 𝛽6𝐷𝐼𝑉𝑖 + 𝛽7𝑇𝑒𝑐ℎ𝑖 + 𝛽8𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽9𝑆𝐷𝑖 + 𝛽10𝑈𝑅𝑒𝑝𝑖


Equation (7)

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Table 3. 3: Operational definitions of variables used in Equation (1), (6) and (7)

Variable Definition

Dependent Variables

Valuation_Method Valuation_Method is a dummy variable that equals one if

underwriter uses peer group multiples valuation method and zero

other wise. Dummy variable equals one if underwriter uses DCF

model and zero other wise. Dummy variable equals one if

underwriter uses DDM model and zero other wise.

SPE The signed prediction errors is measured through the percentage

difference between the estimated value during pre-issue pricing

process and market value over first day market values for each

valuation method separately.

APE The absolute prediction errors are the absolute values of signed

prediction errors for each valuation method seperately.

Independent Variables

LnSize Natural logarithm of total assets for the latest financial year

disclosed in the prospectus.

Ln(1+Age) Natural logarithm of one plus firm age (difference between the IPO

listing year minus the date of incorporation)

AIP Ratio of property, plant and equipment to total assets for the latest

financial year disclosed in the prospectus

GROW Forecasted sales growth during the current year

Div Dummy variable equals one if the IPO firm has a track record of

payout history and/or disclosed dividend policy in the prospectus

prior to an IPO and zero otherwise

Tech Dummy variable equals one if the IPO firm belongs to a technology

industry and zero otherwise

MktRet The aggregate market returns during a 180 days interval from the

185th trading day before to 5th trading day before the formal listing

date of IPO firm. (KSE100 index has been used for market returns)

SD The standard deviation of daily market returns during a 180 days

interval from the 185th trading day before to 5th trading day before

the formal listing date of IPO firm.

URep A dummy variable for underwriter reputation, take the value of 1 for

prestigious underwriters and 0 for less reputed underwriters

Prof The ratio of current year forecasted EBIT to current year forecasted

sales

Dilution Factor The ratio of newly issued shares over Total post-issue outstanding

shares

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3.4 The Basic Valuation Model

An extant literature reveals that different researchers used different IPO

valuation methods. McCarthy (1999) argues that the IPO pricing process is all about

science and art. The scientific division starts when the investment banker and issuer

firm set IPO offer price based on historical information. The art division starts when

more market information such as peer multiples are incorporated to set IPO offer

price. From the academic point of view, Kim and Ritter (1999) estimate the

effectiveness of peer multiples methods. They found that comparable firms valuation

methods are worthy and the inclusion of market information increases the pricing

accuracy. Berkman et al., (2000) explore that peer multiples methods and Discount

Cash Flow (DCF) method have the same accuracy in the process of IPO pricing

decision.

Many researchers used accounting-based valuation models to value the IPO

(e.g., Rees, 1997; Fama and French, 1998; Easton and Sommer, 2003; Richardson and

Tinaikar 2004). Few researchers expand their models by adding more firm

characteristics as per their research objectives. According to Easton and Harris (1991)

basic valuation model, the price is a function of firms’ book value (BV), earnings (E)

and dividends (Div). Rees (1999) includes the dummy variable of negative earnings to

discriminate the effect of loss building firms on the IPO pricing. Rees (1997) uses

non-IPO firms’ data and find that dividends payout is a proxy for permanent income

which gives a positive signal to the firm value. The sample contains both large and

small firms with respect to assets and revenue. Many large firms have the track record

of dividends before the IPO and release the projected dividend in the prospectus,

while many small firms neither offer any payout before the IPO nor promise for any

payout in near future. Therefore, the firms having a track history of offering dividends

payout is added as signaling information in the initial valuation model to estimate the

firm value. Aggarwal et al., (2009) find that firms having higher negative earnings

before IPOs produce higher valuation and vice versa. The initial valuation model also

includes the dummy variable (D) to control the impact of negative earnings on

valuation. The underwriter in a new stock offering serves as the intermediary between

the company seeking to issue shares in an initial public offering (IPO) and investors.

The leading underwriter form a syndicate of investment bankers and provide

guarantees through underwriting agreement to issuer firm that investment banks

84

purchase entire stock if left in the IPO process. Thus, the basic valuation model is

given below.

𝑃0 = 𝛽0 𝐵𝑉𝑖 + 𝛽1𝐸𝑖 + 𝛽2𝐷𝑖𝑁𝐸 + 𝛽3𝐷𝑖𝑣𝑖 + 𝜀𝑖

Equation (8)

Where P0 is the IPO offer price, BV is the book value of shareholder’s equity,

E is earnings, D1NE is a dummy variable of negative earnings and Div is the

dividends disclosed in the prospectus.

It is already reported that the sample of IPO firms consists of large and small

firms that went public on the PSX. The market capitalization of each firm is a proxy

for firm size. A review of sample IPO firms, there is a scale difference which may

mislead the results interpreted. Rees (1999) and Brown et al., (1999) highlights the

existence of scale effects when they perform firm level analysis. Kothari and

Zimmerman (1996) argue that per share data can dilute the impact of

heteroscedasticity problem but does not an adequate control for the scale effects as

new offerings bring with variable sizes. Barth and Kallapur (1996) advocate two

procedures to control the scale effects; first, deflate through a scale proxy or addition

of scale proxy as a further explanatory variable. They argue that deflation by a

regression variable can mitigate the heteroscedasticity and coefficient bias, while the

addition of scale proxy as a separate independent variable can only mitigate

coefficient bias. So, this study uses deflation by a scale proxy followed earlier

literature.

It has been explored from existing literature that various researchers used four

different deflators in cross-sectional valuation studies as proxies for scale effect: First,

Hirschey (1985) used ‘sales’ as a proxy for scale effect. Second, Rees (1997) and

Beaver, Hand and Landsman (1999) used ‘total number of shares’ as a proxy for scale

effect. Third, Lo and Lys (2000) used ‘opening market price’ as a proxy for scale

effect. Finally, Core et al., (2003), Danbolt and Rees (2002) and, Easton (1998) used

‘book value’ as a proxy for scale effect.

This study already used per share data which robotically is deflated by the

total outstanding shares. Brown et al., (1999) argue that per share data is not an

appropriate measure to control scale effect as shares come in different sizes.

85

Therefore, per share data is not a suitable deflator for the valuation model. This study

analyses the IPO valuation on offer price and opening market value as a deflator

might mislead the results discussed. If market value considered as a deflator (P0/P0)

then the dependent variable produces a constant value one for all observations and it

will become the complicated model as WLS regression, as an advocate of Easton and

Sommer (2003). Barth and Kallapur (1996) suggest that book value is a suitable scale

factor than sales or opening market value for two reasons. First, book value is already

an independent variable and helps to mitigate coefficient bias. Second, when book

value is considered as a scale factor, it transforms the dependent variable to price-to-

book value (P/BV) ratio, which is a commonly used ratio despite price-to-sales ratio

or price-to-price ratio. From IPO literature, Kim and Ritter (1999) explore that

market-to-book value ratio increase the predictive power of valuation model, which is

comparable with peer competitor multiples. Keasey and McGuiness (1992) and, How

and Yeo (2000) used price to book value ratios as a deflator in their price valuation

models – information as disclosed in the prospectus. This study follows Kim and

Ritter (1999) and uses the book value as a scale factor.

𝑷𝟎

𝑩𝑽𝑖= 𝛽0

𝐵𝑉𝑖

𝐵𝑉𝑖+ 𝛽1

𝐸𝑖

𝐵𝑉𝑖+ 𝛽2𝐷𝑖 + 𝛽3

𝐷𝑖𝑣𝑖

𝐵𝑉𝑖+ 𝜀𝑖

Equation (9)

The definition of variables is same as discussed in the previous equation scaled by

book value per share.

3.4.1 The IPO Valuation Model

The main research objective of this study is “to investigate the impact of

fundamental factors, ex-ante risk factors and signaling factors on IPO initial

valuations and aftermarket performance”. As addressed earlier, various researchers

(Roosemboom, 2007, 2012; Xia et al., 2012; Deloof et al., 2007; Kim and Ritter,

1999; Ritter, 1984) explored that IPOs ex-ante risk factors impact on IPO pricing

process and their aftermarket successive performance. An extant literature on IPO

signaling studies, a number of signaling factors have been estimated and few

significant factors have been uncovered. This section only presents the empirical

model related to IPO pricing valuation and empirical models related to estimating the

relationship between the risk factors and the signal variables with subsequent

performance to be discussed in the next two sections. The ex-ante risk factors are

86

classified into two categories such as financial and non-financial risk factors. The

capital availability risk and financial leverage are used as financial risk factors. The

non-financial risk variables are the capacity risk, efficiency risk, IPO gross proceeds

and firm’s beta. Based on extant literature on signaling variables, this study identifies

three signal variables as; the underwriter reputation, proportion of shares offered and

the age of IPO firms.

The financial leverage (FinLev) is the degree to which a company uses

external capital (e.g., debt and preferred stock) to attain additional assets. The

excessive use of debt financing increases its financial leverage in terms of high-

interest payments, which negatively impact on firms’ core earnings. A company

should keep its optimal capital structure in mind when making the financing decision

to ensure any increases in debt financing increase the intrinsic value of a firm.

According to studies about IPO valuation and financial leverage (Modigliani and

Miller, 1966); higher the proportion of debt in the capital structure that increases the

systematic risk of a firm’s value and the probability of going bankrupt. Therefore, it is

established that higher leverage, increases the level of insolvency. Loughran and

McDonald (2013), Thomas (2011), and Roosenboom (2012) estimate association

between the financial leverage and the firm’s equity. They argue that pre-IPO higher

financial leverage as a proxy for ex-ante risk increases the deflation of equity

valuation. The listing rules of new offerings required that offering document should

publish financial statements at least three years before IPO. The pre-issue debt ratios

from latest financial statements is used as financial leverage.

The second ex-ante financial risk factor used in this research is capital

availability risk (CaptlRsk). Generally, firms used internal and external capital to

finance their resources. Internal capital includes paid-up capital against common

equity and retained earnings while external capital includes debt and preferred equity.

The excessive use of external capital increases their financial risk because of high-

interest payments while internal capital raises less risk. Therefore, a large portion of

retained income represents the higher capital availability and lower financial risk. The

capital availability is estimated through an average of retained earnings ratios from

the latest financial statements publish in the prospectus documents. Mathematically, it

can be written as; Retained Earnings Ratio = 1 – Payout Ratio

87

Furthermore, the valuation methodology also incorporates the four non-

financial risk factors. The efficiency risk (EffRsk) is the first non-financial risk factor,

which depicts the operating efficiency. The firms’ with high operating efficiency

means the less cost of goods sold and less efficiency risk. An extant literature on

operating efficiency, various researchers explored the positive relationship between

the operating efficiency and the financial performance of the IPO firms. Jain and Kini

(1994) investigate the operating performance of US newly listed firms during 1976 to

1988 after going public. Their findings show that aftermarket operating performance

is positively related to equity retention by initial shareholders. They argue that the

firms having large offer price discount perform worst after the IPO. Mikkelson et al.,

(1997) investigate the post-IPO operating performance and its determinants during

1980 to 1983. They find out that the operating performance decline after going public

and the variation in operating performance explained mostly by firm’s size and firm’s

age rather than the ownership structure. Kim, kitsabunnarat and Nofsinger examined

the Thai IPOs. They explore a significant positive relationship between the post-IPO

operating performance and the financial performance. They find a significant drop in

operating performance followed by subsequent time periods. They investigate that the

operating performance significantly explained by sales growth, capital expenditures

and the asset turnover ratio. The reason for adding efficiency risk factor in our

methodology is due to unavailability of complete information about IPO firms in the

prospectus. The efficiency risk factor is measured by an average of the ratio of cost of

goods sold (CGS) over firm turnover disclosed in the offering documents. Therefore,

there is an inverse relationship between the operating performance and the ratio of

CGS over firms’ turnover.

The second non-financial risk factor is the capacity risk (CpctyRsk), which is

added to the valuation model. The capacity risk is defined as, the probability of

success of the new venture over firms’ capacity. In this research, larger the portion of

IPO proceeds used to be investment activities tends to large uncertainty of returns

from the new venture. Most, IPO firms decide to go public to raise equity capital to

finance their operations and a particular investment project for business expansion.

Though, in many cases IPO proceeds are used to provide an exit strategy, to repay

debt or to redeem preferred equity. An extant literature on utilization of IPO proceeds

describes the impact of IPO proceeds on the underpricing or aftermarket performance

88

(e.g., Leone et al., 2003; Espenlaub et al., 1999; Klein 1996). This study investigates

the impact of the proposed utilization plan of IPO proceeds on the IPO pricing and

aftermarket performance. This research uses the ratio of the proposed utilization plan

of IPO proceeds over net IPO proceeds as a proxy for capacity risk. Higher the

proportion of utilization plan over the IPO proceeds indicates higher the capacity risk.

Beaver et al,. (1970) argues that firm beta is an important factor to evaluate the

riskiness of the firm’s value. The firm beta is defined as to estimate the sensitivity of

change in the firm share price due to the variation in the market index. Though, due to

unavailability of share prices before formal listing in the market, the standard

deviation of IPO aftermarket prices for 180 trading days from the date of formal

listing on the market. A firm having higher beta is perceived to be riskier. Therefore,

firm’s beta is negatively correlated with aftermarket performance. KSE100 index is

considered to estimate market return.

The IPO gross proceeds (OffrSize) is used as a non-financial risk factor in the

valuation model. The offer size is broadly used as a proxy for the level of risk of the

IPO firms. Perhaps, firms having small offer size are more speculative than the large

size offerings (Ritter, 1984; Beatty and Ritter, 1986; Loughran and Ritter, 1995).

Bessler and Thies (2007) and, Agarwal, Liu and Rhee (2008) find that offer size is a

significant determinant of long-term performance. Sohail and Nasr (2006), Loughran

and Ritter (2002), Carter et al, (1998) and Pinkle (1998) investigates the association

between offer size and firm valuation. They find the inverse association between

short-run and long-run performances. The offer size measured through as a product of

the number of shares offered in IPO with the offer price.

The underwriter reputation (UndRep) is a first signaling variable in the

valuation model. The reason for adding underwriter reputation into the valuation

model is based on the theoretical basis. Baron (1982) enlightens the important role in

defining the allocation of shares and their subscriptions. In the IPO process, an

underwriter is bound to make an underwritten agreement to sell all shares with IPO

issuing firm and responsible if shares left unsold. To avoid the under-subscription

risk, the prestigious underwriters only engage in quality IPOs. Therefore, the firms

having lower quality could not bear prestigious underwriters underwriting fees then

these transactions are sponsored by less prestigious underwriters. It is conjectured that

IPOs sponsored by prestigious underwriters offered at a higher price than the firms

89

sponsored by not-prestigious underwriters. Dominique et al., (2013) investigate the

reputation of underwriter and the auditors on the IPO pricing and aftermarket

performance. Underwriters deliberately offer discount in the offer price than the

intrinsic value to minimize the risk of under subscription of offered capital. Their

empirical results show that the firms choose prestigious underwriters for IPO process

leave less money on the table. Mudrik and Imam (2002) emphasize that firms prefer

to hire reputed underwriters due to the reduction in undervaluation of equity. Kim et

al (1995) also explored that there is a negative and significant relationship between

the underwriter reputation and the level of underpricing. This study hold a dummy

variable underwriter prestige and its value equals 1 if IPO is offered through high

reputed underwriter and 0 for the less reputed underwriter. An extant literature on

underwriter reputation estimation, two most common approaches are adopted to

calculate the underwriter reputation classifications such as (1) underwriters included

in the specific year’s ranking of underwriter list and, (2) the underwriters relative

market share in preceding IPOs (Carter and Manaster, 1990; Meggison and Weiss,

1991). The methodology employs to estimate the classification of underwriter

reputation is comparable with Keasay and McGuiness (1992). The ranking

categorization is on the basis of how many newly listed firms underwritten by various

underwriters in the preceding time periods.

The firm age, a signaling factor, is used in the valuation model as a signaling

variable to enlighten the different IPO puzzling facts. The firm age imitates the

maturity level and size gained in the product market, tend to reflect the stability of

business operations and the market share. Ritter (1999) argues that it is complicated to

forecast predicted cash flows and corporate payouts of young firms without having

their track record. Older firms supposed to be less risky because of more experience

and stability in the business operations. The firm age is a key characteristic of the IPO

firm which uses to explain the variation in the aftermarket returns. The firm age is

estimated as the difference between the date of incorporation and the date of formal

listing on the stock exchange.

Firstly, Leland and Pyle (1977) suggest that the percentage of shares retained

by initial sponsors at the time of dilution of ownership contains very useful

information for outside investors. Most stock exchanges, at the time of the IPO,

require a certain limit of minimum shares to be retained by the initial shareholders and

90

the associates. Leland and Pyle (1977) observe that the large shareholder’s decision

about the percentage of ownership retention show the extent of confidence about the

firm’s future prospects. On the other hand, larger the part of shares offered in the IPO,

loses the confidence of large shareholders on the firm’s prospects. Beatty and Ritter

(1986) conjectur that firms offered less offer size are more speculative than larger

offer IPOs. Thus, there is a negetive association between the proportion of shares

offered and underpricing level. Prior literature used a number of proxies to estimate

the association between the ownership retention and the aftermarket valuation. In this

study, the percentage of shares offered to the general public is used as the inverse

proxy for the retained ownership.

The empirical valuation model used based on the theoretical foundations to

examine the association between the fundamental factors, the ex-ante risk factors and

the signaling factors are put together in the following equation.

𝑷𝟎

𝑩𝑽𝒊= 𝛽0 + 𝛽1

𝐸𝑖

𝐵𝑉𝑖+ 𝛽2𝐷𝑖 + 𝛽3

𝐷𝑖𝑣𝑖

𝐵𝑉𝑖+ 𝛽

4𝐹𝑖𝑛𝐿𝑒𝑣𝑖 + 𝛽

5𝐶𝑎𝑝𝑡𝑙𝑅𝑠𝑘

𝑖

+ 𝛽6𝐸𝑓𝑓𝑅𝑠𝑘

𝑖+ 𝛽

7𝐶𝑝𝑐𝑡𝑦𝑅𝑠𝑘

𝑖+ 𝛽

8𝐹𝑟𝑚𝐵𝑒𝑡𝑎𝑖 + 𝛽

9𝑂𝑓𝑓𝑟𝑆𝑖𝑧𝑒

𝑖

+ 𝛽10

𝑈𝑛𝑑𝑅𝑒𝑝𝑖

+ 𝛽11

𝐹𝑟𝑚𝐴𝑔𝑒𝑖

+ 𝛽12

𝑃𝑂𝑆𝑖 + 𝜀𝑖

Equation (10)

Where FinLev is the ex-ante financial leverage before IPO and is measured

through the average of logarithm of the pre-IPO debt ratios, CaptlRsk is the capital

(e.g., internal capital) availability risk prior to IPO and measured through the average

of pre-IPO retained earnings to net income, EffRsk is the pre-IPO efficiency risk and

measured through the average of the ratio of pre-IPO cost of goods sold over net

sales, CpctyRsk is the capacity risk prior to IPO and measured through the ratio of

IPO proceeds utilization plan disclosed in the prospectus over total IPO proceeds,

FrmBeta is a firm beta as proxy of price volatility and measured as a standard

deviation of aftermarket IPO prices for 180 trading days, OffrSize is the IPO gross

proceeds and measured through the product of number of shares offered to general

public and offer prices, UndRep is the underwriter reputation prior to IPO and

measured through a dummy variable take the value of 1 for prestigious underwriter

and 0 for less reputed underwriter. In this study, the cut-off point as the underwriters

participate in the preceding IPOs is 6 to bifurcate the underwriter prestige, FrmAge is

the age of issuing firm at the time of IPO and measured through the difference

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between the date of incorporation and the formal date of listing on the stock

exchange, POS is the proportion of shares unsold by initial shareholders at the time of

IPO and measured as an inverse proxy for proportion of shares offered in the IPO.

Table 3. 4: Operational definitions of variables used in IPO valuation model

Variables Definition

P/BV The preliminary offer price scaled by book value (BV) of

shareholder’s equity per share

E Latest Earnings Per Share disclosed in the prospectus

D A dummy variable for negative earnings reported in the

prospectus document take the value of 1 for negative earnings

and 0 otherwise

UA A dummy variable for underwriting agreement in fixed price and

book building auctions take the value of 1 for IPOs offered

through book building and 0 for fixed price auction

Div Proposed dividend in the latest financial year disclosed in the

prospectus

Financial Leverage

(FinLev)

The ratio of Total Liabilities over Total Assets using the latest

year financial statements data disclosed in the prospectus is also

known as debt ratio.

Capital Availability Risk

(CaptlRsk)

The ratio of retained earnings over net income using the latest

financial statements data before IPO disclosed in the prospectus.

Efficiency Risk (EffRsk) The ratio of cost of goods sold (CGS) over net sales disclosed in

the latest financial statements before the IPO

Capacity Risk

(CpctyRsk)

The ratio of proposed investments plan disclosed in the

prospectus over the IPO gross proceeds

Firm Beta (FrmBeta) Firm beta is a proxy for price volatility, the standard deviation of

IPO aftermarket prices for 180 trading days since the date of

formal listing on the market.

Offer Size (OffrSize) Product of shares offered in the IPO and offer price

Underwriter Reputation

(UndRep)

A dummy variable for underwriter reputation, take the value of 1

for prestigious underwriters and 0 for less reputed underwriters.

Firm Age (FrmAge) Calculate by a natural log of one plus firm’s age: Log (1+Age)

Firm’s age measured through the difference between the date of

incorporation and the date of formal listing on the exchange.

Shares offered in IPO

(POS)

The proportion of shares offered in the IPO over post-IPO

outstanding shares

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3.4.2 Hypotheses about the Valuation Model

Based on the theoretical foundations and the valuation model, it has been

concluded that the price is an increasing function of the book value of shareholder’s

equity and its regular earnings. An existing literature expresses the negative impact of

pre-IPO negative earnings in the IPO valuation process. Hayn (1995) highlights that

loss in preceding years than profits history is less enlightening about firm’s future

prospects. McCarthy (1999) argues that the lead underwriters use accounting

information such as book value, dividends and earnings to set the preliminary IPO

offer prices. As already discussed in the literature review chapter, Klein (1996) and

Beatty et al., (2002) used trailing earnings disclosed in the prospectuses while Kim &

Ritter (1999) and How & Yeo (2001) used forecasted earnings to estimate the fair

value of new offerings and their findings conclude that the earnings have large

predicting power of initial prices. In addition, previous literature also finds the

significant role of the dividends on the stock valuation. The dividend policy also

illustrates the strength of firm’s future cash flows which significantly impact on the

share price variation. Hypotheses regarding the fundamental variables are mentioned

below.

H11a: The book value of shareholder’s equity is positively related to the

preliminary offer prices.

H11b: The latest’s financial year earnings per share is positively related to the


H11c: There is a positive relationship between negative earnings per share

and the preliminary offer prices.

H11d: The dividend policy disclosed in the prospectus is positively related to

the preliminary offer prices.

As discussed earlier, based on risk-aversion hypothesis, it is the supposition that firms

having high risk, lower the share prices. According to Loughran & McDonald (2013)

and Thomas (2011) pre-IPO higher financial leverage as a proxy for ex-ante risk

increases the deflation of equity valuation. Modiliani & Miller (1966) argue that

higher leverage increases the risk of insolvency. Fama and French (1998) argue that

93

the IPO firms follow the pecking order theory as they prefer to finance their

investments first with the internal resources such as retained earnings, then with the

external resources such as debt and issuing equity. Keasey and Short (1997)

investigate a positive relationship between the IPO market prices and the IPO

proceeds. The offer size is generally used as a proxy for the level of risk of the IPO

firms (Aggarwal, Liu and Rhee, 2008: Bessler and Thies, 2007). This implies that the

market price of any security fluctuates based on the available information and the

risky assets face lower demand. The hypotheses regarding ex-ante risk factors and the

offer prices are mentioned below.

H12a: The firm’s financial leverage before the IPO is negatively related to the


H12b: There is a positive relationship between the pre-IPO capital availability

risk and the preliminary offer prices.

H12c: There is a negative relationship between the pre-IPO firm’s efficiency


H12d: There is a positive relationship between the pre-IPO firm’s capacity


H12e: There is a negative relationship between the pre-IPO firm’s beta and


H12f: There is a negative relationship between the IPO offered size and the


Based on the existing studies regarding signaling factors, it is conjectured that the

signaling factors have a positive impact on the IPO valuations. Yung (2011) argues

that prestigious underwriters should have an advantage in information production

because of a large network with high net-worth institutions and/or individuals resulted

in greater price revision in the auction.The hypotheses about signaling factors and the

IPO valuations are discussed below.

H13a: There is a positive relationship between the underwriter reputation and


94

H13b: There is a positive relationship between the firm’s age and the


H13c: There is a negative relationship between the percentage of shares

offered in the IPO and the preliminary offer prices.

3.5 The Initial Excess Return Model

The rationale to construct valuation model is to address the issue of whether

the underwriters and market participants employ prospectus data to value IPO firm’s

equity. Furthermore, it also draws attention to the difference of opinions between the

market participants about the utility of prospectus information on the IPO pricing.

Therefore, this study investigates whether the difference of opinions has any

significant impact on the aftermarket performance. Various researchers argue that

initial excess returns on the first trading day is still an unsettled puzzling stylized fact.

The initial excess returns model attempts to scan whether the prospectus information

(fundamental factors, signaling factors and ex-ante risk factors) has any explanatory

power to resolve this puzzle.

Beatty and Ritter (1986) find that the level of underpricing is directly

proportional to the extent of pre-IPO uncertainty, at the same time Feltham et al.,

(1991) highlight that ex-ante risk attributes published in prospectus documents have a

significant explanatory power of IPO underpricing. Various researchers unfold the

role of pre-issue earnings on the initial valuations. Firth (1998) investigates the impact

of pre-IPO earnings on the aftermarket performance. He finds that the prior IPO

earnings have the significant explanatory power of post-IPO one-year cumulative

returns but on the other hand, it loses its significance in the post-IPO three years

returns. Easton and Harris (1991) used non-IPO data to investigate the association

between the earnings and the post-issue excess returns, and find a positive and

statistically significant association. This study offers a robust evidence of significance

of earnings in the IPO valuations. The initial excess returns model only adds the book

value and earnings as fundamental factors and exclude dummy of negative earnings

and the dividends reported in prospectus to keep the IER model simple.

Su (1999) finds the positive association between the pre-IPO financial

leverage and initial excess returns. He uses three proxies (e.g., debt to total assets,

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debt to equity, and debt to equity ratios) for financial leverage, but found only debt to

total assets ratio is significant with initial returns. As already discussed, the capital

availability is considered as an ex-ante risk determinent. A large amount of retained

earnings available to the IPO firms reveals lower risk and lower initial returns are

anticipated. Jain and Kini (1994) investigate the impact of aftermarket operating

performance on the aftermarket price performance. They argue that the operating

efficiency affects the aftermarket performance and find that less efficient firms

perform better in the post-IPO periods. Therefore, it is anticipated that the efficiency

risk has a positive association with aftermarket initial returns. Various researchers talk

about the relationship between the IPO gross proceeds and the extent of underpricing.

Some of them used IPO transaction proceeds as a size control variable. On the other

hand, this study only considered the IPO proceeds utilization plan as investments over

the net proceeds to indicate a level of risk in the future as published in prospectus

documents. The ratio of utilization plan for investment over net IPO proceeds shared

in the prospectus document is considered as the capacity risk. It is anticipated that

higher the portion of proposed utilization reveals lower the risk. It is anticipated that

there is a positive association between the capacity risk and the initial excess returns.

An extant literature examine the impact of industry beta as a proxy for firm beta on

the initial returns. Though in this study industry beta is used only for the IPO

valuation model as a proxy for firm beta due to unavailability of the market price of

newly listed firms and in the post-IPO period, firm beta is used as an explanatory

variable for underpricing. It is anticipated that the firm’s beta is positively related to

the initial excess returns.

Johnson and Miller (1988) examine the association between the level of

underpricing and underwriter prestige. They argue that IPO offered by reputed

underwriters be inclined less underpriced than the IPOs offered through less reputed

underwriters. Faltham et al., (1991) examine the impact of firm’s age and offer size

on the level of underpricingand find that firm’s age and offer size are negatively

associated with the degree of underpricing. Koh and Walter (1992) show the negative

association between the proportion of shares retained by initial shareholders and the

degree of underpricing. According to (Beatty et al., 2002), this study includes a

residual error term in the initial excess return model to examine the impact of other

factors. However, the underpricing take place as a result of various valuation methods

96

on IPO pricing and unobservable variables, the residual error term from the IPO

valuation model is incorporated added into the initial excess returns model. Therefore,

there is an inverse relationship between the valuation residuals and the initial excess

returns.

The IPO initial excess returns model is devised by considering the impact of

potential fundamentals, ex-ante risk factors, signaling factors and the residuals from

the IPO valuation model on initial returns as follows:

𝑰𝑬𝑹𝒊 = 𝛽0 + 𝛽1

𝐸𝑖

𝑃𝑜+ 𝛽2

𝐵𝑉𝑖

𝑃𝑜+ 𝛽



𝑖+ 𝛽

5𝐸𝑓𝑓𝑅𝑠𝑘

𝑖

+ 𝛽6𝐶𝑝𝑐𝑡𝑦𝑅𝑠𝑘

𝑖+ 𝛽



𝑖+ 𝛽

9𝑈𝑛𝑑𝑅𝑒𝑝

𝑖

+ 𝛽10


+ 𝛽11

𝑃𝑂𝑆𝑖 + 𝛽12

𝑅𝑒𝑠𝑖 + 𝜀𝑖

Equation (11)

Where IER is the initial excess returns on the first trading day closing price. This

study estimates initial excess returns by the market adjusted returns method and the

wealth relative method.

The mathematical equation of the market adjusted returns model can be expressed as,

𝐼𝐸𝑅 = [(1 + 𝑅𝑖,𝑡)

(1 + 𝑅𝑚,𝑡)− 1] ∗ 100

Equation (12)

Where Rit is the natural log of the first trading day closing price divided by the

IPO offer price for stock i at tth trading day and Rmt is the natural log of the first

trading day closing market index divided by the before IPO last day closing market

index for stock i at tth trading day.

The mathematical equation of wealth relative model can be expressed as,

𝑊𝑅 = [1 + (1𝑛⁄ ) ∑ 𝑅𝑖,𝑡

𝑛

𝑖=1

] / [1 + (1𝑛⁄ ) ∑ 𝑅𝑚,𝑡

𝑛

𝑚=1

]

Equation (13)

Where Rit is the return of stock i at the tth trading day and Rmt is the market

returns for stock i at the tth trading day, n is the number of IPO firms in the sample.

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The value of wealth relative model is above one reflects that the IPO firms

outperformed the market and less than one implies that the IPO firms underperformed

on the tth trading day.

The fundamental variables used in the IER model are book value and the

earnings, both are deflated by IPO preliminary offer prices. Now, these variables have

become the book value to market value ratio and the earnings to market price ratio.

The signaling and ex-ante risk factors are already defined in table 3.4 while Resit is

used as a proxy for unobservable variables of stock ‘i’ and estimated by the

standardized residual values from IPO valuation model.

3.5.1 Hypotheses about the Initial Excess Returns Model

Based on the theoretical foundations discussed regarding the initial returns, a

number of working hypotheses are proposed about variables used in the initial excess

returns model. The book value of shareholder’s equity of IPO offer price ratio is used

to identify, either stock is estimated as neither undervalued nor overvalued. Therefore,

it is anticipated that higher the book value to offer price ratio possibly increase the

underpricing. Beneda & Zhang (2009) conclude that the higher estimates of book

value disclosed boost investor’s confidence to invest more in initial trading periods.

Easton & Harris (1991) argue that earnings have a significant explanatory power in

aftermarket performance. They suggest that there is a positive association between

earnings forecast and short run returns.

H14a: There is a positive relationship between the book value of shareholder’s

equity over offer prices and the initial excess returns.

H14b: There is a positive relationship between the earnings before IPO over

offer prices and short-term returns.

An extant literature on IPO studies (Reber and Vencappa, 2016; mumtaz,smith &

ahmed, 2016; Banerjee, Dai & Shrestha, 2011; Lowry, officer & Schwert, 2010; afza,

Yousaf & alam, 2013; Miller and Reilly, 1987; Beatty and Ritter, 1986; Ritter, 1984),

advocate that the firms with greater ex-ante risk experience larger initial excess

returns. Mumtaz, Smith & Ahmed (2016) and Hedge & Miller (1996) find the inverse

correlation between the degree of financial leverage and the degree of underpricing.

Reber & Vencappa (2016), which investigate the positive and significant relationship

98

of the uses of IPO proceeds as investment related to initial excess returns. The offer

size is generally used as a proxy for the level of risk of the IPO firms (Aggarwal, Liu

and Rhee, 2008: Bessler and Thies, 2007). The firms offered large offer size in the

IPOs is riskier and in turn higher initial excess returns.

H15a: The average pre-IPO financial leverage is positively related to the IPO

initial excess returns.

H15b: The firm’s capital availability risk before the IPO is negatively

associated with the IPO initial excess returns.

H15c: The firm’s efficiency risk before the IPO is positively associated with

the IPO initial excess returns.

H15d: The firm’s capacity risk before the IPO is positively related to IPO


H15e: The firm’s beta before the IPO is positively related to IPO initial excess

returns.

H15f: The offer size in the IPO is negatively related to the IPO initial excess

returns.

Carter et al., (1998) examined the association between the investment banker

reputation and the level of underpricing. They conclude that the IPO offered by

reputed investment bankers tends to lower the underpriced. Johnson and Miller

(1988), and Carter and Manaster (1990) used different proxies for underwriter

reputations and document a negative association between the underwriter prestige and

initial excess returns. Lowry, officer & Schwert (2010), Kerins, Kutsuna & Smith

(2007), and Beatty & Ritter (1986) investigate that firm age is an important

determinant to explain the variation in the underpricing. They presumed that firm’s

age is a non-financial risk factor which may impact the aftermarket performance.

Older firms are understood to have more experience and market share tend to be less

risky than the younger firms. The portion of share capital retained by the initial

shareholders at the time of an IPO is a major signaling factor. Existing studies on IPO

suggest that the insiders keep large portion of shares capital in the IPO is a positive

99

signal to the other market participants and the portion of shares offered to general

public is used as an inverse proxy of shares retained by insiders.

H16a: The variable for underwriter reputation is negatively related to the IPO


H16b: There is an inverse relationship between the firm’s age and the IPO


H16c: There is a positive relationship between the percentage of shares

offered to the general public and the IPO initial excess returns.

The initial excess returns model also includes the residual term to unobservable

variables as measured by error terms of the IPO valuation model. A positive residual

point out that the IPOs were offered at higher prices relative to their fundamentals

than to the other IPOs. This implies that there is a negative association between the

residuals and initial excess returns.

H17: The residuals from the IPO valuation model is negatively related to the

IPO initial excess returns.

3.6 The Long-run Performance Model

The rationale to construct the long-run returns model is to determine the cross-

sectional determinants of long-run underperformance.. It is expected that the

investors, who buy shares at offer prices and keep them for three to five years, to be

earn small returns. Furthermore, Ritter (1991) first time reveals that the level of

underpricing is negatively linked to the long run performance of the US IPO firms.

This implies that the IPOs outperformed the market in the short run tend to have

smaller returns in the long-run. At the same time, the literature shows mixed results

about long-run underperformance. But signaling theories suggest that the firms with

good quality signals used underpricing as a signal for the quality product market. It

indicates that the high-quality firms with greater underpricing are expected to do

better in the future and breed larger returns in the long run.

The positive earnings in the preceding years disclosed in the prospectus are

typically observed as a signal of high-quality firms and add value in the evaluation

process. The firms with a strong earnings record assume to keep performing well in

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future. So, it can be conclude that the earnings disclosed in prospectus has a positive

on aftermarket performance in the long run. This study also examined the impact of

book value of shareholders’ equity on the post-issue performance. An extant literature

shows mixed finding on the association between book to market value ratio and the

aftermarket performance. Fama and French (1995) argue that the book value of

shareholder’s equity over market price is also used to compute the extent of risk and

their findings highlight that book to market value ratio is positively related to the

long-run performance. They conceive that the book value to market value ratio is

insignificant determinant to explain the aftermarket returns for 2 and 3 years.

This study based on the risk-aversion hypothesis, estimates the effect of ex-

ante risk factors on the long-run performance. The financial leverage factor is

commonly used to expose the financial risk to the IPO firm and it is anticipated that

higher the financial leverage depict the IPO is high risky which in results suppose the

superior long-run returns. Khurshed et al., (1999) the first time, investigates the

impact of pre-IPO financial leverage ratio on the aftermarket performance in the long

run and their results do not find the significant relationship between them. On the

same pattern, the impact of other risk factors (e.g., capacity risk, capital availability

risk, efficiency risk and industry risk) on aftermarket performance is positive.

As already discussed in the previous section, firm’s age is viewed as a proxy

for the strength of business operations. Therefore, it is anticipated that the older firms

positively related to the long run returns. Khurshed et al., (1999) examined the

association between firm’s age and the long run performance. They show the

insignificant relationship between firm’s age and the long run returns. Carter et al.,

(1998) examined the impact of underwriter reputation in the long run performance

and find a positive and significant relationship between them. They argue that when

more information becomes available in the market, IPOs offered by less reputed

underwriters perform negatively in the long run. Brav and Gompers (1997) investigate

the impact of underwriter reputation on long-run performance. They find that the

underwriter reputation has explanatory power for long-run performance. Therefore, it

is expected that the underwriter reputation is positively related to the IPO long-run

performance. Koh and Walter (1992) find the positive relationship between the

proportion of shares retained by initial shareholders and the IPO long-run returns.

101

Based on existing literature on signaling theory, the large portion retained by the

initial owners at the time of IPO indicated the quality of the firm and good prospects.

Therefore, it is anticipated that the percentage of shares retained by initial

shareholders is positively related to the IPO long-run performance. Many studies have

examined the association between the IPO initial excess returns (underpricing) and its

aftermarket performance in the long run. The signaling hypothesis suggests that the

high-quality firms offered large offer price discount which performs better in the long

run. Therefore, based on the signaling hypothesis, the underpricing is negatively

related to the aftermarket performance in the long run. Ritter (1991) and Levis (1993)

find the negative and significant relationship between the initial excess returns and the

long run returns. Welch (1989) does not find any relation between them. This study

reexamines the relationship to narrow down the divergence of opinion on this

proposed association.

The IPO long run return model is devised by considering the impact of fundamentals,

ex-ante risk factors and signaling factors on long run performance as follows:

𝑳𝑹𝑹𝒊 = 𝛽0 + 𝛽1

𝐸𝑖

𝑃𝑜+ 𝛽2

𝐵𝑉𝑖

𝑃𝑜+ 𝛽



𝑖+ 𝛽

5𝐸𝑓𝑓𝑅𝑠𝑘

𝑖

+ 𝛽6𝐶𝑝𝑐𝑡𝑦𝑅𝑠𝑘

𝑖+ 𝛽



𝑖+ 𝛽

9𝑈𝑛𝑑𝑅𝑒𝑝

𝑖

+ 𝛽10


+ 𝛽11

𝑃𝑂𝑆𝑖 + 𝛽12

𝑅𝑒𝑠𝑖 + 𝐼𝐸𝑅𝑖 + 𝜀𝑖

Equation (14)

Where LRR is the long run returns of IPO firms and measured through the: (i)

Buy and Hold abnormal returns (BHAR), (ii) Cumulative abnormal returns (CAR),

and (iii) Fama-French five factors model (FFFF) from day = 1 to x=1, 2, 3,4,5 years.

To estimate the long run IPO performance, this study employs the

methodology of buy and hold adjusted returns used by (Ritter, 1991; Loughran and

Ritter, 1995; Barber and Lyon, 1997) to estimate long-run returns. The mathematical

equation of buy and hold abnormal returns model can be expressed as,

𝐵𝐻𝐴𝑅𝑖,𝑇 = [∏(1 + 𝑅𝑖,𝑡)

𝑇

𝑡=1

− 1] − [∏(1 + 𝑅𝑚,𝑡)

𝑇

𝑡=1

− 1]

Equation (15)

102

Where BHARi,T is the buy and hold abnormal monthly returns of the firm i at t

time periods, where ∏ represents a product over T time periods, T time start from day

one to first, second and third anniversaries respectively, Rit is the monthly stock

returns of the firm i in t time period, Rmt is the market monthly returns measured

through the KSE100 index as a benchmark at t time period.

The average Buy and Holds abnormal returns for t period is defined as;

Equation (16)

Lyon et al., (1999) propose a skewness adjusted t-statistics to check the

significance of whether the mean BHAR is equal to zero, which can be estimated as

follows:

𝑡 = √𝑛 × (𝑆 +1

3ŷ𝑆2 +

1

6𝑛ŷ)

Where

𝑆 =𝐵𝐻𝐴𝑅𝑡

𝜎(𝐵𝐻𝐴𝑅𝑡) 𝑎𝑛𝑑 ŷ =

∑ (𝐵𝐻𝐴𝑅𝑖 − 𝐵𝐻𝐴𝑅)3𝑛𝑖=1

𝑛𝜎(𝐵𝐻𝐴𝑅𝑡)3

Where BHARt is the sample mean BHAR, 𝜎(𝐵𝐻𝐴𝑅𝑡) is the standard deviation of

cross-sectional sample buy and hold abnormal returns and n is the number of IPO

firms in the sample. Ŷ is an estimated value of skewness coefficient. This study

employs the skewness adjusted t-statistics to deal with the issue of skewness as the

critical values of common t-statistics are inappropriate in BHAR method.

This study employs the following methodology to estimate long-run monthly

returns are used by (Lyon, Barber & Tsai, 1999) to estimate cumulative abnormal

returns. The mathematical equation of the cumulative abnormal returns model can be

expressed as,

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝑅𝑚𝑡

Equation (17)

Where Rit is the monthly returns of stock i at t time event and Rmt is the monthly

returns of KSE100 index considered as a benchmark at t time event.

103

𝐴𝑅𝑡 =1

𝑛∑ 𝐴𝑅𝑖𝑡

𝑛

𝑖=1

Equation (18)

The cumulative abnormal return from event month q to event month s is the total of

the average of market adjusted returns.

𝐶𝐴𝑅𝑞,𝑠 = ∑ 𝐴𝑅𝑡

𝑠

𝑡=𝑞

Equation (19)

CAR is calculated from the closing price on the first trading day and the

cumulative mean adjusted returns for month 1 to 60. Since the cumulative adjusted

return method is less skewed than the buy and hold return method, usually t statistics

provides well-specified results. Ritter recommends the following t statistics and

estimated as:

𝑡𝐶𝐴𝑅1,𝑡= 𝐶𝐴𝑅1,𝑡 ∗ √

𝑛𝑡

𝑡 ∗ 𝑣𝑎𝑟 + 2(𝑡 − 1) ∗ 𝑐𝑜𝑣

Where nt are the event firms traded in each month, var is the mean variation of

ARi,t over 60 months and cov is the first order auto-covariance of the ARt series.

3.6.1 Hypotheses about the Long Run Returns Model

Based on the existing studies on IPO aftermarket performance, this study

attempts to investigate the impact of three groups of variables (ex-ante risk, signaling

and fundamentals factors) on long-run returns. Fama and French (1995) show a

positive relationship between the book value of shareholder’s equity to market value

and the aftermarket performance in the long run. They reveal that the book value to

market value may possibly use as a proxy for financial risk in return which is related

to the firm’s financial distress. Therefore, it is anticipated that the book to market

value multiple is positively linked with long-run performance. The earnings disclosed

in prospectus is viewed as a signal of a quality IPO in the market. An extant literature

finds a positive association of earnings published in prospectus and the aftermarket

performance. This implies that the firms reported earnings in the prospectus

outperform the market in the long run.

104

H18a: There is a positive relationship between the book value of shareholder’s

equity over offer prices and the IPO long-run returns.

H18b: There is a positive relationship between the earnings before IPO over

offer prices and the IPO long-run returns.

Fama and French (1992) argue that the firm size and the book to market value of

shareholder’s equity considered as a riskiness of the IPO firm. They used the natural

logarithm of IPO firm’s capitalization as a proxy for firm size and smaller the market

capitalization is the more risky IPO. Therefore, based on the risk and the return

theoretical assumption, the IPO offer size is negatively related to the aftermarket

performance in the long run. The existing literature (Amor and Kooli, 2017;

mumtaz,smith & ahmed, 2016; Michel, oded & shaked, 2014; Kerins, Kutsuna &

Smith, 2007) on IPOs and the risk-return theoretical assumption are used to develop a

number of hypotheses between the ex-ante risk factors and the aftermarket

performance in the long run. This implies that the long run returns model is an

increasing function of the ex-ante risk variables.

H19a: The average pre-IPO financial leverage is positively related to the IPO

long-run returns.

H19b: The firm’s capital availability risk before the IPO is negatively

associated with the IPO long-run returns.

H19c: The firm’s efficiency risk before the IPO is positively associated with

the IPO long-run returns.

H19d: The firm’s capacity risk before the IPO is positively related to the IPO

long-run returns.

H19e: The firm’s beta before the IPO is positively related to the IPO long-run

returns.

H19f: There is a negative relationship between the IPO offer size and the IPO

long-run returns.

As discussed in the previous section about underwriter reputation, the decision to hire

the prestigious underwriter indicates the signal of a quality firm. The high reputed

105

underwriters only provide consultancy to the quality firms in order to maintain their

standing in the financial markets. Therefore, it is expected that the IPO offered by the

prestigious underwriters performs well in the long run. The firm’s age is generally

considered as a proxy for IPO firms’ experience in the industry. It is expected that the

older firms have the ability to generate consistent profits. Therefore, due to the

conservatism principle in risk-return assumption, investors put a high demand an IPO

firms’ shares, which results in a higher aftermarket performance. It is expected that

the most experienced firms in terms of age are positively linked to the IPO long-run

performance. The percentage of shares retained by initial shareholders is usually

considered as a key signal of the quality firm. It is accepted that the information about

the initial shareholder’s capital structure at the time of IPO transaction reflects the

insider information about the prospects of the issuing firm. Therefore, it is anticipated

that the inverse proxy used as a percentage of shares offered for a portion of shares

retained by initial shareholders is negatively related to the IPO long-run returns.

H20a: The underwriter reputation is positively related to the IPO long-run

returns.

H20b: There is a positive relationship between the age of IPO firms and the

IPO long-run returns.

H20c: There is a negative relationship between the percentage of shares

offered to the general public and the IPO long-run returns.

The IPO long-run returns model includes residual error from valuation model and

initial excess returns as explanatory variables to control the impact of IPO mispricing

on the first day and a mean reversion effect takes place in the market respectively. In

this study, it is expected that the market is efficient and any mispricing on the first day

is ultimately corrected in the long run returns. Therefore, it is anticipated that the

positive values of residual are negatively linked to the aftermarket performance in the

long run. An extant literature on signaling theory suggests that the larger offer price

discount in the IPO offer prices by the high-quality firms used as a signal and perform

better in the long run as compared to the low-quality firms. Therefore, it is expected

that the initial excess returns are negatively linked to the IPO long-run returns.

106

H21: There is a negative relationship between the valuation residuals and the

IPO long-run returns.

H22: There is a negative relationship between the initial excess returns and

the IPO long-run returns.

3.6.2 CAPM, Fama-French Three- and Five-Factor Models

This study used the Sharpe-Lintner (1964) capital asset pricing model

(CAPM), Fama-French (1993) three-factor (FF3F) model and Fama-French (2015)

five-factor (FF3F) model to test the long-run abnormal performance of IPO portfolio

using the calendar-time regression approach. The Fama-French (1993) show that the

capital asset pricing model of Sharp (1964) cannot explain the cross-sectional

variation in the expected returns of a security or portfolio, which related to size

(market capitalization) and the value (book to market) factors. Fama-French proposed

a three-factor model that adds the size and book to market equity factors in addition to

the market risk factor. The CAPM is based on the following time series regression:

𝑅𝑝,𝑡 − 𝑅𝑓,𝑡 = 𝛼𝑝 + 𝛽𝑝(𝑅𝑚,𝑡 − 𝑅𝑓,𝑡) + 𝜀𝑝,𝑡

Equation (20)

When Fama-French (1992) introduce two more factors in the equation (20) then new

FF3F could be based on the following time series regression.

𝑅𝑝,𝑡 − 𝑅𝑓,𝑡 = 𝛼𝑝 + 𝛽𝑝(𝑅𝑚,𝑡 − 𝑅𝑓,𝑡) + 𝑠𝑝𝑆𝑀𝐵𝑡 + ℎ𝑝𝐻𝑀𝐿𝑡 + 𝜀𝑝,𝑡

Equation (21)

Where Rp,t is the value-weighted and/or equally-weighted returns on the IPO

portfolio in month t, Rf,t is the 3-months T-bills rate in the month t in Pakistan, Rm,t is

the PSX benchmark index (KSE100) returns in month t, SMBt is the difference

between the returns of value-weighted portfolio of small stocks and the big stocks in

the month t, HMLt is the difference between the returns of value-weighted portfolio of

high book to market equity stocks and the low book to market equity value stocks in

the month t. βp, sp and hp are the coefficients of market, size and value premium

factors. The intercept (αp) term is employed to test the null hypothesis that the mean

monthly excess return equals zero.

After the 20 years, more recent literature shows that the FF three-factor model

is also unable to explain the cross-sectional variation in expected returns of a security

107

or portfolio particularly related to profitability and investment along with other

anomalies. These anomalies include momentum (Jegadesh and Titman, 1993),

maximum daily returns (Bali et al., 2011), accruals (Sloan, 1996), idiosyncratic

volatility (Ang et al., 2006), net share issues (Loughran and Ritter, 1995; Ikenberry et

al., 1995) and liquidity risk (Pastor and Stambaugh, 2003).

Based on the extant literature on the Fama-French three-factor related

anomalies, Fama-French (2015) introduce five-factor model by adding profitability

and investment factors in the existing Fama-French three-factor model as:

𝑅𝑝,𝑡 − 𝑅𝑓,𝑡 = 𝛼𝑝 + 𝛽𝑝(𝑅𝑚,𝑡 − 𝑅𝑓,𝑡) + 𝑠𝑝𝑆𝑀𝐵𝑡 + ℎ𝑝𝐻𝑀𝐿𝑡 + 𝑟𝑝𝑅𝑀𝑊𝑡 + 𝑐𝑝𝐶𝑀𝐴𝑡

+ 𝜀𝑝,𝑡

Equation (22)

Where RMWt is the difference between the returns of the value-weighted

portfolio of robust profitability stocks and weak profitability stocks in the month t,

CMAt is the difference between the returns of the value-weighted portfolio of

conservative investment stocks and aggressive investment stocks in the month t. rp,

and cp are the coefficients of profitability and investment factors. The intercept (αp)

term is employed to test the null hypothesis that the mean monthly excess return

equals zero.

Equation (20), Equation (21) and Equation (22) models are estimated using the

Newey-West HAC (Heteroskedasticity and autocorrelation) consistent standard errors

to compute the t-statistics for the FF3F and FF5F model coefficients. To estimate the

parameters of market, size, value, profitability and investment factors, we collect

monthly data of 225 non-IPO firms listed on the PSX from 2000 to 2017.

3.6.2.1 FF 3-Factor Variables construction

The Fama-French three-factor model is assembled using the six value-

weighted portfolios designed on the basis of size and book-to-market ratio. SMBFF3F

is the average return on the three small portfolios minus the average return on the

three big portfolios. HML is the average returns on the two value portfolios minus the

average return on the two growth portfolios.

𝑆𝑀𝐵𝐹𝐹3𝐹 =1

3(𝑆𝑚𝑎𝑙𝑙_𝑉𝑎𝑙𝑢𝑒 + 𝑆𝑚𝑎𝑙𝑙_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝑆𝑚𝑎𝑙𝑙_𝐺𝑟𝑜𝑤𝑡ℎ) −

1

3(𝐵𝑖𝑔_𝑉𝑎𝑙𝑢𝑒

+ 𝐵𝑖𝑔_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝐵𝑖𝑔_𝐺𝑟𝑜𝑤𝑡ℎ)

108

𝐻𝑀𝐿 =1

2(𝑆𝑚𝑎𝑙𝑙_𝑉𝑎𝑙𝑢𝑒 + 𝐵𝑖𝑔_𝑉𝑎𝑙𝑢𝑒) −

1

2(𝑆𝑚𝑎𝑙𝑙_𝐺𝑟𝑜𝑤𝑡ℎ + 𝐵𝑖𝑔_𝐺𝑟𝑜𝑤𝑡ℎ)

Rm,t –Rf,t is the excess returns of the market, value weighted monthly returns of

KSE100 index and three-month treasury bills rate of SBP. SMBFF3F and HML for July

of year t to June of year t+1 include 225 firms of PSX for which we have market

equity data for December of t-1 and June of t, and book equity data for t-1.

3.6.2.2 FF 5-Factor Variables construction

The FF five-factor are assembled using the six value-weighted portfolios

designed on the basis of size and book-to-market ratio, size and operating

profitability, and size and investment factors each.

𝑆𝑀𝐵𝐵/𝑀 =1

3(𝑆𝑚𝑎𝑙𝑙_𝑉𝑎𝑙𝑢𝑒 + 𝑆𝑚𝑎𝑙𝑙_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝑆𝑚𝑎𝑙𝑙_𝐺𝑟𝑜𝑤𝑡ℎ) −

1

3(𝐵𝑖𝑔_𝑉𝑎𝑙𝑢𝑒

+ 𝐵𝑖𝑔_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝐵𝑖𝑔_𝐺𝑟𝑜𝑤𝑡ℎ)

𝑆𝑀𝐵𝑂𝑃 =1

3(𝑆𝑚𝑎𝑙𝑙_𝑅𝑜𝑏𝑢𝑠𝑡 + 𝑆𝑚𝑎𝑙𝑙_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝑆𝑚𝑎𝑙𝑙_𝑊𝑒𝑎𝑘) −

1

3(𝐵𝑖𝑔_𝑅𝑜𝑏𝑢𝑠𝑡

+ 𝐵𝑖𝑔_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝐵𝑖𝑔_𝑊𝑒𝑎𝑘)

𝑆𝑀𝐵𝐼𝑁𝑉 =1

3(𝑆𝑚𝑎𝑙𝑙_𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑣𝑒 + 𝑆𝑚𝑎𝑙𝑙_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝑆𝑚𝑎𝑙𝑙_𝐴𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑣𝑒)

−1

3(𝐵𝑖𝑔_𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑣𝑒 + 𝐵𝑖𝑔_𝑁𝑒𝑢𝑡𝑟𝑎𝑙 + 𝐵𝑖𝑔_𝐴𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑣𝑒)

𝑆𝑀𝐵𝐹𝐹5𝐹 =1

3(𝑆𝑀𝐵𝐵/𝑀 + 𝑆𝑀𝐵𝑂𝑃 + 𝑆𝑀𝐵𝐼𝑁𝑉)

Where SMBFF5F is the average returns on the nine small portfolios minus the

average return on the nine big portfolios. To estimate SMBFF5F (size factor), design a

size portfolio in July of year t for each IPO went public during year t, stocks are

sorted on the basis of market capitalization as at the end of June for each IPO listing

year. The two portfolios are constructed (small and big) based on the market

capitalization is greater or below the median and readjust with average returns

estimated using the value-weighted technique.

𝐻𝑀𝐿 =1

2(𝑆𝑚𝑎𝑙𝑙_𝑉𝑎𝑙𝑢𝑒 + 𝐵𝑖𝑔_𝑉𝑎𝑙𝑢𝑒) −

1

2(𝑆𝑚𝑎𝑙𝑙_𝐺𝑟𝑜𝑤𝑡ℎ + 𝐵𝑖𝑔_𝐺𝑟𝑜𝑤𝑡ℎ)

109

HML is the average returns on the two value (high book to market) portfolios

minus the average return on the two growth (low book to market) portfolios. The B/M

ratio sorted using the market capitalization at the end of December of t-1 and the B/M

ratio for the fiscal year ending in the calendar year t-1 for each IPO firm listed during

the calendar year t-1. Three portfolios designed as using the cut-off points of 30 and

70 percentiles and readjust with average returns estimated using the value-weighted

technique. This study form six portfolios from the intersection of two size and three

value factor portfolios (Small_Low, Small_Neutral, Small_High, Big_Low,

Big_Neutral, Big_High).

𝑅𝑀𝑊 =1

2(𝑆𝑚𝑎𝑙𝑙_𝑅𝑜𝑏𝑢𝑠𝑡 + 𝐵𝑖𝑔_𝑅𝑜𝑏𝑢𝑠𝑡) −

1

2(𝑆𝑚𝑎𝑙𝑙_𝑊𝑒𝑎𝑘 + 𝐵𝑖𝑔_𝑊𝑒𝑎𝑘)

RMW is the average return on the two high (robust) operating profitability

portfolios minus the average return on the two low (weak) operating profitability

portfolios. The profitability factor contains accounting data for the year ended

December t-1 and defined as the difference of annual revenues minus cost of goods

sold, administrative expenses and selling expenses divided by total assets for the year

t-1. Three portfolios designed as using the cut-off points of 30 and 70 percentilesand

readjust with average returns estimated using the value-weighted technique. This

study forms six portfolios from the intersection of two size and three profitability

factor portfolios (Small_Robust, Small_Neutral, Small_Weak, Big_Robust,

Big_Neutral, Big_Weak).

𝐶𝑀𝐴 =1

2(𝑆𝑚𝑎𝑙𝑙_𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑣𝑒 + 𝐵𝑖𝑔_𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑣𝑒) −

1

2(𝑆𝑚𝑎𝑙𝑙_𝐴𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑣𝑒

+ 𝐵𝑖𝑔_𝐴𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑣𝑒)

CMA is the average return on the two conservative investment portfolios

minus the average return on the two aggressive investment portfolios. CMA

(investment factor) defined as the percentage change in total assets in the fiscal year t

compared to the fiscal year t-1. Three portfolios designed as using the cut-off points

of 30 and 70 percentilesand readjust with average returns estimated using the value-

weighted technique. This study forms six portfolios from the intersection of two size

and three investment factor portfolios (Small_Conservative, Small_Neutral,

Small_Aggressive, Big_Conservative, Big_Neutral, Big_Aggressive).

110

4. Chapter 4

4. Results and Discussion

111

The chapter 4 splits into five sections. Section 1, explains the power of pre-

IPO valutation estimates to enlighten the cross-sectional variation in the post-IPO

market value of each valuation method and also probe the firm-specific characteristics

and stock market-related factors that had influenced the choice, bias and accuracy of

each valuation method employed by underwriters when valuing IPOs. Section 2,

explains the explanatory power of prospectus information (fundamentals, ex-ante risk

and signaling factors) on the IPO initial prices (valuation) and the cross-sectional

analysis of various valuation models. Section 3, provides the comprehensive analysis

of short-run abnormal returns also known as “underpricing” and to probe the effect of

prospectus information on the short-run returns. Section 4, provides the insights of

long-run aftermarket performance up to five years using Event-time (BHARs and

CARs) approaches to estimate the long-run aftermarket performance also known as

“long-run underperformance” and to probe the effect of prospectus information on the

long-run returns. Section 5, provides the robustness insights of LRR using Jenson’s

Alpha (intercept term) estimated through the Calendar-time approach. In this study,

Jenson’s Alpha has been estimated through capital asset pricing model proposed by

Sharpe-Lintner (1964), Fama-French three- and five factor models proposed by Fama

& French (1993, 2015). This chapter starts with the summary statistics of variables

followed by the quantitative analysis, hypotheses testing and debate on empirical

findings in all the sections are discussed.

4.1 The Choice, Bias and Accuracy of Valuation Methods

This section presents the insights of IPO valuation process used by Pakistani

lead underwriters and to uncover the relevant determinants of valuation methods. This

section explains the power of pre-IPO valuation estimates to explain the cross-

sectional variation in the post-IPO market prices of each valuation method and also

probe the firm-specific characteristics and market factors that had influenced the

choice, bias and accuracy of each valuation method employed by underwriters when

valuing IPOs.

4.1.1 Explaining the Choice of Valuation Methods

In this part, this study addresses the first research objective of “to investigate

the firm-specific characteristics and stock market-related factors that had influenced

the choice of valuation methods when valuing IPOs.”. This study also reviews how

112

lead underwriters add value estimates derived through pre-issue valuation process to

estimate the fair value estimates and determine the preliminary offer price of a given

IPO ( see section 3.3.1).

4.1.1.1 Descriptive Statistics

In relation to firm-specific characteristics used in the binary logit and cross-

sectional regression models, this study provides the descriptive statistics in Table 4.1.

As discussed in the earlier chapter of this study, data have been obtained from the

PSX DataStream and prospectuses (published at the time of formal listing in the

exchange) of IPO firms.

Table 4. 1: Descriptive Statistics of IPO Firm’s characteristics

Variable Name Mean Min Percentiles

Max SD N 25th 50th 75th

Total Assets (millions) 28,25 15.00 1,235 2,715 16,67 691,99 94,57 88

Firm Age (Years) 15.12 1.30 3.50 8.00 19.00 78.00 17.61 88

Property, Plant & Equip.(AIP) 40.08 0.001 8.964 36.98 68.19 99.863 31.46 88

Profitability (%) 20.67 -165.59 4.013 16.16 36.34 137.52 38.21 88

Sales Growth (%) 42.17 -48.338 0.00 18.90 55.89 740.68 95.07 88

Dividend Payout (%) 20.25 0.000 0.00 0.00 41.63 100.00 29.22 88

Market Returns (%) 7.859 -63.807 1.036 9.468 17.70 51.850 19.27 88

Ex-ante Uncertainty (%) 1.297 0.667 0.924 1.159 1.586 2.538 0.503 88

Dilution Factor (%) 23.31 2.500 14.92 25.00 27.87 50.000 10.71 88

Table 4.1 reports the summary statistics of IPO firms’ characteristics

employed in Binary Logit and Cross-sectional ordinary least squares regression

models to estimate the choice, bias and accuracy of each valuation method employed

by underwriters in the IPO valuation process. The financial variables data such as

total assets, firm age, property, plant & equipments, profitability, predicted sales

growth, dividend payout and dilution factor are taken from the latest financial

statements disclosed in the prospectus documents while market returns and ex-ante

uncertainty are calculated using the benchmark index of PSX. The descriptive

statistics of total assets (SIZE) demonstrate that on average total assets before the IPO

year is 28,25 (millions PKR) which symbolize the firm size when decided to go

public. But the values of percentiles and standard deviation depicts that the most of

the firms are smaller in size due to greater variability in the firm size. Another

implication of this lump is due to several privatization IPOs carried out in the sample.

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The extant literature on IPO enlightens that the youngest firms went public to expand

their business operations and to capitalize their growth opportunities. The firm age is

computed as the difference between the date of incorporation and formal listings for

trading at the exchange. It is witnessed that the minimum IPO firm age is 15 months

at the time of formal listing and the maximum age is 78 years old when it went public.

Due to the immense gap between the young and old firms, the natural logarithm of

age i-e Log(1+age) is employed as a proxy for age risk factor. This study observed

that the average age of newly listed firms is about 15 years. It is recorded that the

most firms added in the sample are young firms. The descriptive statistics of assets in

place (AIP) is estimated as the ratio of property, plant & equipment over total assets

in the latest financial year before the IPO. The average (median) of the AIP is 48.09%

(36.98%) in the latest financial year before the IPO tend to indicate that more than

50% firms’ fixed assets are below the median. In this study, about 40% IPO firms are

linked to the telecommunication and financial services sectors who invest more in

working capital rather than the fixed tangible assets.

Table 4.1 documents the descriptive statistics of forecasted profitability

(PROF) described as most latest year predicted operating earnings over latest years’

predicted sales before the IPO. The average (median) forecasted profitability is equal

to 20.68% (16.16%) before the IPO. Only 15 out of 88 IPO firms are predicted to

document negative earnings in latest financial statement and this study is not lead by

loss-making IPO firms. This indicates that the most profitable firms decide to go

public to raise long-term equity capital to capitalize the future opportunities. Further,

the predicted sales growth (GROW) are used as a proxy for growth opportunities in

most recent year and onwards. The average (median) predicted sales growth in sample

is 42.18% (18.91%) in the latest financial year. The results are consistent with the

argument of rapidly growing firms face challenges of cash imbalances in the short to

medium term because the capital investments are more than the cash inflows.

Pakistani firms that decide to go public as a rule disclosed their historical payouts

and/or associated companies in the prospectuses and this information helps investors

to identify the dividend policy in the future. As per Security and Exchange

Commission of Pakistan (SECP) policies, only asset management companies are

liable to pay above 80% dividend to get tax shield benefits. The average (median)

dividend payout is of 20.26% (0%) indicates that only 36 out of 88 firms have

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announced the dividends in the latest financial year before the IPOs. In an earlier

discussion, findings show that most firms of sample are young in age and have low

profits.

Table 4.1 also documents the summary statistics of market returns (MktRet)

during six months before the IPO. A persistent rise in aggregate stock returns may

point out the window of opportunity for the growth companies to get the benefits of

high valuations in the market. This study adds market returns during a six month

interval from 185 trading days prior to IPO and 5 days before the formal listing of

IPO firm. The average (median) market returns before the IPO is 7.86% (9.47%),

positive returns before the IPO open the ‘window of opportunity’ to growth

companies. It is observed that, in a high volatile market, the investors are more

indecisive about the intrinsic values of new offerings. The ex-ante uncertainty (SD) is

estimated as a standard deviation of the benchmark index (KSE100) of daily returns

during a six month interval before the IPO. The average (median) ex-ante uncertainty

equals 1.30% (1.16%) in the preceding months before the IPO. Next, this study

examines the dilution factor is estimated through the percentage of shares offered over

total post-issue outstanding shares at the time of listing. The average (median)

dilution factor equals 23.32% (25%) at the time of formal listing. The large fraction of

equity sold to general public indicates that poor quality firms and underwriters, prefer

to use multiples valuation to value firms’ equity as per needed valuations.

4.1.1.2 The Univariate Analysis

The key reason of univariate analysis is to explain the associations between

the variables used in the binary logit and cross-sectional regression models. Table 4.2

demonstrates the coefficients of correlation of variables employed in the choice, bias

and accuracy of valuation models and t-statistic are reported below each correlation

coefficient. The correlation coefficient between the firm age and firm size appears to

be positive and strongly statistically significant at the 99% level of confidence.

However, the correlation coefficient appears to be modest (0.4173). This implies that

the level of maturity tends to increase the size of the firm and disclosed data on age

and size come into view by stakeholders in their pricing decision during the IPO

process.

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Table 4. 2: Correlation Matrix of Variables used in Binary Logit & Cross-sectional Models

Total Assets Age AIP Profitability Sales Growth Dividends Technology Mkt Return Ex ante Und Rep

Total Assets 1

Age 0.4150 1

4.230***

AIP 0.0882 -0.0017 1

0.821 -0.016

Profitability 0.0827 0.1662 -0.1919 1

0.770 1.563 -1.813*

Sales Growth -0.2449 -0.2374 -0.1890 -0.0994 1

-2.342** -2.266** -1.785* -0.927

Dividends 0.1880 0.2727 -0.1104 0.2624 -0.0892 1

1.775* 2.628** -1.030 2.522** -0.831

Technology -0.0230 0.0731 0.0561 -0.1595 0.2257 0.0417 1

-0.213 0.679 0.521 -1.498 2.148** 0.387

Mkt Return -0.0192 -0.1808 -0.0664 -0.1270 0.0208 0.0270 0.0311 1

-0.178 -1.704* -0.618 -1.188 0.193 0.251 0.289

Ex ante -0.0516 -0.1072 0.0401 0.0261 0.1632 -0.0653 -0.0174 -0.3382 1

-0.479 -0.999 0.372 0.242 1.534 -0.607 -0.161 -3.332***

Und Rep -0.0593 -0.0469 -0.0249 -0.0994 0.1229 0.0696 -0.0254 -0.0327 -0.2312 1

-0.551 -0.435 -0.231 -0.926 1.148 0.647 -0.235 -0.303 -2.204**

Dilution Factor -0.5830 -0.3571 -0.1256 -0.0917 0.1013 -0.1572 -0.1720 0.0052 -0.0531 0.1728

-6.653*** -3.545*** -1.174 -0.854 0.944 -1.476 -1.619 0.048 -0.493 1.627

***significant at the 1% level, **significant at the 5% level and *significant at the 10% level.

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The correlation coefficient of property, plant & equipment related to firm size

and age is positive but insignificant. The coefficients demonstrate that there is weak

association between the property, plant & equipment, firm age and size. The

correlation coefficient of operating profitability is positively related to the firm size

and the number of years in the industry, but these coefficients appear to be

insignificant. The amount of association related to size (0.0742) and age (0.1534) are

nominal. At the same time, the coefficient of operating profitability appears to be

negatively related to the property, plant & equipments but insignificant. The

coefficient related to asset intangibility is (-0.1919).

The coefficient of sales growth appears to be negative and significant related

to the size and age of the firm at the 5% level each respectively. The value of

coefficient reveals a moderate association related to size (-0.2445) and age (-0.2366)

as expected. This implies that the pace of sales growth turns down as size and age of

the firms’ increase over time. The coefficient of sales growth also appears to be

negative and insignificant linked with the fraction of property, plant & equipments

over total assets and the operating profitability. The coefficient of dividends payout

are statistically significance and positive linked with size and age of the firms at the

90% and 95% level of confidence respectively. The degree of association and

significance are consistent with the economic theory of corporate payouts as the larger

and mature firms tend to announce more dividends than to retain as capital reserves.

The coefficient of dividends payout is positively linked with operating profitability

and significant at the 1% level. The amount of profitability coefficient demonstrates a

restrained association (0.2624). This implies that the larger profitable firms pay more

net income as dividends than the less profitable firms. This study also investigates the

negative and insignificant association of dividends related to the sales growth and the

property, plant & equipments. This study has investigated that the correlation

coefficient of market returns before deciding to go public is negative and strongly

significantly related to the age of IPO firm. The amount of coefficient related to prior

IPO market returns is (-0.1808). This implies that the younger firms took advantage of

high valuations in pre-issue valuation procvss. The coefficients of market returns are

negative and insignificant to the firm’s size and operating profitability factors.

Table 4.2 presents the association of ex-ante uncertainty related to other

predictors. The coefficients of ex-ante uncertainty appear to be negatively related to

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the size and age of IPO firms. The amount of association related to firm size is (-

0.0516) and firm age is (-0.1076) but found to be insignificant. The degree of ex-ante

uncertainty linked with property, plant & equipments, operating profitability and sales

growth are (0.0385), (0.0272) and (0.1632) respectively. The coefficient of ex-ante

uncertainty related to market returns is nnegative and significant at the 5% level. The

amount of association reveals a modest correlation between the SD and percentage of

market returns before IPO. This implies that on the average variation in daily market

returns before the IPO decrease when the market returns demonstrate high positive

returns and vice versa. The coefficient of dilution factor is is negative and strongly

significant related to the size and firm age as expected. The degree of association

demonstrates a modest correlation related to firm age (-0.5830) and firm size (-

0.3571). The association of dilution factor appears to be irrelevant related to property,

plant & equipments, operating profitability, sales growth, corporate payouts, prior

IPO market returns and ex-ante uncertainty.

In sum, the correlation matrix presents the disorder findings of the association

between each predictor. The associations of operating profitability come into sight

positive but insignificant. In addition, the results of association of sales growth and

the corporate payouts are consistent with IPO theory. The correlation of dilution

factor appears to be negative and statistically significant to the size of IPO firms and

maturity level of the IPO firms as expected. Though, the purpose of the univariate

analysis is not to examine the hypotheses related to valuation methods. The testing of

hypotheses related to binary logit models is discussed in the next sections.

4.1.1.3 The IPO valuation process

In this section, pre-IPO valuation process is discussed using a graphical and

tabular overview. Figure 4.1 describes the IPO valuation and pricing process involved

in the newly listed securities while Table 4.3 presents the frequencies and percentages

of the valuation methods used by the lead underwriters during the sample period.

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Figure 4.1 provides an overview of the IPO valuation process and IPO pricing

process when private firms went public. After the submission of application of New

Equity Listing Application in the stock exchange, issuer firm appoints a financial

advisor to conduct a due diligence on a firm’s financial statements and core business

operations. When permission is granted by the relevant exchange, the lead

underwriter has to submit and/or publish a prospectus document which contain all the

important information about the issuing firm such as the shareholding structure of the

company, purpose of IPO proceeds, future prospects, company history, details of

trailing financial statements and valuation methods used to estimate fair value, and

deliberate discount in offer prices.

Figure 4. 1: IPO Valuation and Pricing process involve in New Offerings

Figure 4.1 described that the Pakistani lead underwriters employed dividend

discount model, discounted cash flow and comparable firms based on size and

industry to estimate the fair-value estimates. The fair-value estimates calculated using

an underwriter specific valuation method perceived as an ex-ante estimate of market

value. Then underwriters deliberately offer the discount in preliminary offer prices to

attract more participation in the auction. In Pakistan, only high net-worth individuals

(HNWIs) and Institutions are allowed to participate in the bookbuilding auction while

general investors can subscribe after the completion of bookbuilding auction. The

offer price computed through the demand of bookbuilding participants is called a

strike price and this strike price is followed by the general public portion.

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Table 4. 3: The Summary of Valuation Methods disclosed in prospectus

Valuation Method Frequency Percentage

Multiples 65 73.864

• Price/Earnings Ratio 36 40.909

• Price/Book Ratio 51 57.955

• Price/Sales Ratio 1 1.136

• Price/EBITDA Ratio 1 1.136

Discounted Cash Flow 20 22.727

Dividend Discount Model 15 17.073

Source: Complied from prospectus documents

Table 4.3 presents the summary of valuation methods disclosed by Pakistani

underwriters in the prospectuses of 88 IPOs listed on PSX in the sample period. In

practice, most underwriters publish only widely accepted valuation models rather than

the unconventional equity valuation methods as highlighted by Fernandez (2001)11. It

has been observed that the multiples valuation approach is used almost 74% of the

times. This similar fraction is observed in the France (Roosenboom, 2007) and

Belgium (Deloof et al., 2009). The most admired multiples based on industry and/or

size is the price-book ratio, followed by the price-earnings ratio, price-sales ratio and

price-EBITDA ratio. The lead underwriters calculate the fair value estimates by

taking the product of average (or median) of specific accounting-based information

multiple with the matching accounting-based information about the IPO firm.

Usually, the investment banks used projected accounting information for ongoing and

subsequent years during multiples estimations. Furthermore, the lead underwriters

also regularly employ other accounting-based equity valuation models. Table 4.3

documents that DCF is used to calculate the fair value estimates of IPO firm’s equity

in 20 times (22.73%) and the dividend discount model is used to calculate the intrinsic

value of newly listed firm by 15 times (17.07%). The percentage of the DCF model

used in emerging economy is greater than the US lead underwriters (Houston et al.,

2006: Asquith et al., 2005) but less than the France lead underwriters (Roosenboom,

2012).

11 Fernandez (2001) talks about the valuation of Terra-Lycos (internet service provider) in 2000 by

several investment banks, which use weighted-averages of inquisitive combination of different

multiples such as number of inhabitants, gross national product (GNP) per capita, enterprise-value

(EV) per page view, capitalization per subscriber and capitalization per page view.

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Practically, the multiples valuation is frequently used by investment banks to

value IPO firm’s equity while some irregularities have been observed with this

valuation method. First, from a theoretical viewpoint, the economic rationale of

multiples is open to discussion as the analysis is not derived by the fundamentals like

future cash flows, growth opportunities and predicted uncertainties which notify

regarding value independent of market prices. Second, the multiples valuation method

assumes that the market is efficient in order to determine prices for the comparables.

In addition, the multiples valuation method is static in nature in most of the cases,

while DCF is an appropriate measure for a dynamic business environment. From a

conceptual viewpoint, the multiples valuation technique has a problem in

implementation. It is quite difficult to recognize comparable firms having similar

business operations and firm-specific financial characteristics. The multiples

valuation leaves too much room for ‘playing with mirrors’ and open freedom for lead

underwriters to get a needed valuation. Penman (2007) argues that different multiples

offer different valuation estimates and it is difficult to assess which is most unbiased

and accurate. Pratt, Reilly and Schweihs (2000) highlight that the investment banks

fail to make necessary modifications to comparable firms’ financial statements and an

easy dependence on the median or mean of comparable firms multiples to estimate

incorrect results. Lie and Lie (2002) discuss that the DFCF is required sturdy work to

estimate appropriate forward cash flows and discount rates rather than the multiples

valuation estimates. There are some difficulties 12 that recognized related to

underwriter’s real choice of valuation techniques are found from the extant literature.

4.1.1.4 The Analysis of Binary Logit Regression Models

In this section, this study investigates the firm-specific characteristics and

stock market-related factors that had influenced the choice of each valuation method

used by lead underwriters when valuing IPOs. In this analysis, the valuation methods

employed by underwriter are used as dependent variables and t-statistics have been

estimated through Huber/White standard errors. The findings of the binary logit

12 In order to estimate the fair value of IPOs, the lead underwriter is expected to get some consensus

value using the various valuation methods. In emerging markets, the practitioners usually employ

valuation methods for valuing IPOs that are used by developed countries practitioners. The choice of

valuation method is not based on the model’s prerequisite than the desired targeted value. Therefore,

the underwriters deliberately offer discount in fair value estimate (1) to avoid law-suit from outside

investors due to bad-quality IPOs, (2) to avoid the chance of under subscription, (3) the chance of

missing material information disclosure in offering documents, and (4) to create excess demand in the

market

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model assist to examine the cross-sectional determinants of the chosen valuation

method.

Table 4. 4: Results of Binary Logit of Preferred Valuation Methods

Multiples

Models DDM DCF Multiples P/E Ratio P/B Ratio

Independent Variables (1) (2) (3) (4) (5)

Intercept -10.9957** 9.0024 -2.7813 -13.4749** 4.3583

(-2.025) (1.236) (-0.470) (-2.099) (0.924)

Total Assets 0.7851 -0.9634

0.2420 0.9255 -0.3821

(1.374) (-1.391) (0.435) (1.583) (-0.890)

Firm Age -0.3841 -2.5898**

2.4833** 4.4598*** 0.6692

(-0.394) (-2.323) (2.328) (4.188) (0.943)

Property, plant & Equip. 0.0064 0.0307**

-0.0336** -0.0216* -0.0230**

(0.483) (2.648) (-3.032) (-1.779) (-2.558)

Operating Profitability -0.0006 0.0198*

0.0033 0.0125 -0.0125

(-0.039) (1.782) (0.509) (1.331) (-1.379)

Sales Growth -0.0026 -0.022**

0.0056 0.0001 0.0063

(-0.691) (-2.377) (0.894) (0.029) (1.149)

Dividend Payout 0.0408** -0.0119

-0.0123 -0.0184* -0.0009

(2.922) (-0.946) (-1.344) (-1.836) (-0.101)

Technology 1.0477 0.675

0.0053 -0.1599 -0.0806

(1.295) (0.905) (0.007) (-0.214) (-0.138)

Market Returns 0.0032 0.043**

-0.0317* 0.0321* -0.0244*

(0.143) (2.398) (-1.753) (1.703) (-1.667)

Ex-ante -0.7295 -0.1000

-0.0845 -0.0422 -0.1574

(-0.633) (-0.129) (-0.081) (-0.038) (-0.295)

Underwriter Reputation 1.9519*** 0.3148

0.5258 0.7142 0.3514

(2.631) (0.431) (0.817) (0.984) (0.661)

Dilution Factor -0.0213 -0.0143

0.0179 -0.0266 -0.0010

(-0.726) (-0.386) (0.483) (-0.649) (-0.032)

McFadden R2 0.3797 0.3723 0.2823 0.4349 0.1620

LR-Statistic 26.976*** 32.857** 27.149** 51.322*** 19.225*

Prob(LR-Statistic) 0.0046 0.0005 0.0044 0.0000 0.0572

N 88 88 88 88 88

The Z-Statistics are within parentheses and calculated on the basis of Robust Huber/White standard

errors. ***significant at the 1% level, **significant at the 5% level and *significant at the 10% level.

In the dividend discount model (DDM) valuation analysis, Table 4.4 reports

that the Firm age is negatively linked with the choice of DDM valuation method and

these findings contradict with the assumption of the DDM valuation theory that the

underwriters prefer to use DDM when valuing older firms. These findings contrary to

Deloof et al., (2009) argued as the dividend discount model is the best measure to

valuing stable and large firms. The Total Assets used as a proxy for firm size and

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results unfold that the firm size is positively linked with the preference of DDM

valuation method as expected. The coefficient of total assets appears to be positive but

statistically insignificant. The result of firm size determinant provides the supporting

evidence of the proposed hypothesis of H1 which present the positive association

between the firm size and choice of DDM valuation method. The Property, plant and

equipment is also positively linked with the DDM valuation method and underwriters

more likely to use DDM when valuing large firms. The finding reveals that the

coefficient of Operating Profitability is negative and this indicates that the

underwriters less likely to use DDM when valuing firms which are relatively

profitable in the IPO year. On the similar pattern, the coefficient of Sales Growth

appears to be negative and statistically insignificant. Underwriters do not use the

DDM valuation method when firms’ sales growth is relatively less. The findings

reveal that the lead underwriters prefer to choose the DDM valuation method that

firms had offered cash and/or stock payouts in the trailing years. The coefficient of

Dividend Payout appears to be positive (0.0408) and strongly significant at the 95%

level of confidence. This implies that the investment banks prefer to choose DDM

valuation model when valuing large profitable firms which offered their major income

as corporate payouts. These findings are in sequence with working hypothesis of H6

and comparable with existing literature (Bhattacharya, 1979; Damodaran, 1994;

Roosenboom, 2007; Deloof et al., 2009; Abdulai, 2015). The coefficient of

Technology firms is positive but statistically insignificant; underwriters do not use

direct valuation for technology firms and these results are inconsistent with the DDM

valuation theory and Roosenboom (2007) findings. Bartov et al., (2002) argue that the

technology firms are probably to be assessed using multiples valuation than the direct

estimation methods such as DCF and DDM because these models do not incorporate

the value of growth options in the fair-value estimates. The Market returns are

positively linked with the preference of DDM valuation method but statistically

insignificant. According to valuation theories, relative valuation techniques are more

common when aggregate stock market returns are higher rather than the direct

valuation. The findings conjecture that the possible investors may participate

aggresively in the high dividend yield IPOs when market sentiments are bullish before

IPO. The insignificance of coefficient of Ex-ante uncertainty is appear to be negative

that indicates that the underwriters do not use the DDM valuation method when

aggregate market volatility is large before the IPO. The coefficient of Dilution Factor

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appeared to be negative. The coefficient of Underwriter Reputation appears to be

positive (1.9519) and strongly significant at the 99% level of confidence. This implies

that the reputed underwriters prefer to use direct valuation than the relative multiples

“to leave less money on the table” during the IPO valuation process.

In the discounted cash flow (DCF) valuation analysis, Table 4.4 reports that

the lead underwriters prefer to choose the DCF valuation method for young firms. The

coefficient of Firm Age appears to be negative (-2.5898) and strongly significant at

the 95% level of confidence. This implies that the lead underwriters prefer to use DCF

valuation method when valuing young firms. The results of younger firms are

consistent with Roosenboom (2007) but contradict with the Abdulai (2015), and Kim

and Ritter (1986) as they argued that it is complicated to predict future cash flows for

young firms without trailing financial fundamentals. The results reveal that the

coefficient of Property, Plant & Equipment appears to be positive (0.0307) and

significant at the 95% level of confidence. This implies that the underwriters more

likely to use the DCF method when firms have placed their more investments as fixed

assets. The firms having more assets as fixed assets expected to generate more

revenue in the future and DCF is considered as a good measure to capitalize predicted

future cash flows to estimate the fair value estimates. These findings are in sequence

with proposed hypothesis of H3 and findings of Lev (2001) as accounting numbers

supposed to be a good estimator of the firm value from the tangible assets than the

intangible assets. The findings reveal that the coefficient of Operating Profitability

appears to be positive (0.0198) and significant at the 90% level of confidence. This

implies that there is a positive association between the operating profitability and the

underwriter’s choice to select DCF to value IPOs. The results are in sequence with the

assumption of the IPO valuation theory that the profitable firms are valued by the

direct valuation techniques. Roosenboom (2007) and Abdulai (2015) also find a

positive relationship between the choice of DCF and extent of operating profitability

before the IPO. The predicted sales growth in the IPO year is used as a proxy for

growth opportunities. The findings show that the coefficient of Sales Growth appears

to be negative (-0.0220) and significant at the 95% level of confidence. This implies

that the rapidly growing firms face challenges of cash imbalances in the short to

medium term because the capital investments are more-than the cash inflows. The

coefficient of Market Returns is (0.043) and statistically significant at 95% level of

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confidence. These findings are in sequence of Penmen (2001), Roosenboom (2007)

and Demirakos, Strong & Walker (2010). The findings report that the Technology

firms and Underwriter Reputation are positively related to the decision of DCF

valuation method but found statistically insignificant. This study conjectures that

market sentiments before the IPO offer a “window of opportunity” when market

participants are more enthusiastic to purchase risky stocks and more willing to believe

the cash flow and discount rate suppositions factoring the DCF model. The findings

also highlight the Ex-ante uncertainty and Dilution Factor are negatively linked with

the preference of DCF valuation technique. This implies that the market participants

may be more uncertain about fundamental values during high ex-ante uncertainty

because the DCF method offers information which investors required to estimate the

fundamental value of the IPO firms. The findings reveal that the underwriters do not

use the DCF valuation method when valuing larger, older and lower growth firms.

In the multiples valuation analysis, Table 4.4 presents the findings of binary

logistic regression model when lead underwriters used comparable firms as valuation

multiples when valuing IPOs. The findings reveal that the coefficient of Firm Age

appears to be positive (2.4833) and strongly significant at the 95% level of

confidence. This implies that the lead underwriters prefer to choose the multiples

valuation method when valuing older firms. These results are inconsistent with the

proposed hypothesis that older firms are valued by direct valuation methods rather

than the comparable firms approach while findings are also evident from other

existing studies such as Roosenboom (2007), Deloof et al., (2009), Demirakos et al.,

(2010), and Kim & Ritter (1999) as they argued that it is complicated to predict future

cash flows for young firms without trailing financial fundamentals. The results reveal

that the coefficient of Property, Plant & Equipment appears to be negative (-0.0336)

and strongly significant at the 95% level of confidence. This implies that the

underwriters use multiples valuation method when firms have placed less investment

in fixed capital investment. Firms having more assets as fixed are expected to

generate more revenues in the future and vice versa, multiples technique is considered

as a good measure to discount predicted future cash flows to estimate the fair value

estimates. These findings are comparable with Abdulai (2015) but contrary to the

Roosenboom (2007). The findings show that the coefficient of Market Returns

appears to be negative (-0.0317) and weakly significant at the 90% level of

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confidence. This study conjecture that the aggregate stock market returns before the

IPO offer a “window of opportunity” when market participants are more eager to

purchase risky stocks rather than based on their fundamental estimates. This implies

that the underwriter prefers to use relative comparable firms approach to value IPO

firms when market sentiments are bullish before the IPO. As consistent with the

valuation theories, this study finds that the lead underwriters more likely to use

comparable multiples when IPOs are relatively profitable and expected positive sales

growth in IPO year. The findings reveal that the coefficient of dividends payout and

market returns before the IPOs are negatively linked to the selection of multiples

valuation method. This implies that only mature and large profitable firms offer

corporate payouts and underwriters are prefer to use comparable multiples when firms

are immature in terms of sales and enhancing their business operations. The findings

also reveal that the prestigious underwriters opt multiples when valuing IPOs. The

dilution factor represents the percentage of shares offered by initial sponsors to the

general public and there is a positive association with the dilution factor and the post-

IPO valuation estimates. The investment banks are more likely to use comparable

multiples approach when firms having the large fraction of dilution factor at the time

of valuation process. The findings unfold that investment banks prefer to use

multiples approach when valuing technology firms.

In this study, the findings report that the underwriters use price-book and

price-earnings ratios as multiples valuation measures. This part extend our analysis to

further investigate the determinants of both P/E and P/B ratios. Table 4.4 shows the

findings of binary logit regressions when P/E and P/B used as dependent variables

separately. In the P/E analysis, the statistically significant determinants are firm age,

property plant & equipment, dividend payout and market returns before the IPO. Most

of the findings are consistent with multiples valuation analysis and comparable with

Deloof et al., (2009) and Demirakos, Strong & Walker (2010). The findings reveal

that the underwriters prefere to choose P/E valuation measure when aggregate stock

market returns are positive before IPO, don’t have the history of offer corporate

payouts, have fewer investments in fixed assets and older firms. The findings report

that the P/E multiple is positively linked with the high operating profitability, high

sales growth and the prestigious underwriter factors. On the other side, underwriters

prefer to choose a P/B valuation measure when aggregate stock market returns are in

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bearish fashion before the IPO and firms having fewer investments as fixed assets.

The findings reveal that the prestigious underwriters prefer to choose both P/E and

P/B multiples during high ex-ante volatility in the market before the IPO. The

findings conclude that the fair value of technology firms and positive sales growth

firms are drawn from predicted growth opportunities which can easily estimate

through multiples valuation approach.

In sum, the findings of binary logit model of DDM analysis report that the

dividends payout is a key factor when underwriters employ DDM as a valuation

method. However, the firm age, asset-in-tangibility, operating profitability and

predicted sales growth are more considered factors when underwriters used DCF as a

valuation method. In multiples valuation, firm age, asset-in-tangibility and aggregate

market returns before IPOs are significant determinants. The most findings are in line

with proposed hypotheses and valuation theories.

4.1.2 Explaining the Bias of Valuation Methods

In this part, this study addresses the second research objective “to investigate

the firm-specific characteristics and market factors that had influenced the bias

associated with each valuation method”. First, the Wilcoxon Sign Rank test and

standard t-statistics on the medians and mean values of signed prediction errors of

each valuation method are used to estimate the bias associated with each valuation

method. Second, the cross-sectional regression models for each valuation method are

carryied out to investigate the firm-specific characteristics and market-related factors

that had influenced the signed prediction errors.

4.1.2.1 The Analysis of Sign Prediction Errors

In this part, the descriptive statistics of SPE of each valuation method

employed by underwriters are discussed. The SPE is computed as (Estimated

valuei,preIPO – Market Valuei) /Market Valuei, where Estimated Value is provided by

the lead underwriters in the prospectuses to compute the fair value estimates before

IPO using several valuation methods and Market Value represents the first day closing

price of IPO firm i. Kim and Ritter (1999), Francis et al., (2000) Liu et al., (2002),

Deloof et al., (2009) and Roosenboom (2012) used the closing prices of the first

trading day is to estimate the signed prediction errors. An extant literature conjectures

127

that the signed prediction errors capitalize the bias associated with each valuation

technique and these errors also used in the cross-sectional analysis of bias regressions

as a dependent variable.

Table 4.5: Analysis of Signed Prediction Errors (at 1st Day Closing Prices)

Valuation Method Mean Min Percentiles


Dividend Discount Model (%) 3.99 -58.50 -22.48 -1.91 27.27 100.06 39.58 15

Discounted Cash Flow (%) -3.25 -76.30 -17.81 -0.50 13.70 36.96 27.16 20

Multiples (%) 11.21** -55.04 -17.92 5.64* 34.68 236.78 43.36 65

P/E Ratio (%) 9.88 -55.04 -23.52 5.26 34.73 236.78 50.84 36

P/B Ratio (%) 10.81** -49.03 -9.61 6.78*** 27.00 100.29 35.13 51

Fair Value Estimates (%) 8.81** -76.30 -14.92 4.18* 25.47 236.78 40.63 88

***significance at 1% level, **significance at 5% level, *significance at 10% level

Table 4.5 presents the descriptive statistics of signed prediction errors of each

valuation method by undertaking first day closing prices of IPOs as market values.

The Wilcoxon Sign Rank test and standard t-statistics on medians and mean values of

sign prediction errors different from zero are used to estimate bias attached with each

valuation method. According to an efficient market hypothesis and an extant

literature, signed prediction errors approach is the best measure to capture the bias

associated with each valuation method. The findings show that the most valuation

methods are associated with positive values of mean and median valuation prediction

errors which are significantly different from zero. The results show that the positive

valuation prediction errors for all valuation methods exceed 50%. The findings reveal

that the DDM and DCF seem to be unbiased value estimators: the median valuation

prediction errors are only (-1.92%) and (-0.50%) respectively. The median values are

not statistically significantly different from zero estimated through the Wilcoxon Sign

Rank test. This implies that the lead underwriters accurately estimate the intrinsic

value of issuing firms’ equity. These findings are consistent with Deloof, Maeseneire

& Inghelbrecht (2009, 2002), and Francis, Olsson & Oswald (2000) but contrary to

Roosenboom (2012), and Cassia, Paleari & Vismara (2004). Multiples valuation

method (combined all separate multiples) produces biased value estimates: the median

valuation prediction error is 5.64% and significant at the 10% level different from

zero. This suggests that the lead underwriters overestimate the market prices ex-ante.

On the other hand, the key issue of comparing the bias for all valuation methods is

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that certain valuation methods more suitable than others. Furthermore, the multiples

valuation analysis is enhanced to individually analyze the measures of multiples to

examine the bias associated with each multiple measures. The finding shows that the

P/E ratio seems to be an unbiased value estimator while P/B ratio produces biased

value estimates. If market values are considered as equilibrium prices then P/B ratio

tends to overestimate value. These findings are consistent with existing literature

(Deloof, Maeseneire & Inghelbrecht, 2009, 2002; How, Lam & Yeo, 2007; Cassia,

Paleari & Vismara, 2004) while contrary with Roosenboom (2012).

Table 4. 6: Analysis of Signed Prediction Errors (at IPO Offer Prices)

Valuation Method Mean Min Percentiles


Dividend Discount Model (%) 44.01** 2.63 27.27 28.95** 59.00 96.07 27.74 15

Discounted Cash Flow (%) 12.64** 0.00 0.00 0.00** 20.67 78.57 20.77 20

Multiples (%) 39.38*** -30.60 4.56 31.92* 60.33 227.60 53.56 65

P/E Ratio (%) 39.05** -29.20 -2.58 31.46* 49.00 225.38 55.31 36

P/B Ratio (%) 34.82*** -35.00 2.40 22.80* 61.67 227.60 49.07 51

Fair Value Estimates (%) 32.29*** -30.60 0.00 23.56* 46.46 227.60 45.58 88


Table 4.6 presents the descriptive statistics of signed prediction errors of each

valuation method by considering the offer prices of IPO firms as market values. This

study used Wilcoxon Sign Rank test and standard t-statistics on medians and mean

values different from zero of signed prediction errors to estimate the bias associated

with each valuation method. Findings reveal that the no single valuation measure

appears to be insignificant and produces biased value estimates. Comparing each

valuation method, this study finds that the discounted cash flow method is the least

biased estimates of value. These findings are comparable with Cassia, Paleari &

Vismara (2004).

These findings raise the question that why lead underwriters intentionally

overvalue the fair-value estimates with respect to the immediate aftermarket prices

and at preliminary offer prices. One of the reasons that the lead underwriters involved

in both to estimate the fair-value estimates and to set preliminary offer prices in many

cases is to deliberately offer a large discount to the investors. This implies that the

higher offer price discounts are associated with the biased valuations. The findings

conjecture that the biased valuations of lead underwriters may offer opportunities to

the investors to impatiently participate in the subsequent offers.

129

4.1.2.2 Cross-Sectional Analysis of Bias of Valuation Methods

In this part, this study investigates the firm-specific characteristics and market

factors that had influenced the bias related to several valuation methods used by the

lead underwriters when valuing IPOs. In this analysis, the signed prediction errors are

used as dependent variables for each valuation method employed by underwriters and

t-statistics have been estimated through White (1980) heteroscedastic standard errors.

To limit our analysis, this analysis used only three most popular techniques for cross-

sectional regression analysis (DDM, DCF, multiples and fair-value valuations as a

weighted average of all valuation methods).

Table 4.7 shows the results of cross-sectional regressions of bias associated

with dividend discount model, discounted cash flow, comparable multiples and the

fair value estimates methods. In the DDM bias valuation analysis, the number of

observations is very limited and only three variables have been used on the basis of

theoretical support discussed in the research methodology chapter. Table 4.7 reports

that the coefficient of firm size appears to be negative and strongly significant at the

90% level of confidence. The finding reveals that the large firms produce less biased

valuation when followed by the DDM method. The results are comparable with

Beatty and Ritter (1986) who argue that the larger IPO firms can be easily estimated

through direct valuation models such as discounted cash flow model and dividend

discount model as they are more stable in terms of market share, revenue growth,

payout history and forecasted cash flows. The results document that the firms having

large investments as fixed show less biased valuations. This implies that the DDM is

an appropriate value estimator to determine the fair-value estimates for capital-

intensive firms. The results document that the higher market returns before the IPO

show less biased valuations when valuing through DDM method. These findings are

consistent with Roosenboom (2012) bias DDM valuation analysis.

In the discounted cash flow (DCF) bias valuation analysis, the number of

observations is very limited and only four variables have been employed on the basis

of theoretical support discussed in the research methodology chapter. Table 4.7

reports that the coefficient of firm size appears to be negative and significant at the

90% level of confidence. The results show that the large firms produce less biased

valuations when followed by DCF method. The results are consistent with the debate

of Beatty and Ritter (1986). The results document that the firms having a large portion

130

of their investments as fixed show less biased valuations. The coefficient of predicted

profitability in the IPO year appears to be positive and strongly significant at the 99%

level of confidence. This indicates that the more profitable firms produce less biased

valuations. The findings are consistent with existing literature (Cassia, Paleari &

Vismara, 2004; Francis, Olsson & Oswald, 2000). The coefficient of predicted sales

growth in the IPO year is negative and strongly significant at the 99% level of

confidence. This indicates that the higher sales growth firms produce less biased

valuations.

Table 4. 7: Cross-sectional Regressions of Bias of Valuation Methods

Model SPE_DDM SPE_DCF SPE_ Multiples SPE_Fair Value

Independent Variables (1) (2) (3) (4)

Intercept 130.6102* 9.3872 24.1169** 17.9229**

(2.159) (1.342) (2.055) (2.332)

Size -11.9973* -5.5221* -3.8731** 0.8149

(-2.1874) (2.051) (-2.105) (0.332)

Firm Age -3.9101 -6.0302

(-0.303) (-1.150)

Property, plant & Equip. 0.2575* -0.1738 0.1093 -0.0245

(2.141) (-1.432) (0.763) (-0.416)

Operating Profitability -0.0736 -0.1172

(-0.655) (-1.277)

Sales Growth 0.3084*** -0.0867 -0.0563

(4.442) (-1.449) (-1.256)

Dividend Payout -0.3675*** -0.1651 -0.0855

(-3.585) (-0.895) (-0.607)

Technology -8.8599** -8.8025

(-2.038) (-1.115)

Market Returns -1.7162** 1.3571** -0.5044**

(-8.503) (2.339) (-2.337)

Ex-ante -3.7309 16.0768

(-0.303) (1.357)

Underwriter Reputation -10.6093 2.0360

(-1.049) (0.245)

Dilution Factor 0.9944** 1.9418**

(2.039) (2.153)

Adj. R-Square 0.8223 0.6461 0.2355 0.1383

F-Statistic 13.8867*** 5.932*** 1.428 1.079

Prob(F-Statistic) 0.001 0.006 0.189 0.3893

N 15 20 65 88

t-statistics using White(1980) heteroscedastic standard errors are within parentheses. ***significant at

the 1% level, **significant at the 5% level and *significant at the 10% level.

131

In the multiples and fair value estimates bias valuation analysis, Table 4.7

reports that the coefficient of offer size is positive and statistically significant at the

95% level of confidence. This implies that smaller size firms produce large valuation

bias when valued by multiples valuation approach. The coefficient of technology is

negative and statistically significant at the 95% level of confidence, revealing that

technology firms produce more valuation biases when valued using multiples

valuation technique. The coefficient of prior market returns appears to be positive and

strongly significant at the 95% level of confidence. The findings conjecture that the

firms offered in non-crisis effect periods, bullish market fashions, produce more

valuation bias than the one offered in bearish market fashions. The coefficient of

dilution factor appears to be positive and strongly significant at the 95% level of

confidence. The results show that the more shares offered in the IPO produce more

biased valuations when followed by the multiples valuation method. The results are

consistent with the findings of Roosenboom (2012). The results document that the

higher market returns before the IPO show less biased valuations. This implies that

the lead underwriters choose higher multiples firms’ to capitalize the impact of bullish

sentiments in the fair-value estimates.

Table 4.7 presents the results of bias valuation methods estimated using the

closing prices on the first trading day as market values. This study also estimates the

results of bias valuation using IPO offer prices as market value. The results are

reported in the Appendix Table A.5. The findings of the bias DDM valuation method

is same as reported in Table 4.7 while the results of the bias DCF valuation method

are different as reported in Table 4.7. The findings show that the operating

profitability and property, plant & equipments are key drivers to produce biased

valuations when followed by the DCF method. The results report that higher predicted

sales growth firms show less biased valuations, higher pre-IPO aggregate stock

market returns show more biased valuations and prestigious underwriters show less

biased valuations when followed by the multiples valuation method.

4.1.3 Explaining the Accuracy of Valuation Methods

In this part, this study addresses the third research objective “To investigate

the firm-specific characteristics and market-related factors that had influenced the

valuation accuracy associated with each valuation method”. First, the Wilcoxon Sign

132

Rank test and standard t-statistics on medians and mean values of absolute prediction

errors of each valuation method are used to estimate the valuation accuracy of each

valuation method. Second, to explain the value relevance of pre-IPO value estimates,

perform a Wald-test to examine the joint hypothesis that the intercept and slope are

equal to zero and one respectively. If the pre-IPO value estimates are the unbiased

predictor of post-IPO market prices then the joint hypothesis of intercept and slope

should be zero and one respectively. At last, the analysis of cross-sectional regression

models for each valuation method is performed to investigate the firm-specific

characteristics and market factors that had influenced the valuation accuracy.

4.1.3.1 Analysis of Absolute Prediction Errors

In this part, the descriptive statistics of absolute prediction errors of each

valuation method are discussed. The absolute prediction errors of each valuation

method is computed as |(Estimated valuei,preIPO – Market Valuei) /Market Valuei|. The

absolute prediction errors are the absolute values of signed prediction errors as used in

4.1.2.1 subsection. Kim and Ritter (1999), Francis et al., (2000), Berkman, Bradbury

& Ferguson (2000), Deloof, Maeseneire & Inghelbrecht (2002), Cassia, Paleari &

Vismara (2004), Schreiner and Spremann (2007), Deloof et al., (2009), Demirakos,

Strong & Walker (2010), Roosenboom (2012) and Goh, Rasli, Dziekonski & Khan

(2015) used first day closing prices to estimate the valuation accuracy of each

valuation method. An extant literature conjectures that the absolute prediction errors

capture the valuation accuracy associated with each valuation technique and these

errors are also used in the cross-sectional value relevance regressions as a dependent

variable.

Table 4. 8: Analysis of Absolute Prediction Errors (at 1st Day Closing Prices)

Valuation Method Absolute Prediction Errors Percentage

within 15% of

actual estimates

Obs.

Mean Median

Dividend Discount Model (%) 28.442** 24.218** 30.769 15

Discounted Cash Flow (%) 20.279** 16.813** 45.000 20

Multiples (%) 29.880*** 23.940*** 35.385 65

P/E Ratio (%) 34.277*** 27.207*** 25.000 36

P/B Ratio (%) 27.156*** 23.940*** 41.176 51

Fair Value Estimates (%) 28.121*** 22.459*** 36.364 88


133

Table 4.8 presents the mean, median and percentage of valuation errors within

15% or less of actual estimates of absolute prediction errors of each valuation method

by undertaking the first trading day closing price of IPO firms as market values. In

this analysis, the Wilcoxon Sign Rank test and standard t-statistics on medians and

mean values different from zero of absolute prediction errors respectively are used to

estimate the valuation accuracy associated with each valuation method. According to

existing literature, an absolute valuation prediction errors approach is the appropriate

measure to capture the valuation accuracy of each method by estimating the degree of

central tendency and as the percentage of observations with absolute prediction errors

within 15% or less. The findings reveal that the mean absolute errors of valuation

methods are between 20.28% and 34.28%. The results document that the t-statistics of

mean absolute errors statistically different from zero for DCF estimates are

significantly smaller (20.28% of the sample) and the degree of central tendency of

percentage within 15% is highest (45.00%) than the mean absolute errors of other

methods. This implies that the valuation accuracy of DCF is highest. The results of

DCF highest valuation accuracy are consistent with Deloof et al., (2009, 2002) and

Berkman, Bradbury & Ferguson (2000). This study also observed that the mean

absolute errors for P/E ratio estimates are significantly larger (34.28% of the sample)

and the degree of central tendency of percentage within 15% is lowest (25.00%) than

the mean absolute errors of other methods. This implies that the valuation accuracy of

P/E is smallest. The results of P/E lowest valuation accuracy are consistent with Goh,

Rasli, Dziekonski & Khan (2015), Deloof et al., (2009, 2002), Cassia, Paleari &

Vismara (2004), Berkman, Bradbury & Ferguson (2000), and Kim & Ritter (1999). In

sum, the mean absolute errors are range from 20.28% for the DCF method to 34.28%

for the P/E ratio multiples. The valuation accuracy of fair value estimates is equal to

28.12%.

Table 4. 9: Analysis of Absolute Prediction Errors (at IPO Offer Prices)

Valuation Method Absolute Prediction Errors Percentage

within 15% of

actual estimates

Obs.

Mean Median

Dividend Discount Model (%) 44.011** 28.947** 7.692 15

Discounted Cash Flow (%) 12.639** 0.000** 65.000 20

Multiples (%) 46.349*** 31.920*** 23.077 65

P/E Ratio (%) 47.467*** 31.460*** 16.667 36

P/B Ratio (%) 42.770*** 30.600*** 27.451 51

Fair Value Estimates (%) 37.189*** 25.932*** 31.818 88


134

Table 4.9 presents the mean, median and percentage of valuation errors within

15% or less of actual estimates of absolute prediction errors of each valuation method

by undertaking the IPO offer prices as market values. The section estimate the degree

of central tendency of pre-IPO value estimates at IPO offer prices and defined as the

percentage of observations with absolute prediction errors within 15% or less. The

findings reveal that the mean absolute errors of valuation methods are between

12.64% and 47.47%. The results of mean absolute errors estimated through the IPO

offer prices are similar to the findings of mean absolute errors estimated through the

first day closing price of IPO firms. The findings show that the mean absolute errors

are ranging from 12.64% for the DCF method to 47.47% for the P/E ratio multiples.

The valuation accuracy of fair value estimates is equal to 37.19%

These findings raise the question that why do lead underwriters intentionally

overvalue the fair value estimates by using P/E and P/B multiples instead of using

DCF valuation method. This may be one of the reasons that the lead underwriters

involve in both to estimate fair value estimate and set preliminary offer prices in many

cases and deliberately offer a large discount to the investors. The findings conjecture

that the lead underwriters may give opportunities to the investors to impatiently

participate in the subsequent offers in the future.

4.1.3.3 The Analysis of Value Relevancy of each Valuation Model

This study examines the predicting power of pre-IPO value estimates of each

valuation method to explain the cross-sectional variation in the market values. To

explain this value relevancy, this study employed the Wald-test to examine the joint

hypothesis that the intercept equals to zero and the slope coefficient is equal to one. If

the valuation models give unbiased estimates then the intercept should be equals zero

and slope coefficient equals one. In the value relevancy models, the natural logarithm

of Market Value of IPO firms is used as the dependent variable and the natural

logarithm of Estimated Value disclosed in the prospectuses for each valuation model

as the independent variable. In Table 4.10, the t-statistics estimated through White

(1980) heteroscedastic standard errors are reported in the parentheses and test whether

the intercept term is different from zero and slope coefficient is different from one or

not.

135

Table 4. 10: The Analysis of Value Relevancy Through Regressions

Indep. Variable Parameter Adj. R2

(%) N Wald test

Intercept Slop

Dividend Discount Model 0.0354 0.9856** 73.00 15 0.0710

(0.102) (4.317)

Discounted Cash Flow 0.2869 0.7846*** 47.94 20 1.2122

(1.535) (5.670)

Multiples Valuation 0.0423 0.9561*** 83.25 65 0.7378

(0.619) (18.985) P/E Ratio

0.0992 0.9346*** 72.60 36 0.3521 (0.741) (11.534)

P/B Ratio 0.1014 0.9084*** 86.41 51 2.5404*

(1.624) (19.965)

Fair Value Estimate 0.1047 0.9168*** 81.58 88 1.5652

(1.570) (18.990) ***significance at 1% level, **significance at 5% level, *significance at 10% level

Table 4.10 documents the finding of Wald-test and the explanatory power for

each valuation method. On the investigation of ability to explain the predicting power

of each valuation method, the study tests the hypothesis that the slope coefficient

equals one. The findings reveal that the slope coefficient is different from one. The

results report that the multiples (importantly P/B measure) valuation method has

highest predicting power and the discounted cash flow method has lowest predicting

power to market values. These findings are consistent with Cassia, Paleari & Vismara

(2004), and Kim and Ritter (1999). On the other side, Wald-statistic findings reject

the joint hypothesis of the intercept that equals zero and slope coefficient equals one

for each valuation method. The findings of Wald-test of joint hypothesis, none of the

valuation method produces unbiased estimates of market value. The findings of Wald-

statistic are consistent with the Roosenboom (2012).

This study also conduct the value relevancy regression analysis at IPO offer

prices used as market values. Table A.6 (see appendix) reports the finding of the

explanatory power of each valuation method and Wald-statistic to test whether the

intercept term and slope coefficients are different from zero and one respectively. The

findings are similar as observed in the Table 4.10.

136

4.1.3.3 Cross-Sectional Analysis of Accuracy of Valuation Methods

In this part, this study investigates the firm-specific characteristics and market

factors that had influenced the valuation accuracy of each valuation method used

during the IPO valuation process. In this analysis, the absolute prediction errors are

used as dependent variables with respect to the valuation method employed by

underwriters and t-statistics are estimated through White (1980) heteroscedastic

standard errors. To limit our analysis, only three most popular techniques are used for

cross-sectional regression analysis.

Table 4.11 presents the results of the cross-sectional regression of valuation

accuracy of the dividend discount model, discounted cash flow, multiples valuation

and fair value estimates methods. In the dividend discounted model (DDM) valuation

accuracy analysis, Table 4.11 reports that the coefficient of firm size appears to be

negative and strongly significant at the 95% level of confidence. The finding reveals

that the large firms produce high valuation accuracy when followed by the DDM

method. The results are comparable with Beatty and Ritter (1986) who argue that the

larger IPO firms can be easily estimated through direct valuation models. The results

document that the firms having large investments as fixed show high valuation

accuracy. This implies that the DDM seems to be an appropriate value estimator for

capital-intensive firms. The results document that the higher market returns and large

market volatility before the IPO show less valuation accuracy. These findings are

consistent with Roosenboom (2012) accuracy of DDM valuation analysis.

In the discounted cash flow (DCF) valuation accuracy analysis, Table 4.11

reports that the coefficient of firm size appears to be negative but statistically

insignificant related to the accuracy of DCF method. The results show that the larger

firms produce high valuation accuracy as compared with Beatty and Ritter (1986).

The results document that the firms having large investments as fixed show less

valuation accuracy. This implies that the DCF seems to be an inappropriate value

estimator for capital-intensive firms. The coefficient of operating profitability before

the IPO year is negative and strongly significant at the 95% level of confidence. This

indicates that the higher profitable firms produce less valuation accuracy. The

coefficient of predicted sales growth is found to be positive but statistically

137

insignificant. This implies that the high sales growth IPOs followed by DCF produce

modest valuation accuracy.

Table 4. 11: Cross-sectional Regressions of Accuracy of Valuation Methods

Model APE_DDM APE_DCF APE_Multiples APE_Fair Value

Independent Variables (1) (2) (3) (4)

Intercept 140.1700** 104.2097 63.1419*** -2.9339

(2.699) (1.361) (2.820) (-0.051)

Total Assets -12.5947** -15.548 -4.4939* 3.6154

(-2.481) (-1.742) (-2.019) (0.634)

Firm Age 1.1136 6.6711

(0.123) (0.870)

Property, plant & Equip. -0.0755 0.7089** -0.0866 -0.0305

(-0.344) (2.333) (-0.703) (-0.349)

Operating Profitability -0.2313** -0.107** -0.0766*

(-2.224) (-2.579) (-1.907)

Sales Growth 0.0171 -0.1009** -0.0516*

(0.383) (-2.147) (-1.729)

Dividend Payout -0.1216 -0.113

(-0.861) (-0.929)

Technology 6.894 0.36167

(0.693) (0.063)

Market Returns -1.1312*** 0.1657 -0.1496

(-5.382) (1.101) (-0.976)

Ex-ante 6.9779** -10.1482

(2.604) (-1.677)

Underwriter Reputation 5.6632 2.4605

(0.850) (0.373)

Dilution Factor 0.1221 0.4086

(0.538) (1.153)

Adj. R-Square 0.7422 0.4467 0.2119 0.11183

F-Statistic 7.679 4.624 1.247 0.869

Prob(F-Statistic) 0.009 0.008 0.281 0.5727

N 15 20 65 88



In the multiples and fair value estimates valuation accuracy analysis, Table

4.11 reports that the coefficient of offer size appears to be negative and significant at

the 90% level of confidence. The coefficient of predicted sales growth in the IPO year

appears to be negative and strongly significant at the 95% level of confidence. The

results show that the firms with a positive trend in sales growth produce more

valuation accuracy when followed by the multiples valuation method. The results are

138

consistent with the findings of Roosenboom (2012) and Demirakos et al., (2010). The

results document that the higher operating profitability predicted sales growth and

large volatility in the market returns before the IPO show high valuation accuracy

when followed by the fair value estimates. The coefficient of predicted operating

profitability appears to be negative and statistically significant at the 95% level of

confidence. This implies that the firms more operating profitable before the IPO are

accurately valued during pricing process. The coefficient of ex-ante risk is found to be

positive and statistically significant at the 95% level of confidence. This implies that

the more aggregate stock market volatility before the IPO produce less valuation

accuracy of firms followed by multiples valuation technique.

Table A.7 (see appendix) presents the results of valuation accuracy of each

valuation method using IPO offer prices as market values and these results are

estimated for robustness. The findings of the accuracy of the DDM valuation method

is same as reported in the Table 4.11 while the results of valuation accuracy of the

DCF method reveals that operating profitability and AIP are additional significant

factors that impact the intensity of valuation accuracy of DCF. The findings show that

the large firms, higher sales growth and bullish market sentiments increase the

valuation accuracy of the multiples valuation approach.

4.2 The IPOs Initial Prices (Valuation) Analysis

In this section, the data is used for various valuation and aftermarket

performance models, and the results are discussed to unfold the research questions of

“whether the prospectus information is useful to predict the IPO offer prices, initial

returns and long-run returns?” As documented in figure 4.2 of post-IPO performance

analysis, the preliminary offer prices are related to prospectus information. As

discussed in the literature review chapter, the prospectus information is classified into

three groups: the fundamental, signaling and the ex-ante risk factors. In this section,

the basic valuation models have been used as set out in equation 9 and equation 10.

The graphical overview of this section is presented in figure 4.2.

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Figure 4. 2: Aftermarket IPO Valuation and Performance Analysis

Figure 4.2 presents the graphical overview of short and long-run performance

determinants, and irregularities involved in aftermarket performance such as short-run

underpricing and long-run underperformance anomalies. During the pricing process,

the underwriters deliberately offer discount in preliminary offer prices to attract more

participation in the bidding auction. In Pakistan, only high net-worth individuals

(HNWIs) and institutions are allowed to participate in the bookbuilding auction while

general investors can subscribe after the completion of bookbuilding auction process.

The prospectus information plays the vital role in the aftermarket performance

because the prospectus document contains the information of the shareholding

structure of the IPO company, the purpose of IPO proceeds, proposed investment

plans, future prospects, company history, details of trailing financial statements and

valuation methods used to estimate fair value estimates. The prospect information is

divided into three groups namely as fundamental, signaling and ex-ante risk factors.

The key objective of this study is investigate the impact of prospectus information on

the IPOs aftermarket performance and also irregularities that prevail in the primary

market.

140

4.2.1 Descriptive Statistics

The descriptive statistics of fundamental, signaling and ex-ante risk factors

used as a proxy for prospectus information are reported in the Table 4.12. As already

discussed earlier, data has been obtained from the PSX DataStream and prospectuses

(published at the time of formal listing in the exchange) of the IPO firms.

Table 4. 12: Descriptive Statistics of Variables used in Performance Models


Max Std.

Dev N

25th 50th 75th

Offer Price (PKR) 26.67 10.00 10.00 14.00 30.00 235.00 32.20 86

Book Value (PKR) 20.63 6.82 10.29 13.67 24.02 124.50 19.34 86

Earnings Per Share (PKR) 3.14 -4.43 0.12 1.24 4.53 26.51 5.12 86

D (Dummy for Negative Earnings) 0.21 0.00 - - - 1.00 0.41 86

Dividend Per Share (PKR) 1.02 0.00 0.00 0.00 1.50 10.00 1.81 86

Financial Leverage (%) 49.41 1.40 32.51 49.65 67.80 96.60 24.34 86

Capital Availability Risk (%) 81.60 12.50 66.80 100.00 100.00 100.00 26.86 86

Efficiency Risk (%) 68.08 0.07 52.07 74.52 85.68 248.03 32.64 86

Capacity Risk (%) 61.20 0.00 24.34 70.17 100.00 100.00 40.47 86

Firm Beta (%) 6.71 0.26 1.27 3.61 6.89 50.60 9.55 86

Offer Size (Millions PKR) 992.40 40.00 150.00 330.80 1,150.00 1,2161.25 1,677.23 86

Underwriter Reputation (Dummy) 0.66 0.00 - - - 1.00 0.48 86

Firm Age (Years) 15.23 1.30 3.50 8.00 19.00 78.00 17.79 86

Portion of Shares Offered (%) 23.28 2.50 14.77 25.00 27.73 50.00 10.81 86

Table 4.12 presents the descriptive statistics of IPO firm’s characteristics and

market data used in the valuation and performance models to estimate the impact of

prospectus information on aftermarket IPOs performance. The financial variables data

such as earnings per share, book value per share, retained earnings, dividends payout,

total liabilities, total assets, net sales, cost of goods sold, proposed investment plan,

firm age and the portion of shares offered are taken from the latest financial

statements available in the prospectus documents, while IPOs offer prices, gross IPO

proceeds, market returns and ex-ante uncertainty are taken from the official website of

Pakistan Stock Exchange. In this part, this study excludes two more firms on the basis

of incomplete information about concerned factors.

The descriptive statistics of IPO offer prices and book values that on average

the IPO sample takes a value of 26.67 and 20.63 respectively. The descriptive

statistics of forecasted earnings before the IPO report that on average the IPO sample

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takes a value of 3.14. This study also used a dummy variable for negative earnings

reported before the IPO. This study observes about 20.0% IPO firms have reported

negative earnings. Rees (1999) argues that the inclusion of negative earnings dummy

in the valuation model is to discriminate the effect of loss building firms on the IPO

pricing. This implies that most of the firms in the sample are young firms. The

average value of proposed dividends at the time of IPO takes a low value. This study

observes that about 41.0% firms (36 out of 86 IPOs) offered cash dividends to its

shareholders. This implies that the sample of this study contains both large and small

firms while many large firms have the track record of paying dividends before IPO

and release the projected dividends in the prospectus and, on the other side, many

small firms did not offer any payout before IPO and not promise for any payout in

near future.

Table 4.12 reports the descriptive statistics of Financial Leverage described as

the ratio of total liabilities to total assets in the recent financial statements disclosed in

the prospectuses. The average (median) financial leverage of IPO sample is equal to

49.41% (49.65%) which is greater than the US, UK and China countries. This implies

that the excessive use of debt financing increases its financial leverage in terms of

high-interest payments, which negatively impact on firms’ core earnings. According

to Loughran & McDonald (2013) and Thomas H.T. (2011) pre-IPO higher financial

leverage is a proxy for ex-ante risk, which deflates equity values. Modiliani & Miller

(1966) argue that higher leverage increases the level of insolvency. Further, we look

at the Capital Availability Risk defined as the ratio of retained earnings over net

income using latest year financial statements data disclosed in the prospectuses. The

availability of internal capital is very important to capitalize the growth opportunities

because most IPO firms are young. The average (median) capital availability risk of

IPO sample is equal to 81.6% (100.0%). This indicates that a large portion of net

income retained in the firm means higher capital availability and lower financial risk.

Table 4.12 also reports the descriptive statistics of four other non-financial risk

factors such as efficiency risk, capacity risk, firms’ beta and IPO gross proceeds. The

efficiency risk is defined as the cost of goods sold over the net sales and used as a

proxy for production efficiency. The average (median) efficiency risk is equal to

68.08% (74.52%). This implies that the high operating efficiency risk means higher

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cost of goods sold and less production efficiency. As we already discussed earlier that

it is mandatory for issuing firms to disclose the purpose of IPO proceeds and their

proposed investment plans in the prospectus documents. The Capacity Risk is defined

as the ratio of the proposed investment plan over IPO proceeds disclosed in the

prospectus. If the large portions of IPO proceeds are allocated for investment

activities tend to large uncertainty of the returns indicate the higher capacity risk. In

the IPO sample, on average 61.2% of IPO proceeds are proposed for investment

purposes. Our results indicate that the higher proportion of utilization plan over IPO

proceeds leads higher capacity risk. Leone et al. (2003) and Espenlaub et al., (1999)

find the positive relation between the impact of IPO proceeds on under-pricing or

aftermarket performance. Beaver et al,. (1970) first time in the history argues that the

firm beta is an important factor to evaluate the riskiness of the firm’s equity value.

Firm beta is a proxy for price volatility and measured by the standard deviation of

IPO aftermarket prices for 180 trading days from the date of formal listing on the

market. The sample mean of firm beta is 6.7% and the median is 3.61%. This implies

that the higher value of firm beta indicates negative aftermarket performance. The

Offer Size is broadly used as a proxy for the level of risk of the IPO firms. Bessler and

Thies (2007) and, Agarwal, Liu and Rhee (2008) find that offer size is a significant

determinant of aftermarket performance. Sohail & nasr (2007) and Loughran & Ritter

(2002) conjecture that the offer size is negatively linked with the firm valuation. The

offer size is measured as the product of the number of shares offered in an IPO and

the offer price. The average (median) offer size is equal to 992.4 million (330.8

million) and the standard deviation is 12,161.25 million. The findings indicate that the

approximately 70% firms offered capital is less than the average offered capital and

support the evidence of most IPO firms are young.

The Underwriter Reputation is a first signaling variable in the model. Baron

(1982) enlightens the significant role of underwriter reputation in determining the

subscription and allocation of shares. Dominique et al., (2013) finding reveals that the

selection of prestigious underwriters in the IPO process leave less money on the table

because they offered shares at high prices. In this study, underwriter reputation is

measured by how many times the underwriter participated in the IPO process. This

variable takes a value of 1 for prestigious underwriters and 0 for less reputed

underwriters. In our sample, 66% firms (57 out of 86 IPOs) are sponsored by

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prestigious underwriters. Based on an extant literature and aforementioned reputation

measure, most IPO firms went public in their early business cycle years. It is

conjectured that the Firm Age reflects the level of maturity and size gained in the

product market. Ritter (1999) argues that it is difficult to predict expected cash flows

and payouts (dividends) of younger firms without establishing their trail records and

their value estimated based on their expected future growth rates. The findings report

that the youngest firm in the study is 15 months old and the oldest firm is 78 years

old. The average (median) firm age is 15 years (8 years). The percentile results show

that the majority firms are young as most firms fall under 15 years. The Percentage of

Shares offered during IPO is a signaling factor in the valuation model. Beatty and

Ritter (1986) describe the phenomenon that smaller offering firms are on average

more speculative (higher uncertainty) than their larger offering firms. The descriptive

statistics of the percentage of shares offered show that on average; IPO firms offered

23% of the total outstanding shares to the public.

4.2.2 The Univariate Analysis

The key purpose of the univariate analysis is to explain the association

between the variables used in the valuation and aftermarket performance models.

Table 4.13 demonstrates the coefficients of correlation (Pearson) between variables

used in the valuation models with initial offer prices while t-statistics are reported

below the each correlation coefficient. In this section, the study only focused are the

discussion of correlations between the independent variables (fundamental, ex-ante

risk and signaling factors) and the dependent variable of IPO offer prices.

The univariate analysis of fundamental factors reports expected correlations.

The finding reveals that the correlation coefficient between the predicted earnings and

initial offer prices appears to be positive and strongly significant at the 95% level of

confidence. The coefficient value (0.2204) shows a moderate association between the

predicted earnings and initial offer prices. The correlation coefficient of dividends

payout is positively linked with initial offer prices and strongly significant at the 95%

level of confidence. The coefficient value (0.2409) demonstrates a modest association

between the dividends disclosed in the prospectuses and the initial offer prices. The

predicted earnings and proposed dividends disclosed in the offering documents

strongly contribute in the valuation process.

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Table 4. 13: Correlation Matrix of Variables used in Valuation and Aftermarket Performance Models

OP/BV EPS/BV D DPS/BV Fin Lev Captl Rsk Eff Rsk Cpcty Rsk Firm Beta Offr Size Und Rep Firm Age

EPS/BV 0.2204

2.0708**

D -0.0725 -0.6196

-0.6661 -7.2364***

DPS/BV 0.2409 0.6323 -0.2951

2.2748** 7.4803*** -2.8302**

Fin Lev -0.0852 -0.1727 0.0941 -0.0456

-0.7837 -1.6067 0.8663 -0.4186

Captl Rsk -0.0748 -0.4702 0.3546 -0.7734 0.1168

-0.6879 -4.8825*** 3.4762** -11.1807*** 1.0779

Eff Rsk -0.0329 -0.3776 0.3400 -0.2404 0.1133 0.2305

-0.3020 -3.7379** 3.3137** -2.2698** 1.0447 2.1711**

Cpcty Rsk -0.2030 -0.1769 0.2881 -0.1931 -0.2188 0.1995 0.0550

-2.9003* -1.6472 2.7577** -2.8036* -2.0552** 2.8661* 0.5052

Firm Beta 0.4803 0.5503 -0.1642 0.5039 0.0710 -0.2234 -0.1392 -0.1715

5.0187*** 6.0401*** -1.5261 5.3471*** 0.6526 -2.1006** -1.2888 -1.5963

Offr Size 0.3891 0.2458 -0.1373 0.2984 0.1287 -0.1663 -0.0925 -0.2759 0.3183

3.8709** 2.3238** -1.2704 2.8649** 1.1891 -1.5453 -0.8517 -2.6308** 3.0773**

Und Rep 0.0427 -0.0101 0.0042 -0.0573 -0.1493 -0.0096 0.0953 0.1567 -0.0473 0.0164

0.3918 -0.0923 0.0387 -0.5259 -1.3836 -0.0875 0.8783 1.4547 -0.4346 0.1509

Firm Age 0.1849 0.2565 -0.3211 0.1985 0.0972 -0.2785 -0.0355 -0.4436 0.2804 0.4713 -0.0406

2.7240* 2.4320** -3.1072** 1.8562 0.8949 -2.6578** -0.3261 -4.5371*** 2.6781** 4.8981*** -0.3724

POS -0.0952 -0.1699 0.2778 -0.2157 -0.1592 0.1443 0.1260 0.3531 -0.0495 -0.3406 0.1886 -0.3141

-0.8762 -1.5798 2.6499** -2.0245** -1.4783 1.3373 1.1641 3.4595** -0.4547 -3.3210** 2.7601* -3.0319**


145

The findings reveal that the association between the ex-ante risk factors and

the offer prices has expected correlations. The correlation coefficient between the

financial leverage and the offer prices appears to be negative. This indicates that the

higher financial leverage has a negative impact on the offer prices as consistent with

the existing theoretical and empirical literature. The correlation coefficient of capital

availability risk is negatively linked to offer prices but statistically insignificant. The

association between the efficiency risk and the initial offer prices appears to be

negative but statistically insignificant. The correlation coefficient of capacity risk

appears to be negative relate to offer prices and significant at the 90% level of

confidence. This implies that the firms allocate more proceeds as long-term

investments produce large capacity risk tend to price lower in the pricing decision

process. The correlation coefficient of firm beta found to be positive and significant at

the 90% level of confidence to initial offer prices. The findings of firm beta are

contradicted with the theoretical literature because firms having large volatility priced

are lower in the market. The association between the offer size and the offer prices

appears to be positive and statistically significant at the 90% level of confidence. The

univariate analysis of the offer size with initial offer prices is contradicted by the

findings of Sohail & Nasr (2007) and Loughran & Ritter (2002).

The findings of signaling factors are consistent with the literature and

proposed hypotheses. The correlation coefficient of underwriter reputation is

positively related to initial offer prices. The coefficient of firm age appears to be

positive and statistically significant at the 90% level of confidence to the initial offer

prices. This indicates that the level of maturity in the product market creates

additional value in the initial offer prices decision process. The association between

the percentage of shares offered in the primary market and the initial prices appear to

be negative. This implies that the initial shareholders retain a large part of their initial

shareholdings and offer smaller fractions to the market which produce a positive

signal. These findings are consistent with an extant literature.

In sum, Table 4.13 of correlation matrix shows mixed findings of each

predictor with initial offer prices. The findings of fundamental factors link to initial

offer prices are correlated. From the risk factor analysis, only financial leverage,

capital availability risk, efficiency risk and capacity risk are correlated to the initial

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offer prices. From the signaling factors, all predictors (underwriter reputation, firm

age and the percentage of shares sold) are consistent factors as expected to be.

However, the discussion of univariate analysis is not intended to test the proposed

hypotheses related to initial offer prices.

4.2.3 Accounting Based Valuation Models Analysis

In this part, the multivariate analysis is employed to address the research

objective of “to investigate the usefulness of prospectus information to price the IPOs

in the ex-ante pricing decision process”. The prospectus information is classified into

three clusters as fundamental, ex-ante risk and the signaling factors.

McCarthy (1999) argues that the lead underwriters used accounting

information such as book value, dividends and earnings to set the preliminary IPO

offer prices. In the literature review chapter, this study documents that the

fundamentals such as earnings, dividends and book values are key driving forces for

equity valuation. Equation (9) presents the basic valuation model for the offer and

first-day closing prices. Table 4.14 presents the findings of basic valuation model for

initial offer prices and first trading day closing prices. The findings are divided into

two panels, Panel A presents the finding by using full IPO sample while Panel B

presents the finding by using non-privatization IPOs sample because the existing

studies document that the state-owned firms are somehow valued differently.

Table 4. 14: Empirical Findings of Basic Valuation Models

Panel-A: Full IPO Sample

Variable Model 1 (OP/BV) Model 2 (FDCP/BV)

Intercept (BV/BV) 1.2416*** 1.5454***

(12.0243) (11.8874)

Earnings (EPS/BV) 0.8279 1.7577**

(1.5493) (2.1102)

D 0.2866** 0.5065**

(2.3324) ( 2.1038)

Dividends (DPS/BV) 2.6327** 5.0129**

(3.1175) (3.1955)

Adj. R2 0.12805 0.24559

F-Statistic 3.96523** 8.79001***

Wald-test 144.58510*** 141.31150***

N 86 86

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Panel-B: Non-Privatization IPO Sample


Intercept (BV/BV) 1.2519*** 1.5656***

(12.4276) (11.6504)

Earnings (EPS/BV) 0.6294 1.1885

(1.2050) (1.3655)

D 0.2129** 0.3261

(2.7512) (1.8281)

Dividends (DPS/BV) 2.5193** 4.795801**

(2.2605) (2.3273)

Adj. R2 0.11477 0.19455

F-Statistic 3.11188** 5.79727**

Wald-test 154.44560*** 135.73330***

N 78 78



Table 4.14 presents the results of basic valuation models and t-statistics have

been estimated through White (1980) heteroscedastic standard errors in both panels.

The first column of each panelist the accounting variables, model 1 and model 2

exhibits the regression coefficients using initial offer prices and initial market prices

scaled by book value as dependent variables respectively.

Model 1 demonstrates the findings of basic valuation model using initial offer

prices scaled by book value as the dependent variable. The coefficient of intercept

appears to be positive and strongly significant at the 99% level of confidence to the

initial offer prices. The intercept coefficient indicates the impact of book value on

initial offer prices and confirms the H11a hypothesis that proposed a positive

relationship between the initial offer prices and book values. This indicates that the

book value has a positive impact on the pricing valuation decision and the findings are

consistent with Firth (1998). The regression coefficient of earnings scaled by book

value appears to be positive but statistically insignificant to the initial offer prices.

The finding reveals that the lead underwriters and issuers put a greater weight to

earnings before the IPO when valuing IPOs. As already discussed in the literature

review chapter, Klein (1996) and Beatty et al., (2002) used trailing earnings disclosed

in the prospectuses while Kim & Ritter (1999) and How & Yeo (2001) used

forecasted earnings disclosed in the prospectuses, the empirical findings reveal similar

conclusions that pre-IPO earnings (predicted earnings) have a larger predicting power

to the initial offer (market) prices. According to signaling theory, good quality firms

148

produce signals to disclose its true value in the market. When lead underwriters

decide to reveal this information means it separated the good firms from bad ones.

The findings of this study report the robust relationship between the initial offer prices

and the earnings disclosed in the prospectuses. The coefficient of negative

earnings dummy appears to be positive and strongly significant at the 99% level of

confidence. The negative earnings dummy variable weighting parameter to firms

reporting losses in the prospectus and these findings are similar to trailing earnings

results. The results indicate that, for loss-making firms before the IPOs, the lead

underwriters put more attention to the book value of equity when valuing IPOs. The

findings also support the working hypothesis of H11c that proposed a positive

relationship between the negative earnings dummy and initial offer price. The

coefficient of dividends disclosed in the offering documents appears to be positive

and statistically significant at the 95% level of confidence. The lead underwriters

considered dividend information as a positive signal about future cash flows and a key

driving force of valuation model. The empirical findings reveal that the dividends

disclosed in the prospectuses have positive and significant effect on the preliminary

offer prices.

The value of adjusted-R squared of the fundamentals valuation model looks to

have a less but statistically significant explanatory power to initial offer prices. This

implies that the book value of equity, earnings and dividends have the significant role

in the valuation process. The findings of the Wald test support the evidence of

fundamentals valuation model findings that document the model validity and the

robust joint impact of fundamentals on the IPO offer prices.

Third column of Table 4.14 demonstrates the findings of basic valuation

model related to initial market prices. The findings reveal that all variables of

fundamentals are positive and statistically significant. Their regression coefficients

are slightly higher than the regression coefficients of model 1. The predicting power

of accounting information of model 2 is greater than the predicting power of model 1.

The finding of Wald test is similar to initial offer prices valuation model, which report

the validity of the model that proposed the positive impact of fundamentals on the

initial market prices. In Panel B of Table 4.14, report the empirical findings of basic

valuation model of non-privatization 78 IPOs. The findings unveil that the

coefficients of non-privatization IPOs are slightly lower than the full IPO sample. The

149

statistical results of non-privatization IPOs are similar for both the initial offer prices

and the initial market prices to the earlier full IPO sample results. This implies that the

non-privatization IPOs are priced similarly as in the private IPOs.

This study also estimate the impact of underwriting contract with offerer about

the unsold equity during IPO implementation process. In Pakistan, for fixed price

auction, according to rule 4 of the Companies (Issue of Capital) Rules, 1996, it is

mandatory for underwriters to assign a underwriting contract about the commitment

of fully subscription in cash under the specified period of time but SECP has only

relaxed this clause to privatization IPOs (state-owned enterprises) due to the large

offer size. For book building auction, according to Clause 5 of appendix 2 of the

Listing of companies and securities regulations of KSE, the offer price should be

determined through book building process and the offer size should be underwritten

by book runner of the said IPO. The shares allocated for general public should be

underwritten under Clause 6 of appendix 2 of the Listing Companies and Securities

Regulations of KSE and rule 4(iii) of Companies (Issue of Capital) Rules, 1996. The

findings of this analysis are unable to explore the significance role of underwriting

contract during initial valuation process. This analysis is performed using full sample

data as well as for non-privatized IPOs sample but the findings are consistent in both

cases (see Appendix Table A.8).

One of the key objective of this study is to investigate the impact of

fundamental, risk and signaling factors to IPO valuation. Table 4.14 only analyzes the

explanatory power of the fundamental factors in the valuation model. However, there

are few other factors such as ex-ante risk and signaling factors that explain the

variation in the initial offer and initial market prices.

Table 4.15 and Table A.9 (see appendix) report the findings of the cross-

sectional analysis of valuation models using full IPO sample, both on the initial IPO

offer prices scaled by book value of equity and the initial market prices (1st day

closing prices) scaled by book value of equity respectively. Model 1 includes

fundamental, ex-ante risk and signaling factors while model 2 also includes the

privatization dummy variable to examine the impact of privatizations on initial prices.

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Table 4. 15: Cross-sectional Analysis of Valuation Models using Full Sample

Variable Model 1 (OP/BV) Model 2 (OP/BV)

Fundamental Factors

Intercept (BV/BV) -2.0982 -2.7758

(-0.9129) (-1.1751)


(0.9950) (0.8403)

D 0.4120* 0.3539

(2.9413) (1.5418)

Dividends (DPS/BV) -3.8275** -3.5840**

(-2.9787) (-2.1882)

Risk Factors

Financial Leverage -0.0113* -0.0111*

(-2.9028) (-2.9069)

Capital Availability Risk -0.0027 -0.0036

(-0.7809) (-1.0305)

Efficiency Risk 0.0009 0.0001

(0.1738) (-0.0040)

Capacity Risk -0.0089** -0.0096**

(-2.1816) ( -2.2898)

Firm Beta 0.0632*** 0.0673**

(3.5315) (4.2925)

Offer Size 0.4137* 0.5205**

(2.6727) (2.9574)

Signal Factors Underwriter Reputation 0.1719 -0.0062

(0.5431) (-0.0177)

Firm Age -0.0195** -0.0183**

(-2.1366) (-2.0376)

Percentage of Shares Offered -0.0225 -0.0228

(-1.3430) (-1.3588)

Privatization - -0.9284*

(-2.7478)

Adj. R-square 0.34345 0.368765

F-Statistic 3.13868*** 3.190606***

N 86 86



The findings of fundamental factors discussed in Table 4.14 are similar as

investigated in Table 4.15. However, the explanatory power of full sample models

(discussed in Table 4.15 and Table A.9) improves significantly when more variables

are added in the models. The regression coefficients have mixed sign while their

magnitudes differ by the addition of more independent variables. The coefficients of

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the book value of equity and dividends are contrary to the basic valuation model

analysis.

As discussed in the methodology framework chapter, the proposed hypotheses

related to ex-ante risk factors are developed on the basis of risk-aversion assumption.

It is conjectured that the initial offer prices are diminishing function of risk factors.

Therefore, IPO firms are priced lower at the initial offer prices because investors

demand compensations for riskier investments.

In the Table 4.15, Model 1 shows the impact of ex-ante risk factors on the

initial offer prices scaled by book value of equity. The coefficient of financial

leverage appears to be negative and statistically significant at the 90% level of

confidence. This finding is consistent with Fama and French (1998), who investigate

an inverse relationship between the financial leverage and the firm valuation. This

implies that the excessive use of debt financing increases their debt paying ability due

to high-interest payments, which negatively impact on IPOs prices. According to

Loughran & McDonald (2013) and Thomas (2011) pre-IPO higher financial leverage

as a proxy for ex-ante risk, increases the deflation of equity valuation. Modiliani &

Miller (1966) argue that higher leverage increases the level of insolvency. The results

are consistent with the proposed H12a hypothesis that the financial leverage is

negatively related to IPO prices. The findings of financial leverage are similar by the

inclusion of privatization dummy in model 2. Table A.9 (see appendix) presents the

findings of valuation model by taking first day closing prices scaled by book value of

equity as a dependent variable. The coefficient of financial leverage retrieved from

Table A.9 is not in line with the proposed hypothesis of H12a. The capital availability

is the next risk factor. Fama and French (1998) argue that the IPO firms follow the

pecking order theory as IPOs prefer to finance their investments first with the internal

resources such as retained earnings, then with the external resources such as debt and

issuing equity. Therefore, the greater the internal capital availability considered as

beneficial and lower business risk. The coefficient of capital availability risk appears

to be negative but statistically insignificant. This implies that lower capital availability

indicates higher business risk and this finding is contras to the proposed H12b

hypothesis that a large portion of net income retained in the firm means higher capital

availability and lower financial risk. The univariate analyses presented in Table 4.13

also support the findings of capital availability risk. As a robustness measure, in Table

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A.9, the coefficient of capital availability risk is found to be positive but statistically

insignificant to the initial market prices.

In this study, the efficiency risk and capacity risk factors are viewed as non-

financial risk factors. The efficiency factor estimates the operational efficiency of

IPOs which hypothesized that more efficient firms generate more profit for the

shareholders and are priced higher during the IPO pricing process. The efficiency risk

factor is estimated as the ratio of cost of goods/services sold (CGS) over total revenue

from the latest financial statements disclosed in the prospectuses. The coefficient of

efficiency risk is found to be positive but statistically insignificant. This implies that

the less operating efficient firms are priced higher during IPO transactions. This

finding contradicts with the proposed hypothesis of H12c that the less operating

efficient firms have the negative impact on the initial prices. The correlation matrix

(see Table 4.13) also reports the negative and insignificant association between the

efficiency risk and the initial offer prices. As a result, no multicollinearity issue

detected about efficiency factor. The results of efficiency risk (reported in Table A.9)

on initial market prices are also contradicted with the proposed hypothesized

relationship between the operating efficiency and the initial valuations.

The capacity risk illustrates the risk associated with issuing firm decision of

the proposed investment plan of IPO proceeds. The coefficient of capacity risk

appears to be negative and strongly significant at the 95% level of confidence. This

indicates that the issuing firms proposale of a larger part of gross IPO proceeds to be

utilized in investment activities produce more risk. As a result, firms having a larger

fraction of IPO proceeds as investment priced lower by market participants.

Therefore, the capacity risk factor is negatively linked with prices, which in turn,

supports the proposed hypothesis of H12d. Keasey and Short (1997) investigate a

positive relationship between the IPO market prices and the IPO proceeds. They

unveil that the utilization of IPO proceeds disclosed in the prospectus are viewed as a

signal to the IPO valuation. The correlation matrix (see Table 4.13) also produces the

negative association between the capacity risk and the initial offer prices.

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Table 4. 16: Cross-sectional Analysis of Valuation Models using non-PIPO Sample


Fundametal Factors

Intercept (BV/BV) -3.0814 -2.7660

(-1.2791) (-1.0908)


(1.2412) (1.2894)

D 0.5534** 0.8663**

(2.2383) (2.3533)

Dividends (DPS/BV) -4.0636 -10.9249**

(-1.3649) (-2.0941)

Risk Factors Financial Leverage -0.0103* -0.0015

(-2.1671) (-0.2469)

Capital Availability Risk -0.0055 -0.0185*

(-0.6577) (-2.3864)

Efficiency Risk 0.0032 0.0040

(0.7670) (1.5463)

Capacity Risk -0.0092** -0.0016

(-2.0839) (-0.2735)

Firm Beta 0.0836*** 0.1556**

(5.1690) (4.2234)

Offer Size 0.5637* 0.5740*

(2.9512) (2.9395)

Signal Factors

Underwriter Reputation 0.0126 -0.0880

(0.0335) (-0.2189)

Firm Age -0.0177 -0.0130

(-1.5299) (-1.1275)


(-1.3697) (-1.6820)

Adj. R-square 0.38769 0.48605

F-Statistic 3.37696*** 4.96503***

N 78 78



The firm beta is used as a proxy of price volatility before the IPO and

measured as the standard deviation of IPO aftermarket prices for 180 trading days

from the date of formal listing on the market. The firms having larger volatility are

riskier and consequently, valued lower by the lead underwriters. The coefficient of

firm beta is found to be positive and statistically significant at the 90% level of

confidence. This implies that the finding of this risk factor is contradicted with the

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working hypothesis of H12e. The coefficient of firm beta with the initial market

prices is found to be positive but statistically insignificant. This finding also

contradicts with the proposed hypothesis. The last risk factor is offer size which is

measured through the product of the number of shares offered and the IPO offer

prices. The offer size is generally used as a proxy for the level of risk of the IPO firms

(Aggarwal, Liu and Rhee, 2008: Bessler and Thies, 2007). If firms offere a larger

portion of their paidup capital in the IPOs is considered to be more riskier and priced

lower by the lead underwriters. The coefficient of offer size is found to be positive

and statistically significant at the 90% level of confidence. This implies that the firms

offered more capital seems to be priced higher. This finding contradicts with the prior

literature and doesn’t support the proposed hypothesis of H12f. Table 4.15 shows

mixed results to the relationship between the ex-ante risk factors and the initial offer

prices. Therefore, it has been concluded that the pre-IPO risk factors have small but

weak significant influence on the initial IPO valuations.

Regardless of numerous methods used to estimate the lead underwriter

reputation, existing literature summarized that the prestigious underwriters reduce the

uncertainty of the IPO firms that are priced higher at the IPO pricing decision and

lower the initial excess returns in the early days. In this study, the prestigious

underwriter reputation is measured as when it undertook more than 6 IPOs in the

sample. The underwriter reputation as a dummy variable is employed in the valuation

model which gets 1 for the prestigious underwriters and 0 for the less reputed

underwriters. Therefore, the investors are willing to pay higher prices that IPOs are

sponsored by the prestigious underwriters. Yung (2011) argues that prestigious

underwriters should have an advantage in information production because of a large

network with high net-worth institutions and/or individuals resulted in greater price

revision in the auction. The coefficient of underwriter reputation is found to be

positive but statistically insignificant to the initial offer prices. This implies that the

IPOs underwritten by prestigious underwriters are priced higher on the initial offer

prices. In this analysis, the firm age is used as another signaling variable. The existing

literature conjectures that the mature firms (older firms) have less uncertainty because

they contain the consistent market share in the product market. The coefficient of firm

age appears to be negative and statistically significant at the 95% level of confidence.

This implies that mature firms are priced lower at the IPO listing. This finding is

155

inconsistent with the extant literature and proposed hypothesis of H13b. However, the

correlation matrix (see Table 4.13) reports a positive association of firm age to the

initial offer prices. The key signaling factor of this valuation model is the percentage

of shares offered in the IPO. The percentage of shares offered in the IPO signals the

dissatisfaction of the initial shareholders on the firm’s future prospects. Based on the

theoretical and empirical literature, it is conjectured that the percentage of shares

offered is positively linked to the initial valuation as proposed in the H13c hypothesis.

The coefficient of the percentage of shares offered is found to be negative and

statistically insignificant. This implies that the lead underwriters priced higher the

IPOs that offered smaller fractions of outstanding shares. The findings of this analysis

is completely consistent with the proposed hypothesis of H13c and also can be

verified from the univariate analysis. But the coefficient of the percentage of shares

offered with the initial market prices (reported in Table A.9) is found to be positive

but statistically insignificant. This finding is contrary to the working hypothesis

related to initial market prices.

In Table 4.15 and Table A.9 (see appendix), the privatization dummy variable

is used to control the impact of privatization on the valuation model because

Dewenter et al., (1998) argue that the state-owned IPO firms are priced differently

than other private IPO firms. However, the overall results without privatization

dummy model show similar results as presented in the model 2. The privatization

dummy variable model (model 2) overall slightly improves the prediction power from

34.35% to 36.88%. The coefficient of privatization dummy is found to be negative

and significant at the 90% level of confidence.. A sensitivity analysis has been

undertaken in the Table 4.16 using non-privatization IPOs sample. The findings of the

first model of Table 4.16 are similar as presented in the first model of Table 4.15. In

the second model of Table 4.16, all results are in line with earlier findings presented

in the Table A.9 while negative earnings dummy and the capital availability risk

factors are other significant determinants.

4.3 The IPOs Initial Excess Returns Analysis

The empirical findings of IPO initial excess returns (IER) also known as IPO

underpricing have been discussed in literature. As discussed in the research

methodology chapter, the initial excess returns can be estimated as the rate of return

156

earned by market participants on 1st trading day closing or a week or a month closing

prices. This IER analysis attempts to deal with the research objective “To investigate

the usefulness of prospectus information on the IPOs short-run excess returns”. The

IER analysis also investigates the underpricing phenomenon which results in

abnormal returns in early trading days as discussed in the literature. An empirical

model has been employed to investigate the effect of prospectus information,

privatization dummy and initial valuation residuals on the IER.

Similar to the initial valuation analysis (section 4.2), the data of 86 IPOs from

prospectus information (fundamental, risk and signaling factors) have been extracted

from the offering documents. Furthermore, the standardized residual series extracted

from the valuation analysis is added to the IER analysis as a proxy for the

unobservable determinants of the IPO prices.

4.3.1 Descriptive Statistics of IER

The summary statistics of initial excess returns, fundamentals and residual

series extracted from the valuation model are reported in the Table 4.17. As already

discussed in the earlier the share prices and financial variables data of IPO firms has

been obtained from the PSX DataStream and the prospectuses respectively.

Table 4. 17: Descriptive Statistics of Initial Excess Returns Analysis



Initial Excess Returns (IER %) 32.85*** -35.76 0.00 13.84 37.10 322.0 59.88 86

Book Value (BV/OP) 1.02 0.18 0.67 0.95 1.14 6.42 0.73 86

Earnings Per Share (EPS/OP) 0.09 -0.44 0.01 0.08 0.16 0.55 0.15 86

Residuals (Resi_Val) 0.00 -3.71 -0.43 0.03 0.58 3.85 1.08 86

***Significant at 1% level

Table 4.17 presents the average (median) underpricing of IPO sample is equal

to 32.85% (13.84%) which is greater than the US, UK and other developed countries

(Loughran and Ritter, 2003; Ljungqvist, 2009; Bodnaruk, et al., 2008; Lee, et al.,

2012). However, these IER are lesser relative to China, Jordan, India and Sri Lank

(Yu and Tse, 2006; Marmar, 2010; Shelly and Singh, 2008; Peter, 2007). The finding

implies that the excessive returns highlighting the underpricing anomaly exist in PSX

market. This indicates that the initial participants who buy shares in primary market

157

and sell them on first trading day earn abnormal returns by 32.85% from their

investments. It has been observed from the data that the most IPOs are underpriced

(57 out of 86 IPOs), 17 are overpriced where 12 are priced accurately.

To examine the association between the size of IPO firms and IER on the first

trading day, The scholar categorized the sample into four groups based on market

capitalization of IPOs at the offer prices. PKR 600 million, PKR 2,100 million and

PKR 6,500 millions are taken as cut-offs closet to 1st, 2nd and 3rd quartiles

respectively. The cutoff market capitalization for small group firms is less than PKR

2,100 million.

Table 4. 18: Descriptive Statistics of IERs in different Issue Proceeds

IPO Proceeds N %age of IPOs Mean Median Min Max SD

≤ 600 mn 23 26.74 39.46** 8.45 -20.00 322.00 72.80

> 600 and < 2,100 mn 20 23.256 34.09** 18.70 -22.40 228.00 228.00

> 2,101 and < 6,500 mn 22 25.581 37.39** 16.14 -35.76 270.74 66.68

≥ 6,500 mn 21 24.419 19.64** 13.70 -15.03 99.91 29.60

Small IPO Firms 43 50.00 36.96*** 10.00 -22.40 322.00 67.10

Big IPO Firms 43 50.00 28.73*** 13.98 -35.76 270.74 52.16

***Significant at 1%, **Significant at 5% and *Significant at 10%

Table 4.18 presents the descriptive statistics of IER related to different size

based IPO groups. The average initial excess returns for different size groups are

significantly different from zero. The IER decrease as the size of firm is increases.

The IPOs that have highest market capitalization (greater than 6,500) produce of

19.64% IER, while firms with lowest market capitalization (less than 600 million)

produce an IER 39.46%. These findings are in sequence with the assumption of ex-

ante uncertainty hypothesis. Table 4.18 also reports the initial excess returns of small

IPO firms (market capitalization below 2,100 million used as the breakpoint) and big

IPO firms separately. The empirical findings highlight that the average IER (36.96%)

of small firms is greater than the average IER (28.73%) of big firms. These findings

point out that the IER are indirect by proportional to the size of issuing firms.

To investigate the impact of the IPOs issued in Hot and Cold time periods and

if they are significantly linked with the IER on the 1st trading day, The periodic cycles

of high volume (number of IPOs) with high IERs are referred as “Hot Issue” market

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while relatively low volume with lower initial excess returns are referred as “Cold

Issue” market. According to Signaling Theory (Allen and Faulhaber, 1989; Jegadeesh

et al., 1993; Nanda and Yun, 1997), only high-quality firms went public during hot

issue market and deliberately offer the discount in terms of large underpricing to pass

a signal of high-quality firms. Colak and Gunay (2011) inspected that the good quality

firms intentionally wait and decide to go public when they make sure that the market

is hot. According to Risk Composition Hypothesis, Ritter (1984) argued that the more

risky firms went public when they observe hot issue market phenomenon and initial

returns are leading. Peterle and Berk (2016) inspected that generally riskier firms

prefer to issue IPOs during a “hot issue” market when initial excess returns are higher.

This study follows the statistical technique for identification of cyclical

patterns of “hot- and cold-issue” markets as followed by the Ritter (1984), Ghosh

(2004) and Agathee et al., (2012a) as a positive correlation between the number of

IPOs offered and IERs on early days. Figure 4.3 displays the IPOs listed and IER per

year in PSX from 2000 to 2016. Based on the IPO activity and initial returns

displayed in figure 4.3, periods from 2000-2003 and 2008-2013 could be viewed as

cold issue market while periods from 2004-2007 and 2014-2016 could be viewed as

hot issue markets

Figure 4. 3: Yearly Listed IPOs and IER in Hot-Cold Issue Market

32

43

8

14

2

109

3

6

43

1

5

7

4

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

0

2

4

6

8

10

12

14

16

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

IPOs IER %

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Table 4. 19: Descriptive Statistics of IERs in Hot- & Cold-issue Periods

No. of years N %age of IPOs Mean Median Min Max SD

Hot Issue Periods

2014-2016 3 16 18.60 10.89 13.84 -20.00 35.43 17.96

2004-2007 4 33 38.37 59.46 36.20 -22.40 322.0 74.91

Cold Issue Periods

2008-2013 6 26 30.23 16.54 0.12 -35.76 228.0 52.47

2000-2003 4 11 12.79 23.50 8.45 -18.00 83.00 35.55

Hot IPOs 7 49 56.98 43.59*** 23.98 -22.40 322.0 66.12

Cold IPOs 10 37 43.02 18.61** 2.00 -35.76 228.0 47.68

Wilcoxon-Mann-Whitney Rank Test 2.5513**


Table 4.19 presents the findings of IER during “hot- and cold-issue” markets

in the PSX. The findings reveal that the weighted average initial returns (43.59%) in

the “hot-issue” markets are more than the weighted average initial returns (18.61%) in

“cold-issue” markets. The average returns of hot and cold issue markets are

significantly different from zero at 99% and 95% level of confidence respectively.

The Wilcoxon-Mann-Whitney rank test (nonparametric test) is used to examine for

equality medians. The difference between medians is significant at the 95% level of

confidence. The findings conclude hat the average IERs in the “hot-issue” market are

significantly higher than in “cold-issue” market. The findings of the hot-cold issues

analysis are inline with the signaling hypothesis and the changing risk composition

hypothesis (Ritter, 1984). The findings also report that the most IPOs (57% offered in

seven years) and a smaller number of IPOs (43% offered in 10 years) decide to go

public in “hot-issue” and “cold-issue” markets respectively. The empirical findings

are consistent with the existing literature (Agathee et al., 2012a; Moorman, 2010;

Helwege and Liang, 2004).

From the existing literature on IPOs offering mechanism, Sherman (2005)

argues that the institutional investors discourage all other methods than the

bookbuilding because of free rider and winner’s curse issues. The institutional

investors, who are more informed, prefer bookbuilding mechanism because they get

more shares at a better price due to their active participation in the price discovery

process. However, Lee (1999), aggarwal (2002), Ljungqvist (2003), and Jenkinson

160

and Jones (2009) argued that the preference of bookbuilding is improved auction to

reduce the level of underpricing in many countries. Many researchers stimulate the

causes of privatization as budget constraints especially during the economic recession,

the inefficiency of state-owned ventures, high political intervention and deprived

financial disciplines. Farinos et a., (2007), Aussenegg and Jelic (2007), Florio and

Memzoni (2004) and Paudyal et al., (1998) investigate the impact of privatization

IPOs on the initial returns and found that the privatization IPOs (PIPOs) outperform

in the short run because they offer large discount due to large offer size.

Table 4. 20: Descriptive Statistics of IERs of various sub-samples

IPO sub-Sample N %age of IPOs Mean Median Min Max SD

Panel A

Fixed Price 64 74.42 41.23*** 21.20 -35.76 322.00 66.85

Book-Building 22 25.58 8.46** 2.57 -20.00 40.79 16.48

Wilcoxon-Mann-Whitney Rank Test **2.1082

Panel B

Privatization 8 9.30 58.76** 65.08 -20.00 131.00 46.31

Non-Privatization 78 90.70 30.19*** 9.23 -35.76 322.00 60.72

Wilcoxon-Mann-Whitney Rank Test 2.2036**

Panel C

Survivors 78 90.70 34.14*** 13.84 -35.76 322.00 61.56

Non-Survivors 8 9.30 20.20 11.67 -31.30 98.50 40.72

Wilcoxon-Mann-Whitney Rank Test 0.6913 ***Significant at 1%, **Significant at 5% and *Significant at 10%

Table 4.20 presents the descriptive statistics of initial excess returns of firms

that went public through Bookbuilding and Fixed Price mechanism, Privatization and

non-Privatization IPOs, and the firms survive after the listing as compared to non-

surviving firms. Panel A reports the initial excess returns of IPOs issued through fixed

prices and bookbuilding during the sample period in Pakistan. Most of the IPOs (64

out of 86) used fixed price offering mechanism and had underpricing of 41.23%,

while the IPOs offered by the bookbuilding mechanism had underpricing of 8.46%.

The results of this analysis indicate that due to the large participation of institutional

investors are allotted more shares at a better price during the IPO bookbuilding

bidding process. The findings are in sequence with extant literature that bookbuilding

is used to reduce the degree of underpricing. The Wilcoxon-Mann-Whitney rank test

161

(nonparametric test) is used to examine for equality medians. The difference between

medians is significant at the 95% level of confidence. This implies that initial returns

which followed by the fixed price mechanism, are significantly greater than the

followed by bookbuilding mechanism.

Panel B of Table 4.20 presents the summary statistics of IERs of privatization

and non-privatization IPOs. During the 2000-2016 sample period, eight firms went

public due to the privatization process in the Pakistan. The underpricing of

privatization IPOs is higher than the underpricing of non-privatization IPOs. These

returns are significantly different from the zero. The findings of the Wilcoxon-Mann-

Whitney rank test unveil that the privatization IPOs are priced differently than non-

privatization IPOs during ex-ante valuation. The IPO firms that delist from the PSX

during five years from the date of formal listing are known as non-survivor firms.

Panel C of Table 4.20 reports the descriptive statistics of initial returns of survivor

and non-survivor IPOs during the sample period. The underpricing of survivor IPOs is

higher than the underpricing of non-survivor IPOs. However, thier median returns are

not statistically significantly different from each other when Wilcoxon-Mann-

Whitney rank test is employed.

Table 4. 21: Year-wise Initial Excess Returns Analysis

Year No. of IPOs %age of IPOs Initial Returns

2016 4 4.65 0.29

2015 7 8.14 7.00

2014 5 5.81 24.80

2013 1 1.16 0.14

2012 3 3.49 1.50

2011 4 4.65 1.26

2010 6 6.98 1.20

2009 3 3.49 11.10

2008 9 10.47 42.20

2007 10 11.63 71.03

2006 2 2.33 114.75

2005 13 15.12 44.10

2004 8 9.30 56.13

2003 3 3.49 56.48

2002 3 3.49 26.67

2001 2 2.33 1.88

2000 3 3.49 1.78

Total 86 Average Underpricing 32.85

Table 4.21 presents the year-wise initial excess returns also known as

underpricing of IPO firms listed in PSX during 2000-2016. The average underpricing

of IPOs offered during the sample period is 32.85%. During 2004 to 2008 and 2013 to

162

2016, market performance was terrific and touched its peak levels due to high GDP

growth rate, low inflation rate, stable exchange rate and healthy FDI (Sohail and

Raheman, 2010). The large underpricing is observed often in the hot issue phases

while less underpricing is observed in the cold issues. A smaller underpricing during

2000-2002 and 2009-2013 were observed due to the impact of the US internet bubble

crisis and the US subprime mortgage crisis respectively.

Table 4. 22: Sector-wise Initial Excess Returns Analysis

Sector Name No. of IPOs %age of IPOs Initial Returns

Automobile & Electrical Goods 4 4.65 27.37

Cement 5 5.81 16.46

Chemicals 6 6.98 65.88

Commercial Banks 9 10.47 52.59

Engineering 7 8.14 40.94

Fertilizer 2 2.33 12.51

Foods & Allied 2 2.33 -7.90

Insurance & Leasing 1 1.16 0.10

Investment Sec/Banks 12 13.95 38.87

Modaraba 3 3.49 -3.33

Oil & Gas 5 5.81 88.03

Power Gen. & Distribution 7 8.14 20.94

Property & Investment 3 3.49 35.48

Technology & Communication 12 13.95 20.07

Textile 6 6.98 -3.87

Transporation & Communication 2 2.33 50.67

Total 86 Average Underpricing 32.85

Figure 4. 4: Sector-wise Initial Excess Returns Analysis

0

27.3716.46

65.88

52.5940.94

12.51

-7.900.10

38.87

-3.33

88.03

20.94

35.48

20.07

-3.87

50.67

-20

0

20

40

60

80

100

163

Figure 4.4 presents the sector-wise under and overpricing of IPO firms listed in PSX

during 2000-2016. The higher underpricing observed in the Oil & Gas, Chemicals,

Commercial Banks and Transportation & Communication sectors are 88.03%,

65.88%, 52.59% and 50.67% respectively. On the other side, the overpricing is

observed in the Foods & Allied, Textile and Modaraba sectors. However, the firm

listed in Insurance & Leasing sector is accurately placed in the market.


Table 4.23 demonstrates the correlation (Pearson) coefficients of variables

used in the initial excess returns analysis. In this part, one-to-one relationship between

the initial excess returns and each predictor is discussed while t-statistic is reported

below each correlation coefficient.

The univariate analysis of fundamental factors reports expected correlations.

The findings reveal that the correlation coefficient between the book value of equity

scaled by offer prices and the initial excess returns appears to be positive (0.0114).

The second fundamental factor is the earnings-to-price ratio and indicates the

information of shares risk. The correlation matrix reveals that the coefficient between

the earnings-price ratio and IER is found to be positive (0.1085). This indicates that

the lead underwriters used earnings disclosed in the prospectus, producing a signal of

good quality firms, valued higher by the market participants on the 1st trading date.

The correlation matrix of ex-ante risk factors related to initial excess returns

reports expected associations are developed on the basic assumption of a risk-return

hypothesis. The findings reveal that the investment in IPOs is considered as riskier

and investors demand compensations in terms of higher underpricing in early days.

On the other hand, only financial leverage and firm beta are significantly linked with

IERs. The univariate analysis of signaling factors related to initial excess returns

reports expected associations. The finding reveals that the coefficient between the

underwriter reputation and initial offer prices appears to be negative (-0.2022). The

coefficient of firm age is found to be negative (-0.0607). The findings indicate that the

rapidly growing and young firms offered discount in offer prices and resulted in high

underpricing on the first trading day. The correlation coefficient of the percentage of

shares offered in IPOs related to initial excess returns is found to be positive (0.1104).

164

Table 4. 23: Correlation Matrix of Variables used in Initial Excess Returns Analysis

IER BV/OP EPS/OP Fin Lev Captl Rsk Eff Rsk Cpcty Rsk Firm Beta Offer Size Und Rep Firm Age POS Priv

BV/OP 0.0114

0.1049

EPS/OP 0.1085 0.4101

1.0000 4.1218**

Fin Lev 0.1989 0.1563 -0.0697

1.8602* 1.4503 -0.6410

Captl Rsk 0.0556 0.0149 -0.3581 0.1168

0.5106 0.1368 -3.5148** 1.0779

Eff Rsk 0.1731 -0.1607 -0.4302 0.1132 0.2305

1.6109 -1.4929 -4.3686** 1.0447 2.1711**

Cpcty Rsk -0.0088 -0.1519 -0.2128 -0.2188 0.1995 0.0550

-0.0809 -1.4090 -1.9963** -2.0552** 1.8661* 0.5051

Firm Beta 0.3529 -0.2608 0.2284 0.0710 -0.2234 -0.1392 -0.1715

3.4573*** -2.4763** 2.1507** 0.6526 -2.1006** -1.2888 -1.5963

Offer Size -0.0601 -0.2577 0.0587 0.1286 -0.1663 -0.0925 -0.2759 0.3183

-0.5517 -2.4449** 0.5397 1.1891 -1.5453 -0.8517 -2.6308** 3.0773**

Und Rep -0.2022 -0.1629 -0.0885 -0.1493 -0.0095 0.0953 0.1567 -0.0473 0.0164

-1.8919* -1.5137 -0.8150 -1.3836 -0.0875 0.8783 1.4547 -0.4346 0.1509

Firm Age -0.0607 -0.0897 0.1664 0.0972 -0.2785 -0.0355 -0.4436 0.2804 0.4713 -0.0406

-0.5581 -0.8257 1.5474 0.8949 -2.6578** -0.3261 -4.5371*** 2.6782** 4.8981** -0.3724

POS 0.1104 -0.0740 -0.1211 -0.1592 0.1443 0.1260 0.3531 -0.0495 -0.3406 0.1886 -0.3141

1.0187 -0.6806 -1.1182 -1.4783 1.3373 1.1641 3.4595** -0.4547 -3.3211** 1.7601** -3.0319**

Priv 0.1394 0.1815 0.2318 0.1272 -0.3127 -0.2263 -0.3233 0.3058 0.3645 -0.3643 0.3222 -0.2540

1.2902 1.6918** 2.1841** 1.1755 -3.0175** -2.1301** -3.1314** 2.9443** 3.5886** -3.5853** 3.1194** -2.4072**

Resi -0.1168 -0.2939 -0.1975 -0.0003 0.0017 -0.0064 0.0013 -0.0001 -0.0001 0.0001 -0.0001 0.0000 -0.1543

-1.0785 -2.8189** -1.8468** -0.0024 0.0162 -0.0058 0.0011 -0.0008 -0.0006 0.0009 -0.0009 0.0002 -1.4316


165

An extant literature reports the evidence of large underpricing of privatization

(state-owned firms) IPOs. The correlation coefficient between the privatization and

the initial excess returns appears to be positive but insignificant. As discussed earlier,

the standardized residuals are extracted from valuation model is added to capture the

effect of unobservable factors influencing the initial evaluation process. The

correlation coefficient of standardized residuals related to initial excess returns is

found to be negative (-0.1168). The findings imply that IPOs are accurately placed in

the PSX and the market does not misprice them.

In sum, the findings of univariate analysis conclude that IER are shaped by the

investors’ sentiments. The univariate analysis implies that only a few variables such

as financial leverage, underwriter reputation and firm beta are significantly link firm

betad with IERs. This analysis also validates the evidence of privatization IPOs

underpricing.

4.3.3 The Analysis of Initial Excess Returns Models

this section investigates the explanatory power of prospectus information

(fundamental, risk and signaling factors) on the initial excess returns estimated as the

percentage difference between the IPOs offer prices and the first day market values.

Table 4.24 exhibits the OLS findings of two IER models. In model 1, only the

fundamental, ex-ante risk and signaling factors are used as predictor variables.

However, in the second IER model, the standardized residuals factor along with all

previous variables is used as predictor variables. In this analysis, the initial excess

returns (IERs) are used as dependent variables and t-statistics have been estimated

through the Huber/White standard errors. The findings of IER models assist to

investigate the cross-sectional determinants of initial returns. Finally, a sensitivity

analysis of the findings of IER models is also displayed in Table 4.25.

166

Table 4. 24: Regression Analysis of IER Models using Full IPO Sample

Variable Model 1 (IER) Model 2 (IER)

Fundamental Factors

Intercept 0.7489 2.0278

(0.7615) (1.1900)

Book Value (BV/OP) 0.0229 0.0216

(0.3880) (0.3114)

Earnings (EPS/OP) 0.8076* 0.8548

(2.0249) (1.1966)

Risk Factors Financial Leverage 0.0045* 0.0043

(2.1006) (1.1998)

Capital Availability Risk -0.0011 -0.0015

(-1.3442) (-1.6165)

Efficiency Risk 0.0061*** 0.0061**

(4.0253) (2.6704)

Capacity Risk 0.0012 0.0030

(0.6281) (1.2807)

Firm Beta 0.0271** 0.0233*

(2.0812) (2.0975)

Offer Size -0.1535 -0.3478*

(-1.2979) (-2.1763)

Signal Factors

Underwriter Reputation -0.0893* -0.1329*

(-2.06855) (-2.1827)

Firm Age -0.0051 -0.0047

(-1.3355) (-0.7723)


(-0.7852) (-0.1509)

Valuation Residuals - -0.047*

- (-2.7601)

Adj. R-square 0.31609 0.25928

F-Statistic 3.025212** 3.071128**

N 86 86



The intercept coefficient in both IER models is found to be positive that

validate the underpricing phenomenon exist in the PSX as discussed in the descriptive

statistics of IER analysis. The IER models show the diligence of underpricing even

after amending and controlling additional variables in the analysis.

The research methodology chapter document that the higher book value to

offer price (BV/OP), the higher the underpricing relative to the fundamental factors.

167

In addition, Fama and French (1995) discuss that the higher the BTM ratio exhibits

the riskiness of equity firms. Beatty & Ritter (1986) argue that firms having more ex-

ante uncertainty leads to greater underpricing during early trading days. On the basis

of the aforementioned arguments, the proposed hypothesis hypothesizes a positive

relationship between the BV/OP ratio and IPO initial excess returns. Table 4.24

reports that the regression coefficient of the book value of shareholder’s equity scaled

by offer prices related to initial excess returns is positive but insignificant. The

findings of this analysis are consistent with the existing literature, univariate analysis

and support the evidence of the proposed hypothesis of H14a that documented the

direct relationship of BV/OP related to initial returns. The finding of book value

fundamental is also consistent with Beneda and Zhang (2009) but inconsistent with

Mumtaz, Smith & Ahmed (2016), Banerjee, Dai & Shrestha (2011). The findings are

same as reported in the model 2. Although, the association between the book-values

over offer prices and standardized residuals was found to be negative and statistically

significant. The earnings disclosed in offering documents is positively linked with the

aftermarket performance is generally viewed as a signal as discussed earlier. The

regression coefficient of an earnings-price ratio is appeared to be positive and

significant in the first IER model, however, the regression coefficient appears to be

positive but insignificant in the second IER model. How and Yeo (2000) and Firth

(1998) found the positive association between the accuracy of earnings disclosed in

the prospectus documents and degree of underpricing observed in the market. Sohail

(2015) investigates the negative association of earnings related to underpricing of 83

IPOs listed in Pakistan.

As reported in the correlation matrix, the relationship of all ex-ante risk factors

except capital availability risk and capacity risk factors are related to the initial

returns. The regression coefficient of financial leverage is found to be positive and

statistically significant at the 90% level of confidence related to the initial returns.

This implies that the highly leveraged firms considered as risky and produce higher

returns in the market. This finding provides the evidence to validate the proposed

hypothesis of H15a. This finding is in line with Mumtaz, Smith & Ahmed (2016) but

is contradict with Hedge and Miller (1996), who investigate the impact of financial

leverage on IPO valuation and find an inverse relation between the degree of financial

leverage and underpricing. The second financial risk factor is capital availability risk;

168

the regression coefficient between the capital availability and the initial returns

appears to be negative but statistically insignificant in both models. The findings

conjecture that the market participants raise higher demand for firms that hold their

major earnings as retained earnings. The firms with greater capital availability

generate high IER in the primary market.

The regression coefficients of efficiency risk related to initial excess returns

appear to be positive and significant. This finding supports the proposed hypothesis of

H15c. The findings reveal that less operating efficient firms before the IPOs are

considered riskier firms and generate lower initial returns and vice versa. The capacity

risk is estimated through the percentage of proceeds utilization plan over total

proceeds as disclosed in the prospectuses. The coefficient of capacity risk related to

initial excess returns appears to be positive but statistically insignificant. Firms

proposing more funds for investment activities are viewed as riskier IPOs and

generate higher initial excess returns. The correlation matrix also validates the finding

of regression analysis. The findings contradict with Reber & Vencappa (2016) who

investigate the positive association of the uses of IPO proceeds as investment related

to initial excess returns. The firms having higher returns volatility are considered as

riskier firms and consequently, generates excess returns during early trading days.

The coefficient of firm beta is found to be positive and statistically significant at the

95% level of confidence related to IERs. This implies that the finding of this risk

factor is in line with the working hypothesis of H15e. The findings of the correlation

matrix (see Table 4.23) also support the theoretical and empirical literature of firm

beta. The finding of firm’s beta is in line with existing literature (Mumtaz, Smith &

Ahmed, 2016; Afza, Yousaf & Alam, 2013, Banerjee, Dai & Shrestha, 2011; Beneda

& Zhang, 2009; Sohail & Raheman, 2009; Sohail & Nasr, 2007) but is contradicted

by Javid & Malik (2016), Kafayat & Farooqi (2014). The last risk factor is offer size,

which generally used as a proxy for the level of risk of IPO firms (Bessler and Thies,

2007; Aggarwal, Liu and Rhee, 2008). The firms offere large offer size in the IPOs is

considered as riskier and generate higher initial excess returns. The coefficient of

offer size is negative but statistically insignificant. This implies that the firms offered

more capital seems to be priced higher in early trading days. This finding is consistent

with Reber & Vencappa (2016), Javid & Malik (2016), Banerjee, Dai & Shrestha

169

(2011) and Kerins, Kutsuna & Smith (2007). Therefore, it has been concluded that the

pre-IPO risk factors have the influence on the initial excess returns.

The high underwriters prestige dilute the impact of uncertainty of IPOs that

priced higher at the IPO pricing decision and lower the initial excess returns in the

early trading days. Therefore, the investors are intentionally to pay more prices for

IPOs when launched by prestigious underwriters. The coefficient of underwriter

reputation is found to be negative and statistically significant at the 90% level of

confidence related to the initial excess returns. This implies that the IPOs

underwritten by prestigious underwriters are priced higher on the first day of trading

and support the evidence of working hypothesis of H16a. These findings are

marginally consistent with Jenkinson, Jones & Suntheim (2016), Reber & Vencappa

(2016), and Wang & Yung (2011) who argue that prestigious underwriters have more

inside information because of large network with high net-worth institutions and/or

individuals resulting greater price revision in auction and lower price volatility on first

trading day. On the other side, Banerjee, Dai & Shrestha (2011), Lowry, officer &

Schwert (2010) and Kerins, Kutsuna & Smith (2007) found a positive association of

underwriter reputation with initial excess returns. In this analysis, the firm age is used

as another signaling variable. The existing literature conjectures that mature firms

(older firms) have less uncertainty because they contain persistent market share in the

product market. The coefficient of firm age appears to be negative but statistically

insignificant related to the initial returns. Reber & Vencappa (2016) and Lowry,

officer & Schwert (2010) use IPOs data floated in US primary markets and assert

negative association between firm age and short-run underpricing, while in Pakistan,

Afza, Yousaf and Alam (2013) find positive association when they used corporate

governance factor as a mediator between the underpricing and firm-specific

characteristics. The greater % of shares offered in the new offering is considered as a

signal of dissatisfaction of the initial shareholders on the firm’s future prospects.

Based on the theoretical and empirical literature, it is conjectured that the offer size is

positively related to initial valuation as proposed in the H16c hypothesis. The

coefficient of the percentage of shares offered is found to be negative but statistically

insignificantly related to IERs. The finding of shares retention at the time of formal

listing is inline with the existing literature of Pakistan Javid and Malik (2016), Afza,

Yousaf and Alam (2013) and Sohail and Raheman (2009) but contrary with developed

markets (Reber & Vencappa, 2016).

170

In model 2, the standardized residual series extracted from the IPO valuation

model is added to control the unobservable effect of the initial valuation on the initial

returns. The coefficient of residual is found to be negative and statistically significant

at the 90% level of confidence related to initial returns. The finding implies that

higher values of residuals indicate that IPOs are valued higher relative to the IPOs

fundamentals and consequently lower the IERs.

4.3.4 The Sensitivity Analysis

Table 4.25 report the findings of IER models for the non-privatization IPO

sample. On the same pattern as in Table 4.24, the standardized residuals series from

the non-PIPO valuation model is added in model 2. In this section initial excess

returns (IER) of non-PIPOs used as a dependent variable and t-statistics have been

estimated through Huber/White standard errors. The findings of IER models assist to

estimate the cross-sectional determinants of the initial returns.

The intercept value shows a robust finding of underpricing of the non-

privatization IPOs but the underpricing level is lower than the full IPOs sample. The

justification of lower underpricing is that the full sample is included privatization

IPOs as well and PIPO is confirmed to have higher IER as discussed in the descriptive

statistics part (see Table 4.20). The coefficient of the book value of

shareholder’s equity over offer prices is found to be positively related to IERs and this

finding is similar to the main analysis. However, the coefficient earnings-price ratio

appears to be negatively related to IERs but statistically insignificant (found

significant in model 2). These resuls are not consistent with main analysis and

proposed hypothesis of H14b.

As discussed in methodology chapter, the proposed hypotheses related to ex-

ante uncertainty factors are developed on the basis of risk-aversion assumption. It is

conjectured that initial excess returns are the increasing function of risk factors.

Therefore, the IPO firms are priced higher on the 1st trading date because investors

demand compensations for riskier investments. Except the capital availability risk

factor, the coefficients of all ex-ante risk factors are consistent with the proposed

hypotheses related IERs and with main analysis of a non-PIPOs sample. In the model

1, the capital availability risk, efficiency risk, firm beta and offer size factors are

significant determinants. However, in the model 2, the residuals series is added to

171

control the effect of unobservable factors on the IERs whereas the financial leverage,

efficiency risk and offer size are significant determinants related to IERs.

Table 4. 25: Regression Analysis of IER Models using Non-PIPO Sample

Variable Model 1 (IER) Model 2 (IER)

Fundamental Factors

Intercept 0.9078 1.5893*

(0.9698) (1.7057)

Book Value (BV/OP) 0.6105 1.0235**

(1.0790) (2.0436)

Earnings (EPS/OP) -0.0045 -0.3145*

(-0.0381) (-1.9308)

Risk Factors

Financial Leverage 0.0040 0.0051*

(1.6341) (2.1012)

Capital Availability Risk 0.0031* 0.0019

(2.0765) (1.0550)

Efficiency Risk 0.0049** 0.0060***

(3.3124) (3.7052)


(0.4427) (0.5051)

Firm Beta 0.0327* 0.0099

(2.2137) (1.5962)

Offer Size -0.1977* -0.2879*

(-2.7419) (-2.4254)

Signal Factors

Underwriter Reputation -0.1527 -0.1558

(-0.8938) (-1.3821)

Firm Age -0.0016 0.0012

(-0.4479) (0.3720)

Percentage of Shares Offered 0.0052 0.0078

(0.6329) (0.8799)

Valuation Residuals - -0.1720*

- (-2.4921)

Adj. R-square 0.33602 0.33113

F-Statistic 3.03646** 3.16402**

N 78 78

t-statistics using white(1980) heteroscedastic standard errors are within parentheses. ***significant at


Except the percentage of shares offered factor, the findings of signaling factors

remain unchanged. In this analysis, the coefficients of underwriter reputation, firm

age and the percentage of shares offered related to initial excess returns are as

172

expected and provide supporting evidence of proposed hypotheses H16a-H16c. The

coefficient of a residual factor related to initial excess returns appears to be negative

and statistically significant at the 90% level of confidence. The finding implies that

the higher values of residuals indicating that IPOs are valued higher relative to the

IPOs fundamentals and consequently, in turn, lowers the IERs. The finding of

residuals is consistent with the working hypothesis of H17.

4.4 The IPOs Long-run Returns Analysis

This section reports the findings of long-run aftermarket performance of IPOs

floated during 2000-2012. As already discussed in methodology chapter, the long

term performance can be estimated as the adjusted returns of loyal investors who buy

shares on second trading day and keep them over 5 years. This LRR analysis attempts

to address the objective of “To investigate the usefulness of prospectus information on

the IPOs long-run adjusted returns” The LRR analysis investigates the long-run

performance phenomenon which results in a poor performance observed in longer

period as discussed in literature. The long-run aftermarket price performance is

estimated by using event-time approach (the commutative adjusted returns proposed

by Ritter (1991) and the BHAR as proposed by Barber & Lyon (1997)) and calendar-

time approach (the CAPM as proposed by Sharp-Lintner (1964), and three- and five-

factors asset pricing models as proposed by Fama-French (1993,2015)). The long-run

performance estimated through Calendar-time approach has been discussed in Section

4.5. An empirical model has been employed to estimate the effect of prospectus

information, IERs, privatization dummy and valuation residuals on the LRR. The

initial excess returns and the valuation residuals are used as a proxy for ‘mispricing’

on the first trading day.

In the long run analysis part, the sample of 65 IPOs has been used for the LRR

analysis. The inclusion of IPOs based only those firms that survive for more than five

years from the listing date in the stock exchange. There may be a survivorship bias

because new sample dropped the firms under delistings as highlight by (Shunway,

1997). The data of prospectus information such as fundamental, risk and signaling

factors have been extracted from the offering documents. Furthermore, the

standardized residual series is extracted from the valuation analysis and the IERs are

added in the LRR analysis as a proxy for the unobservable determinants of the IPO

prices and ‘mispricing’ experienced on the 1st day of trading respectively.

173

4.4.1 Descriptive Statistics of LRR

This section provides the summary statistics of equal-weighted and value-weighted

LRR up to five years. These long-run returns are estimated using BHARs and CARs

methods. As discussed earlier, the share prices and the financial variables data of IPO

firms have been obtained from the PSX DataStream and the prospectuses

respectively. The month-wise equally-weighted and value-weighted monthly returns

up to 60 months using BHARs and CARs respectively, are reported in Table A.10 and

Figure A.1. The results of value-weighted returns analysis are used as robustness

measure to validate the results estimated through equally-weighted approach.

Table 4. 26: Descriptive Statistics of equally-weighted Long-run Returns

Variables Mean Min Percentiles

Max SD N

25th 50th 75th

BHAR-EW Y1 -0.0771 -0.9148 -0.6603 -0.2184 0.0916 3.3323 0.8043 65

BHAR-EW Y2 -0.1721 -1.6264 -0.8831 -0.3368 0.2763 3.7589 1.0645 65

BHAR-EW Y3 -0.2352 -2.0876 -0.9933 -0.5671 -0.0145 4.1963 1.3782 65

BHAR-EW Y4 -0.3388 -2.7575 -1.3599 -0.7431 -0.2158 12.6947 2.2289 65

BHAR-EW Y5 -0.6522** -4.6458 -1.6259 -0.9507 -0.1985 8.6673 2.0733 65

CAR-EW Y1 -0.1305 -1.2664 -0.6440 -0.1968 0.1896 1.9937 0.6668 65

CAR-EW Y2 -0.1833* -2.0810 -0.8594 -0.2590 0.4044 2.2881 0.8445 65

CAR-EW Y3 -0.2462* -2.4753 -1.0043 -0.3726 0.3457 4.5127 1.1153 65

CAR-EW Y4 -0.3188** -2.2126 -1.0449 -0.6382 0.3171 3.3329 1.1251 65

CAR-EW Y5 -0.2937** -2.2540 -1.2552 -0.2175 0.4433 3.5519 1.1436 65


Table 4. 27: Descriptive Statistics of value-weighted Long-run Returns

Variables Weighted Average Min Percentiles

Max SD N

25th 50th 75th

BHAR-VW Y1 -0.1028 -0.0478 -0.0027 -0.0011 0.0004 0.0199 0.0090 65

BHAR-VW Y2 -0.1745* -0.0413 -0.0043 -0.0018 0.0010 0.0449 0.0121 65

BHAR-VW Y3 -0.4338*** -0.0836 -0.0057 -0.0029 -0.0001 0.0281 0.0185 65

BHAR-VW Y4 -0.3557* -0.1038 -0.0086 -0.0037 -0.0009 0.0838 0.0230 65

BHAR-VW Y5 -0.5843** -0.1908 -0.0104 -0.0040 -0.0009 0.0947 0.0321 65

CAR-VW Y1 -0.0622 -0.0462 -0.0024 -0.0006 0.0013 0.0428 0.0112 65

CAR-VW Y2 -0.0154 -0.0373 -0.0031 -0.0011 0.0027 0.0531 0.0122 65

CAR-VW Y3 -0.2583** -0.0655 -0.0070 -0.0014 0.0018 0.0333 0.0147 65

CAR-VW Y4 -0.2501* -0.0669 -0.0069 -0.0015 0.0013 0.0475 0.0158 65

CAR-VW Y5 -0.3206* -0.1395 -0.0056 -0.0007 0.0014 0.0574 0.0229 65


174

Table 4.26 and Table 4.27 present the descriptive statistics of equally-weighted and

value-weighted long-run returns using BHAR and CAR respectively. Table 4.26

portrays that the average returns using BHAR for year 1 to year 5 are -7.71%, -

17.21%, -23.52%, -33.88% and -65.22% respectively. However, the average returns

using CAR for year 1 to year 5 are -13.05%, -18.33%, -24.62%, -31.88% and -29.37%

respectively. These findings show that the LRR found a negative trend when moving

from year 1 to year 5 in the both BHAR and CAR techniques. However, the results

estimated through the BHARs are more negative than those estimated through the

CAR method. Various researchers Barber and Lyon (1996), Eckbo and Norli (2005)

and Choi, Lee and Megginson (2010) argue that the accuracy of LRRs estimated

through BHAR is more than the accuracy of CAR. These results are in sequence with

some Pakistani authors Sohail and Nasr (2007), Mumtaz and Smith (2015), Javid and

Malik (2016) and Mumtaz, Smith and Ahmed (2016) and with international studies

Loughran and Ritter (1995), Stehle (2000), Kooli & Suret (2004), Chorruk &

Worthington (2010), and Bossin & Sentis (2014). The LRRs estimated over five years

are greater than the other emerging countries (Malaysia, India, Taiwan, and China)

and all developed countries except the South Africa and Switzerland (See Table 2.4-

2.7). In this study, the value-weighted returns are used as a robustness measure for the

equally-weighted returns and Table 4.27 document the value-weighted long-run

underperformance using BHARs and CARs. The findings of value-weighted long-run

performance found a similar trend that is observed in the equally-weighted long-run

returns.

Table 4. 28: Year-wise Long-run Returns Analysis using BHAR and CAR

Year BHAR-EW BHAR-VW CAR-EW CAR-VW

Year 1 -0.0771 -0.1028 -0.1305 -0.0622

Year 2 -0.1721 -0.1745* -0.1833* -0.0154

Year 3 -0.2352 -0.4338*** -0.2462* -0.2583**

Year 4 -0.3388 -0.3557* -0.3188** -0.2501*

Year 5 -0.6522** -0.5843** -0.2937** -0.3206*


175

Figure 4. 5: Year-wise Long-run Returns Analysis using BHAR and CAR

Table 4.28 and Figure 4.5 present the summary view of long-run equal- and value-

weighted returns measured by the BHAR and CAR methods. These findings affirm

the long-run underperformance anomaly exist in Pakistan. The investors who

purchase shares on day 2 and keep them over five years, in turns, negative returns.

Table 4. 29: Year-wise Long-run Returns Analysis using BHAR

Year N BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

2000 3 -0.0925 -0.3068 -0.4362 -1.5848 -3.1674

2001 2 -0.2861 0.7083 1.4276 -0.3587 0.1158

2002 2 0.4224 0.8050 2.7676 7.2426 5.7697

2003 2 0.0499 -0.4312 -0.5035 -0.8528 -0.4154

2004 7 -0.2582 -0.4070 -0.9482 -0.4727 -1.0147

2005 13 -0.1991 0.0324 0.3734 -0.1984 -0.6096

2006 2 -0.5461 -0.6100 -0.3312 -0.6278 -0.8539

2007 10 0.2927 0.1001 -0.3556 -0.5274 -0.3384

2008 9 -0.1961 -0.5392 -0.6619 -0.8810 -1.3411

2009 3 1.1459 -0.1139 -0.2040 0.4460 0.6642

2010 5 -0.5795 -0.6096 -0.9924 -1.6142 -2.0962

2011 4 0.2604 0.6856 0.1644 0.7480 0.1468

2012 3 -0.7867 -1.4282 -1.4957 -1.7483 -1.2479

-0.7000

-0.6000

-0.5000

-0.4000

-0.3000

-0.2000

-0.1000

0.0000

Year 1 Year 2 Year 3 Year 4 Year 5

BHAR-EW

BHAR-VW

CAR-EW

CAR-VW

176

Table 4. 30: Year-wise Long-run Returns Analysis using CAR

Year N CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

2000 3 -0.0953 -0.2155 -0.2885 -0.5187 -1.1058

2001 2 -0.2755 0.4327 0.4542 0.2685 0.4924

2002 2 0.2760 0.3613 0.7250 1.0853 0.9175

2003 2 0.3978 0.0926 0.1084 0.1941 0.1179

2004 7 0.0128 0.0368 -0.1320 -0.0194 -0.2668

2005 13 -0.1366 0.0942 0.4175 0.2404 0.2560

2006 2 -0.7642 -0.6106 -0.8579 -1.0471 -1.4254

2007 10 0.2847 0.0537 -0.5260 -0.9141 -0.2984

2008 9 -0.4661 -0.9118 -1.0979 -1.0627 -1.0896

2009 3 0.0878 -0.0477 -0.1469 -0.0607 -0.2708

2010 5 -0.6015 -0.5657 -0.6408 -0.6837 -1.0211

2011 4 0.1120 0.1469 0.1493 0.3475 0.2581

2012 3 -0.7119 -1.0834 -0.7713 -0.7463 0.0802

Figure 4. 6: Year-wise Long-run Returns using BHAR and CAR

Table 4.29 and Table 4.30 present the LRRs of IPOs listed in PSX during 2000-2012.

In the LRRs analysis using BHAR, the firms went public during 2001 and 2002

outperform the market up to year 3 and year 5. However, the firms went public during

2003 and 2008 show underperformance thorough 3- and 5-year periods. Again, firms

went public in 2009 and 2011 show positive long-run returns. The large

underperformance is observed often in the hot issue phases while over performance is

observed in the cold issues. This implies that the firms outperform the market that

went public after the US internet bubble crisis in 1999-2001 and the US subprime

-4.0000

-3.0000

-2.0000

-1.0000

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

6.0000

7.0000

BHAR1

BHAR3

BHAR5

CAR1

CAR3

CAR5

177

mortgage crisis in 2008-2009 periods. The results of CARs analysis, the IPOs listed

during 2001-2003 outperform the market. However, the IPOs listed during 2004-2009

show underperformance over 3- to 5-year periods. More or less, the pattern of over-

and underperformance is similar to the long-run returns estimated through BHAR.

Table 4. 31: Sector-wise Long-run Returns Analysis using BHAR

Sector Name N BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

Automobile & Elect. 3 -0.5611 -1.1443 -0.7297 -1.5179 -2.1050

Cement 5 -0.0441 0.0914 -0.0356 -0.2082 0.1950

Chemicals 6 -0.1927 -0.5323 -1.0067 -1.4450 -1.8961

Commercial Banks 9 -0.3305 -0.4743 -0.2750 0.5728 -0.1976

Engineering 4 -0.4344 -0.8717 -0.9765 -1.0695 -0.8371

Fertilizer 1 -0.3496 0.4142 0.0038 -0.2158 -0.0531

Foods & Allied 1 1.6601 3.7589 1.9279 3.7598 2.6410

Insurance & Leasing 1 -0.2429 -0.8968 0.0417 -0.5164 -1.6259

Investment Sec/Banks 12 0.1652 -0.1657 0.0748 -0.4106 -1.1435

Modaraba 1 0.1440 2.5865 1.3535 1.5573 4.2396

Oil & Gas 3 -0.0218 -0.0647 -0.3905 0.0581 -0.2582

Power Gen. & Dist. 4 -0.1369 0.1878 -0.1514 1.2111 1.6856

Property & Investment 1 0.1660 -0.2046 -0.5891 -0.9194 -1.0491

Technology & Comm. 6 0.1715 0.3408 0.1242 -0.8700 -1.4548

Textile 6 -0.1742 -0.6016 -0.5300 -0.9710 -1.1948

Transportation & Comm. 2 0.0158 0.0381 0.0385 -0.6742 -0.5265

Table 4. 32: Sector-wise Long-run Returns Analysis using CAR

Sector Name N CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

Automobile & Elect. 3 -0.4813 -0.7756 -0.3882 -0.4932 -0.7972

Cement 5 -0.1323 -0.0684 -0.1857 -0.2942 -0.0975

Chemicals 6 -0.0730 -0.4544 -0.6614 -0.7940 -0.9473

Commercial Banks 9 -0.2901 -0.2698 -0.4167 -0.4977 -0.8801

Engineering 4 -0.3836 -0.7995 -0.9685 -1.1914 -0.5213

Fertilizer 1 -0.2522 0.4738 0.2111 0.1143 0.2092

Foods & Allied 1 0.9787 1.2584 0.8263 1.1800 0.9835

Insurance & Leasing 1 -0.1810 -0.6204 0.2138 0.0546 -0.1622

Investment Sec/Banks 12 -0.0246 -0.3164 -0.2678 -0.3944 -0.0127

Modaraba 1 0.1586 1.6904 0.9451 0.8830 1.6863

Oil & Gas 3 0.3845 0.4696 0.4524 0.5550 0.5514

Power Gen. & Dist. 4 -0.1721 0.1551 -0.0605 0.4499 0.4316

Property & Investment 1 0.2492 -0.1608 -0.6859 -1.5646 -1.5647

Technology & Comm. 6 -0.1748 0.2379 0.1292 0.0097 -0.5317

Textile 6 -0.3250 -0.5539 -0.4009 -0.4395 -0.1759

Transportation & Comm. 2 0.0405 0.0634 -0.0507 -0.2910 -0.3268

178

Figure 4. 7: Sector-wise Long-run returns Analysis using BHAR and CAR

Table 4.31 and Table 4.32 present the sector-wise over- and under-performance of

IPOs listed during 2000-2012. In the long-run returns analysis using BHAR, the

underperformance is observed in all the sectors other than the Cement, Foods &

Allied, Modaraba and Power Generation & Distribution sectors for the year 5. The

highest on average underperformance (-210.5%) is observed in the Automobile &

Electrical Goods sector while on the other side, the highest over-performance

(423.96%) is observed in Modaraba sector, In the LRRs analysis using CAR, the

underperformance is observed in all the sectors other than the firms went public from

the Fertilizer, Foods & Allied, Modaraba, Oil & Gas, and Power Generation &

Distribution sectors. The highest on average underperformance (-156.47%) is

observed in the Real Estate Investment sector while on the other side, the highest

over-performance (168.63%) is observed in the Modaraba sector,

The findings of Table 4.33 helps to investigate the relationships of LRRs using

BHAR & CAR related to privatization and non-privatization IPOs respectively. The 7

out of 65 IPOs went public due to the privatization process in Pakistan during the

sample period.

-3.0000

-2.0000

-1.0000

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

BHAR1

BHAR3

BHAR5

CAR1

CAR3

CAR5

179

Table 4. 33: Privatization and Non-Privatization IPOs Long-run Returns Analysis

Panel A

Sub-Sample N %age of IPOs BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

Privatization 7 10.77 -0.1955 -0.1514 0.1870 1.5377 0.8130

Non-Privatization 58 89.23 -0.0628 -0.1746 -0.2862 -0.5653*** -0.8291***

Wilcoxon-Mann-Whitney Rank Test 0.4127 0.9205 1.1322 2.2961** 2.2114**

Panel B

Sub-Sample N %age of IPOs CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

Privatization 7 10.77 -0.0544 0.1042 0.0015 0.1063 0.0143

Non-Privatization 58 89.23 -0.1397 -0.2180* -0.2761* -0.3701** -0.3308**

Wilcoxon-Mann-Whitney Rank Test 0.6454 1.1957 1.2591 1.3861 1.0263


Figure 4. 8: Privatization and Non-Privatization IPOs Long-run Returns Analysis

Panel A of Table 4.33 presents the LRRs using BHAR of privatization and

non-privatization IPOs that took place during 2000-2012. The privatization IPOs

outperform the market from 3- to 5-year periods while non-privatization IPOs show

underperformance throughout the period. The results reveal that the PIPOs produce

negative returns in short-run periods, but outperform the market in the long term

because the PIPOs are mature enough in the product market than the non-privatization

IPOs. The findings of the Wilcoxon-Mann-Whitney rank test unveil that PIPOs are

priced differently than non PIPOs in early periods. Panel B of Table 4.33 presents the

LRRs using CAR of privatization and non-privatization IPOs that took place during

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000


BHAR-PIPO

BHAR-Non PIPO

CAR-PIPO

CAR-Non PIPO

180

2000-2012. The privatization IPOs outperform the market from 2- to 5-year periods

while non-privatization IPOs show underperformance in all the time periods. These

results are consistent as estimated through the BHAR.

To examine the effect of firm size (market capitalization basis) on the long-run

returns using BHAR and CAR, we categorized the sample IPOs into four types based

on market capitalization at the offer prices. PKR 600 million, PKR 1,800 million and

PKR 3,750 millions are taken as cut-offs closet to the 1st, 2nd & 3rd quartiles

respectively. The cutoff market capitalization for small group firms is less than PKR

1,800 million.

Table 4. 34: Firm’s Size-wise Long-run Returns Analysis using BHAR

IPO Proceeds N BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

< 600 mn 17 -0.1156 0.0391 0.2916 -0.3014 -0.6047

≥ 600 and < 1,800 mn 16 0.1866 -0.2836 -0.4059 -0.9544** -1.5037**

≥ 1,800 and < 3,750 mn 16 -0.2914** -0.3679** -0.2955 0.3474 0.2000

≥ 3,750 mn 16 -0.0855 -0.0893 -0.5639** -0.4493 -0.7036**

Small Firms 33 0.0309 -0.1173 -0.0466 -0.6180** -1.0406**

Big Firms 32 -0.1884* -0.2286 -0.4297** -0.0509 -0.2518



Table 4. 35: Firm’s Size-wise Long-run Returns Analysis using CAR

IPO Proceeds N CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

< 600 mn 17 -0.1520 -0.0170 0.1772 0.1458 0.1215

≥ 600 and < 1,800 mn 16 -0.0185 -0.2832 -0.2790 -0.5067** -0.4554*

≥ 1,800 and < 3,750 mn 16 -0.2409* -0.2628 -0.4031** -0.4370 -0.3530

≥ 3,750 mn 16 -0.1093 -0.1807 -0.5065** -0.5062* -0.5137*

Small Firms 33 -0.0873 -0.1461 -0.0440 -0.1706 -0.1582

Big Firms 32 -0.1751* -0.2217* -0.4548*** -0.4716** -0.4333**



Table 4.34 and Table 4.35 present the long-run returns as categorized on the

basis of market capitalization using BHAR and CAR methods respectively. Table

4.34 reports the long-run returns using BHAR for the period over the year 1 to the

year 5. The findings reveal that the long-run underperformance brings up when

181

increases the firm size over all time periods. In sum, the long-run returns of small size

firms’ are less than the long-run returns of large firms. This implies that smaller size

firms are riskier than the big firms over the longer time horizons. Table 4.35

demonstrates the on average returns using CAR for the period year 1 to the year 5.

The findings report that smaller firms outperform the market over 3- to 5-year

periods. On the other side, medium to large size firms shows underperformance

throughout the time periods. In sum, the long-run underperformances of smaller size

IPOs is less than the underperformance of larger IPOs. It is conclude that smaller size

IPOs are less risky than the large size IPOs and these results are contradicted related

to BHAR results. In both cases, the Wilcoxon-Mann-Whitney rank test is insignificant

for all the time periods.

It is important to discuss IPOs long-run performance is inclined by the degree

of IERs. In general, existing literature argues that the ‘mispricing’ on day 1 may have

the lowest aftermarket performance in the long term. We categorized the IPOs sample

into four groups based on initial excess returns, IER less than 0.00% , 20.00% and

68.00% are taken as cut-offs closet to 1st, 2nd & 3rd quartiles respectively.

Table 4. 36: Initial Returns-wise Long-run Returns Analysis using BHAR

Initial Excess Returns N BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

IER ≤ 00.00 % 18 -0.3100** -0.2756 -0.6100*** -0.7858** -1.2541***

> 0.00 IER < 20.00 % 16 0.1690 -0.0142 0.1418 -0.1582 -0.5950

≥ 20.00 IER < 68.00 % 16 0.1245 0.1572 0.3114 0.3765 0.0485

IER ≥ 68.00 % 15 -0.2750** -0.5676*** -0.7706*** -0.7582*** -0.7384**


Table 4. 37: Initial Returns-wise Long-run Returns Analysis using CAR

Initial Excess Returns N CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

≤ 0.00 % 18 -0.3533*** -0.4282** -0.5711*** -0.4996** -0.6652***

> 0.00 and < 20.00 % 16 -0.0878 -0.1048 0.0717 -0.0688 0.1280

≥ 20.00 and < 68.00 % 16 0.1629 0.2293 0.2307 -0.0078 0.1205

≥ 68.00 % 15 -0.2217 -0.4135** -0.7044*** -0.7001** -0.7393**


Table 4.36 and Table 4.37 present the LRRs as categorized on the basis of

IERs using BHAR and CAR methods respectively. In Table 4.36, this study explores

the long-run performance using BHAR for the period over year 1 to year 5. The

findings reveal that the IPOs that are underpriced and overpriced more in early days

produce high underperformance in longer period. The IPOs underpriced between the

182

20-68% perform better than the large under- and over-priced in the long run. The

results in Table 4.37 of long-run CAR performance related to IERs are consistent but

the magnitudes vary over different time periods.

The findings of Table 4.38 employed to investigate the relationships of LRRs

using BHAR & CAR related to financial and non-financial IPOs respectively. Out of

65 IPOs, 24 ( 37% approx.) firms went public in the financial sectors mainly as

commercial banks and investment/securities companies while 41 (63%) firms were

from the non-financial sectors.

Table 4. 38: Financial and Non-Financial IPOs Long-run Returns Analysis

Panel A

Sub-Sample N %age of IPOs BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

Financial 24 36.92 -0.0385 -0.1988 -0.0322 0.0146 -0.5807

Non-Financial 41 63.08 -0.0996 -0.1565 -0.3541* -0.5457** -0.6942**


Panel B

Sub-Sample N %age of IPOs CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

Financial 24 36.92 -0.1116 -0.2215 -0.2704 -0.4099 -0.3381

Non-Financial 41 63.08 -0.1416 -0.1610 -0.2321* -0.2654* -0.2676*



Figure 4. 9: Long-run Performance Analysis of Financial and Non-Financial IPOs

-0.8000

-0.7000

-0.6000

-0.5000

-0.4000

-0.3000

-0.2000

-0.1000

0.0000

0.1000


BHAR-Fin

BHAR-Nfin

CAR-Fin

CAR-Nfin

183

Panel A of Table 4.38 shows that non-financial IPOs produce more negative

BHAR returns from 1- to 5-year periods than the financial IPOs. The findings of the

Wilcoxon-Mann-Whitney rank test unable to unveil that the financial IPOs did not

perform differently from the non-financial IPOs in the long run. Panel B of Table 4.38

presents that the financial IPOs produce more negative returns from 1- to 5-year

periods than the non-financial IPOs. These results are not in line as estimated through

the BHAR.


Table 4.39 demonstrates the correlation (Pearson) coefficients of variables

used in the long-run performance models. To avoid the complexity, only year 1, year

3 and year 5 long-run returns are added in this analysis. In this part, the one-to-one

relationship of BHAR1Y, BHAR3Y & BHAR5Y related to each predictor is

discussed while t-statistic is reported below each correlation coefficient. The

correlation matrix related to CAR1Y, CAR3Y & CAR5Y with each predictor is

attached in appendices (see Table A.11). From the Table 4.39, it has been observed

that the correlation matrix does not report any multicollinearity problem.

The finding reveals that the correlation coefficient between the book value of

equity scaled by offer prices as well as earnings scaled by offer prices and the BHARs

(only with BHAR3Y and BHAR5Y) appears to be positive and statistically

significant. The earnings coefficient related to BHAR1Y is also found positive.

The correlation coefficient of financial leverage is significantly positive. The

coefficients of capital availability risk are found to be negatively linked with

BHAR1Y, BHAR3Y & BHAR5Y each. The efficiency risk coefficients are linked

with BHARs appear to be negative. The coefficients of capacity risk show mixed and

statistically insignificant results related to BHARs. The coefficient of firm beta is

significantly positive. The coefficient of offer size is found to be negative but

statistically insignificant. The correlation coefficient of underwriter reputation dummy

appears to be negative and significant (only with BHAR1Y) related to BHARs. This

implies that the IPOs sponsored by prestigious underwriters produce negative LRRs.

The firm age coefficient is also found to be negative means the younger firms

outperform in the long run.

184

Table 4. 39: Correlation Matrix of Variables used in LRR Models

Variables

BHAR

1Y

BHAR

3Y

BHAR

5Y

BV/

OP

EPS/

OP

Fin

Lev

Captl

Rsk

Eff

Rsk

Cpcty

Rsk

Firm

Beta

Offer

Size

Und

Rep

Firm

Age POS Priv IERs

BHAR

3Y 0.3211

2.691***

BHAR

5Y 0.0684 0.5757

0.544 5.589***

BV/ OP -0.0225 0.363 0.4276

-0.179 3.092*** 3.755***

EPS/ OP 0.1883 0.514 0.2737 0.4558

1.522 4.757*** 2.258** 4.06**

Fin Lev -0.0552 0.0338 0.2261 0.1802 -0.1018

-0.439 0.269 1.842* 1.454 -0.812

Captl

Rsk -0.0394 -0.0587 -0.0381 -0.0421 -0.3649 0.1305

-0.313 -0.467 -0.302 -0.334 -3.1*** 1.044

Eff Rsk -0.1279 -0.2176 -0.1131 -0.1782 -0.5103 0.1424 0.2498

-1.023 -1.770* -0.903 -1.438 -4.71** 1.142 2.048**

Cpcty

Rsk 0.1148 -0.0639 -0.0935 -0.1860 -0.2778 -0.0848 0.2465 0.1131

0.917 -0.508 -0.746 -1.502 -2.29** -0.675 2.019** 0.903

Firm

Beta 0.2456 -0.0561 -0.0448 -0.2665 0.2367 0.0435 -0.2812

-

0.1811 -0.1484

2.011** -0.446 -0.356 -2.1** 1.933* 0.346 -2.326** -1.462 -1.191

Offer

Size -0.0001 -0.1461 0.0495 -0.213 0.1147 0.1841 -0.1415

-

0.1694 -0.2962 0.3951

-0.001 -1.172 0.393 -1.73* 0.917 1.487 -1.134 -1.364 -2.461** 3.414***

Und Rep -0.2993 -0.1784 -0.1437 -0.142 -0.1174 -0.151 0.0088 0.1069 0.2010 -0.0573 -0.1610

-2.489** -1.439 -1.153 -1.142 -0.939 -1.208 0.070 0.853 1.628 -0.455 -1.294

185

Firm Age -0.0170 -0.1331 -0.0965 -0.068 0.1055 0.1012 -0.2612

-

0.1165 -0.5883 0.3294 0.4889 -0.2087

-0.142 -1.066 -0.770 -0.541 0.842 0.807 -2.148** -0.931 -5.77*** 2.769*** 4.448*** -1.693*

POS 0.0150 -0.0401 -0.1213 -0.082 -0.0662 -0.1792 0.1343 0.1527 0.3836 -0.0558 -0.4135 0.2131 -0.3907

0.119 -0.318 -0.970 -0.654 -0.527 -1.446 1.076 1.226 3.297*** -0.444 -3.60*** 1.731* -3.36***

Priv -0.0515 0.1072 0.2474 0.1752 0.3020 0.2193 -0.3189

-

0.2573 -0.4035 0.3464 0.5779 -0.3241 0.5205

-

0.3198

-0.409 0.856 2.026** 1.412 2.515** 1.784* -2.67*** -2.1** -3.50*** 2.931*** 5.621*** -2.7*** 4.83*** -2.6**

IERs 0.0239 -0.0368 0.0814 -0.024 0.0570 0.1656 0.0323 0.1729 0.0161 0.3544 0.0169 -0.1857 -0.0892 0.1460 0.157

0.190 -0.292 0.648 -0.193 0.453 1.333 0.256 1.393 0.128 3.008*** 0.134 -1.500 -0.711 1.172 1.271

Resi -0.0061 -0.0102 0.0394 -0.278 -0.1890 -0.0734 0.0355 0.0030 0.0480 -0.0144 -0.0314 -0.0105 0.0022 -0.024

-

0.151

-

0.112

-0.048 -0.081 0.313 -2.3** -1.527 -0.584 0.282 0.023 0.382 -0.115 -0.249 -0.083 0.018 -0.198

-

1.217

-

0.898


186

The coefficient of the percentage of shares offered finds to be negatively

linked with BHARs (only with BHAR3Y & BHAR5Y). The privatization IPOs

coefficient is significantly positive meant the state-owned enterprises perform better

in the longer periods. The findings of this analysis is inlinet with the descriptive

statistics of PIPOs (see Table 4.33). The coefficient of IERs and the residuals

extracted from valuation model show mixed and insignificant results.

4.4.3 The Analysis of Long-run Returns (LRR) Models

In this part, this study investigates the explanatory power of prospectus

information (fundamental, risk and signaling factors), IERs and residuals linked with

LRRs estimated through the BHAR and CAR respectively. It is widely discussed in

the descriptive statistical analysis that the long run, up to 5 years from the listing day,

underperformance in Pakistan is in line with the literature of emerging and developed

markets (see Table 2.4-2.7). Table 4.40 exhibits the regression results of BHARs long

run models for the year 1 to the year 5. In these models, BHAR1Y, BHAR2Y,

BHAR3Y, BHAR4Y and BHAR5Y are used as a dependent variable and t-statistics

have been estimated through the Huber/White standard errors. The findings of LRR

models assist to investigate the cross-sectional determinants of LRRs. Finally, a

sensitivity analysis of the findings of LRR models is also reported in the Table 4.41.

The intercept coefficient in LRR models is found to be negative and validate that the

underperformance phenomenon exist in the PSX as discussed in the descriptive

statistics of LRR analysis. The LRR models show the diligence of the

underperformance even after amending and controlling more variables in the analysis.

Table 4.40 reports that the regression coefficient of book value of

shareholder’s equity scaled by offer prices related to BHAR1Y, BHAR2Y and

BHAR3Y are found to be negative and significant, which is not inline with the

proposed hypothesis of H18a. However, in year 4 and year 5, the book value to offer

prices coefficients are found to be positive and statistically significant, which are in

sequence with extant literature and hypothesis of H18a that documented the direct

relationship of BV/OP related to LRRs. Fama and French (1995) find a positive

association between the book value of shareholder’s equity over market values and

aftermarket performance in the long run.

187

Table 4. 40: Regression Analysis of LRR Models using Full Sample

Variable BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

Fundamental Factors

Intercept 1.5978 -2.4392 1.4789 -7.3272 -2.3201

(0.741) (-0.456) (0.415) (-0.947) (-0.286)

Book Value (BV/OP) -0.3538*** -0.8128** -0.0043 1.0413** 0.7456***

(-2.84) (-3.271) (-0.025) (2.164) (3.955)

Earnings (EPS/OP) 1.5733** 4.1749*** 4.4721*** 4.2894** 2.8338**

(2.432) (2.721) (3.569) (2.271) (2.121)

Risk Factors

Financial Leverage -0.0067 -0.0129** -0.0061 -0.0003 -0.0027

(-1.102) (-2.083) (-1.309) (-0.045) (-0.713)

Capital Availability Risk 0.0079* 0.0319** 0.0258** 0.0218* 0.0163

(2.0608) (2.203) (3.469) (2.807) (1.282)

Efficiency Risk -0.0013 -0.0083 -0.0071 -0.0118* -0.0010

(-0.324) (-1.231) (-1.257) (-2.075) (-0.152)

Capacity Risk 0.0063* 0.0065 -0.0017 0.0115 0.0124*

(2.108) (0.956) (-0.276) (1.444) (2.269)

Firm Beta 0.0248** -0.0606** -0.0309** -0.0271** -0.0190*

(2.101) (-2.621) (-2.703) (-2.033) (-2.281)

Offer Size -0.1733 -0.0184 -0.2886 0.8303 0.1505

(-0.696) (-0.029) (-0.741) (0.962) (0.164)

Signal Factors

Underwriter Reputation -1.1688*** -0.0617 -0.3995 -0.1952 0.0973

(-3.89) (-0.101) (-0.834) (-0.305) (0.182)

Firm Age 0.0010 -0.0030 -0.0055 -0.0306 -0.0002

(0.338) (-0.346) (-0.345) (-1.163) (-0.011)

Percentage of Shares Offered 0.0021 -0.0500** -0.0124 -0.0570** -0.0607

(0.129) (-2.091) (-0.628) (-2.069) (-1.608)

Initial Excess Returns -0.0058*** 0.0033 0.0004 0.0089 0.0079*

(-3.31) (0.763) (0.099) (1.574) (2.793)

Residuals -0.2121 -0.2580* 0.0818 0.4302* 0.4109**

(-1.661) (-2.067) (0.581) (2.192) (2.216)

Adj. R-square 0.42465 0.35608 0.46598 0.55270 0.34054

F-Statistic 2.782*** 2.084** 3.288*** 4.657*** 1.946**

N 65 65 65 65 65



Their findings reveal that the book-value over market-value may possibly use

as a proxy for financial risk in return which is linked with firm’s financial distress.

The results of this analysis reveal that the BV/OP is robustly and significantly related

to LRR. The earnings disclosed in prospectus is viewed as a signal of quality firm in

188

the market and extant literature also has similar findings. The regression coefficients

of earnings over offer prices are positive and statistically significant related to year

from BHAR1Y to BHAR5Y. The findings of this analysis are consistent with the

proposed hypothesis of H18b. A sensitivity analysis is reported in Table 4.41 as

excluded the privatization IPOs from the sample based on the literature and

descriptive statistics analysis support as (1) privatization IPOs are priced differently

than the private IPOs (2) aftermarket performance inconsistent than the private IPOs,

and (3) state-owned enterprises hold a major market share in the product market due

to governmental involvement. The findings of Table 4.41 reveal that the regression

coefficients of book value and earnings scaled by offer prices are found to be negative

and positive respectively related to BHAR1Y to BHAR5Y.

In this LRR analysis, the CAR for year 1 to the year 5 also used as dependent

variable to investigate the cross-sectional determinants of LRRs. The findings of LRR

analysis using full sample and non-privatization sample are mentioned in the

appendix (see Table A.12-A.13). The findings of fundamentals (BV/OP and EPS/OP)

are same as estimated in the BHARs long-run returns models. This implies that the

book value of shareholder’s equity no more important in the longer period, however,

earnings disclosed in prospectus is a significant factor.

The proposed hypotheses related to ex-ante risk factors are based on the

positive relationship supposition between the risk and return. The regression

coefficient of financial leverage is negative and statistically insignificant linked with

BHARs for BHAR1Y to BHAR5Y. The finding is in line with Amor and Kooli

(2017) and Hedge and Miller (1996). This study also estimates the determinants of

long-run returns using non-privatization IPOs sample. The findings of financial

leverage related to BHARs are consistent with the findings by using full IPOs sample

(see Table 4.41). As a robustness measure, long-run returns using cumulative

abnormal returns method are used as a depended variable in the long-run models to

probe the impact of cross-sectional determinants on the LRR. The findings of

financial leverage coefficients are inconsistent with the findings of BHARs long-run

models, however, consistent with the related hypothesis (see Table A.12 and Table

A.13). The second financial risk factor is capital availability risk; the regression

coefficients between the capital availability and the long-run models appear to be

positive and statistically significant for year 1 to the year 5. The finding is robust to

189

the BHARs when long-run models were used using non-privatization IPOs sample.

The results of non-privatization IPOs are similar to the full IPOs sample, which are

contradicted to the proposed hypothesis of H19b. The findings conjecture that the

firms which retain less part of their net income as retained earnings generate high

returns in the long term period. The results estimated through CARs models also same

as with the BHARs models.

The regression coefficients of efficiency risk related to BHARs long-run

returns appear to be negative but statistically insignificant. The regression coefficients

extracted from the other BHARs and CARs long run models are similar, which

contradicts to the proposed hypothesis of H19c. The findings reveal that less

operating efficient firms before the IPOs generate high returns and outperform the

market in the long run. The coefficients of capacity risk related to long-run returns

appear to be positive and statistically significant in each BHARs model for year 1 and

year 5. The firms proposed more funds for investment activities are viewed as riskier

IPOs and in turn earn lower long-run returns. The findings are consistent with the

proposed hypothesis of H19d. The findingss of non-privatization IPOs sample are in

line with the full IPOs sample. The CARs long run models also produce similar

results as estimated through BHARs models. Leone et al., (2003) and Klein (1996)

explore the impact of IPO proceeds linked with aftermarket performance and

conclude the value relevance of IPO transaction proceeds information. The findings

reveal that the IPO firms; point of view, higher the utilization plans as investments

means fewer chances that the IPO firm is under their optimal production capacity

reflects lower capacity risk. On the investor view point, a large part of IPO proceeds

assigned as investment means more funds allocated to risky ventures reflects the

positive long-run returns anticipated. Amor and Kooli (2017) investigate the

association between the utilization of IPO proceeds as capital investments and

aftermarket long-run performance of newly listings. They find that the firms, which

invest more in fixed assets produce negative returns in the long run.

The firms with higher returns volatility are considered as riskier firms and

consequently, generate excess returns in the long term. The coefficient of firm beta is

found to be positive and statistically significant only for BHAR1 but appear to be

negative for BHARs for year 2 to year 5. The finding of prior market volatility is in

line with existing studies of Pakistan (Mumtaz, Smith & Ahmed, 2016; Javid &

190

Malik, 2016). The findings from non-privatization IPOs sample are in line with

estimates from the full IPOs sample. This implies that the finding of this risk factor

shows mixed association with different time periods. The last risk factor is offer size,

the coefficient of firm size is negatively related to BHARs for year 1 to the year 3.

However, for year 4 and year 5, BHARs appear to be positive and contradicts with the

theoretical base. The findings are consistent with Javid & Malik (2016) but are

contradicted with Mumtaz, Smith & Ahmed (2016), and Michel, oded & shaked

(2014).

As discussed in methodology chapter about the underwriter reputation, the

decision to hire the prestigious underwriter indicates the signal of a quality firm. The

high reputed underwriters only provide consultancy to the quality IPOs due to

maintain their standing in the financial markets. Amor and Kooli (2017) and Kerins,

Kutsuna & Smith (2007) argue that the IPOs offered through high underwriters

prestige perform well in the long run. The regression coefficient of underwriter

reputation is positive only for BHAR5Y while during year 1 to year 4 appears

negative. The results of BHARs from non-privatization IPOs sample are similarly

estimated through the full sample. The CARs long-run models also show mixed

results. This study conjectures that the underwriter prestige is not a key important

factor of long-run returns. However, it has the significant impact on initial excess

returns.

In this analysis, firm age is used as another signaling variable. The firm age is

generally considered as a proxy for IPO firm’s experience in the industry. It is

expected that older firms have the ability to generate consistent profits. Therefore, due

to the conservatism principle in risk-return assumption, investors put the high demand

of IPO firms’ shares, which results in a higher aftermarket performance. The

coefficient of firm age appears to be positive but only for early years related to the

BHARs. However, the coefficients are negative for BHARs for year 3 to year 5. This

implies that the mature firms produce positive returns for initial years but produce

negative returns are in longer periods. On the other side, the CARs long-run models

produce positive returns for year 2 to year 5, which are consistent with the extant

literature (Mumtaz, Smith & Ahmed, 2016) and proposed hypothesis of H20b.

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Table 4. 41: Regression Analysis of LRR Models using Non-PIPO Sample

Variable BHAR1Y BHAR2Y BHAR3Y BHAR4Y BHAR5Y

Fundamental Factors

Intercept 2.9615 3.3978 5.2120 9.5279 14.6045

(1.178) (0.525) (1.016) (0.827) (1.052)

Book Value (BV/OP) -1.0416*** -0.8899 -0.0753 -1.9248* 0.0432

(-3.11) (-1.589) (-0.182) (-1.761) (0.064)

Earnings (EPS/OP) 1.9110*** 4.2626** 4.5190*** 6.7542** 4.4549**

(2.738) (2.423) (2.876) (2.137) (2.321)

Risk Factors

Financial Leverage -0.5708 -0.0171* -0.0038 -0.0114 -0.0005

(-0.004) (-1.751) (-0.518) (-1.188) (-0.051)

Capital Availability Risk 0.0087 0.0506*** 0.0279*** 0.0671*** 0.0451*

(1.361) (2.867) (3.080) (2.997) (1.882)

Efficiency Risk -0.0045 -0.0184** -0.0043 -0.0216 -0.0127

(-1.314) (-2.131) (-0.723) (-2.106)** (-1.450)

Capacity Risk 0.0046 0.0128* 0.0007 0.0257** 0.0333**

(1.124) (1.693) (0.082) (2.236) (2.558)

Firm Beta 0.0197 -0.0467 -0.0002 -0.0322 -0.0491*

(1.404) (-1.300) (-0.011) (-1.185) (-1.77)

Offer Size -0.3223 -0.8973 -0.7843 -1.8286 -2.1844

(-0.982) (-1.198) (-1.426) (-1.392) (-1.364)

Signal Factors

Underwriter Reputation -1.2539*** 0.2892 -0.4004 -0.1717 0.6955

(-4.14) (0.355) (-0.616) (-0.168) (0.945)

Firm Age 0.0111* 0.0121 -0.0169 -0.0048 0.0533

(1.715) (1.097) (-0.783) (-0.103) (1.395)

Percentage of Shares Offered 0.0093 -0.0497* -0.0078 -0.0709*** -0.1198**

(0.575) (-1.980) (-0.340) (-2.732) (-2.289)

Initial Excess Returns -0.0072*** 0.0033 -0.0029 0.0042 0.0161***

(-4.52) (0.584) (-0.562) (0.713) (2.775)

Residuals -0.4412** -0.1979 -0.0126 -0.3528 0.3603

(-2.509) (-0.910) (-0.076) (-0.950) (0.920)

Adj. R-square 0.55043 0.41844 0.39350 0.44853 0.33907

F-Statistic 3.955*** 2.3246** 2.096152** 2.627707*** 1.657

N 58 58 58 58 58



The large percentage of shares offered in the IPOs generate signals of

dissatisfaction of initial shareholders on the firms’ future prospects. Based on the

theoretical and empirical literature, it is conjectured that the % of shares offered is

negatively linked with LRRs as proposed by H20c hypothesis. The % of shares

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offered coefficient is negative and statistically significant linked with BHARs from

BHAR2Y to BHAR5Y. The finding of this analysis is consistent with the proposed

hypothesis of H20c and also can be verified from the univariate analysis (see Table

4.39).

It is hypothesized that any ‘mispricing’ on the first trading day, in the efficient

markets, is corrected in the longer period when more IPOs related information has

taken place. The regression coefficient of IERs related to BHARs appears to be

positive for all years except year 1. These findings are also similar to non-

privatization IPOs sample BHARs. On the other side, the CARs long-run models

produces negative coefficients related to IERs, which is consistent with the working

hypothesis of H22 and with Kerins, Kutsuna & Smith (2007). These findings are

consistent with signaling theory, which suggests that the large offer price discount in

the offer price by the quality IPOs used as a signal and perform better in the longer

period than the low-quality firms. Therefore, it is expected that IERs are negatively

linked with IPO long-run returns.

The standardized residual series extracted from the IPO valuation model is

added to control the unobservable effect of the initial valuation on the LRRs. The

coefficient of residual is negative and statistically significant at the 90% level of

confidence related to BHARs. The findings of CARs long-run models are robust as

concluded in the BHARs long-run models. The higher values of residuals indicates

that IPOs are valued higher in early years and adjust these prices in the long run when

more information becomes accessible, in turn lowers the LRRs.

In this section, BHAR and CAR are employed to estimate the LRRs in order

to investigate the accuracy of measuring long run returns. Based on the empirical

findings of intercepts and adjusted R-squared of BHARs and CARs (see Table 4.40

and Table 4.41), the BHAR is more prudent valuation estimator than the CAR

because the BHAR is a geometric product of the spread in compound returns of

returns vs compound returns of what expected returns over a time period.

4.5 IPOs Long-run Performance Using Calendar-Time Approach

This section provides the insights of long term aftermarket performance of

firms that went public during 2000-2012. As already discussed in methodology

193

chapter, the LRRs can be estimated as the adjusted returns of loyal investors who

purchase shares in primary market and hold them up to 5 years. This LRR analysis

attempts to deal with objective: “To investigate the robustness of IPOs long-run

underperformance using asset pricing models (calendar-time approach)”. This long-

run returns analysis also validates the long-run underperformance anomaly which is

observed in section 4.4 and discussed in the literature as well. In the calendar time

analysis, the long term price performance is estimated by using the capital asset

pricing model proposed by the Sharpe-Lintner (1964) and, Fama-French three- and

five-factor models proposed by Fama & French (1993,2015). In the long run analysis

part, the sample of 65 IPOs has been used for the asset pricing models analysis. The

inclusion of IPOs is based only on those firms that at least survive for five years from

the date of formal listing. The data of IPO prices and asset pricing models’ factors

have been extracted from PSX DataStream and annual reports of 225 non-IPO firms

respectively.

4.5.1 Descriptive Statistics

This part provides the summary statistics of variables used in equally-weighted and

value-weighted (see Appendix Table A.14) long-run returns analysis. These LRRs are

estimated by approaches of calendar-time such as CAPM and Fama-French three &

five factor models. As already discussed in the earlier, the share prices and financial

variables data of IPOs and non-IPO firms have been obtained from the PSX

DataStream and annual reports. The descriptive statistics of determinants of asset

pricing models used in equally-weighted and value-weighted analysis are reported in

Table 4.42 and Table A.14 respectively. The results of value-weighted returns used as

robustness check and to validate the results estimated through equally-weighted

approach.

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Table 4. 42: Descriptive Statistics of Variables Used in Asset Pricing Models

Variable Mean Min Percentiles

Max SD Obs. 25th 50th 75th

IPO_Rf 0.0043 -0.8236 -0.0818 -0.0105 0.0685 2.3180 0.1795 3,900

Rm_Rf 0.0071*** -0.3853 -0.0180 0.0067 0.0516 0.3074 0.0780 3,900

SMBFF3F 0.0086*** -0.2933 -0.0210 0.0049 0.0322 0.2787 0.0534 3,900

SMBFF5F 0.0086*** -0.2911 -0.0204 0.0049 0.0327 0.2819 0.0529 3,900

HML 0.0072*** -0.5069 -0.0243 0.0020 0.0279 0.5269 0.0551 3,900

RMW 0.0000 -0.3183 -0.0225 0.0005 0.0228 0.3315 0.0454 3,900

CMA -0.0043*** -0.3676 -0.0240 -0.0021 0.0168 0.4529 0.0446 3,900


The median excess return on IPOs portfolio over risk-free rates is found to be positive

irrespective of whether it is value-weighted or equally-weighted. The difference

between the minimum and maximum values is large for both value-weighted and

equally-weighted IPOs adjusted returns indicative the evidence of extreme values in

the sample. However, the author didn’t try to winsorize or truncate the data due to

limited sample of IPO firms as compared to other similar studies. The standard

deviation of value-weighted returns is greater than the equally-weighted returns.

Ediriwickrama & Azeez (2017) and Fama (1998) employ th Fama (1998) and value-

weighted returns since it represents the total wealth effect. The median market risk

premium is observed positive for the sample period during 2000-2012 indicates that

more investment in a risk-free asset such as treasury bills is a profitable venture than

the investing in a risky-asset such as IPOs. The mean and median excess returns of

SMBFF3F, SMBFF5F, HML and RMW appeared to be positive and statistically

significant at the 99% level of confidence except CMA.


Table 4.43 demonstrates the correlation (Pearson) coefficients of variables used in

asset pricing models. To avoid the repetition, this study only document the equally-

weighted correlation matrix in Table 4.43, while value-weighted correlation matrix is

reported in Table A.15 (see appendix). In this part, one-to-one relationship of IPO

portfolio excess returns related to the market risk premium, SMB, HML, RMW and

CMA factors is discussed while t-statistic is reported below each correlation

195

coefficient. It has been observed that the correlation matrix does not report any

multicollinearity problem.

Table 4. 43: Correlation Matrix of Variables Used in Asset Pricing Models

IPO_RF RM_RF SMBFF3F SMBFF5F HML CMA RMW

IPO_RF 1

RM_RF 0.4002*** 1

(27.268)

SMBFF3F -0.1625*** -0.4169*** 1

(-10.284) (-28.641)

SMBFF5F -0.1666*** -0.425*** 0.9965*** 1

(-10.554) (-29.319) (743.751)

HML -0.0211 -0.0891*** 0.3891*** 0.3834*** 1

(-1.3207) (-5.586) (26.368) (25.923)

CMA 0.0247 0.0196 -0.1965*** -0.1881*** -0.2504*** 1

(1.5485) (1.2273) (-12.518) (-11.963) (-16.151)

RMW -0.0601*** -0.1114*** -0.0573*** -0.0532*** -0.2173*** 0.3110*** 1

(-3.753) (-7.001) (-3.583) (-3.326) (-13.900) (20.431)


The univariate analysis of asset pricing models’ factors reports expected

correlations. The finding reveals that the correlation coefficient of the market risk

premium appears to be positive (0.4002) to IPOs portfolio excess returns and

statistically significant at the 1% level. This finding is consistent with the theorey and

the empirical literature on asset pricing models. The SMB is measured by the

difference between the portfolio returns of small stocks and big stocks in the month t.

The correlation coefficient between the size factors (i.e. SMBFF3F and SMBFF5F) and

IPO portfolio adjusted returns is found to be negative and statistically significant. The

results are in sequence with the theorey of size effect because small (low market

capitalization) firms produce higher returns in the market. The HML is measured by

the difference between portfolio returns of a high BTM stocks and the low BTM value

stocks in month t. The correlation coefficient of the value factor (HML) is found to be

negative by (-0.0211) related to IPO portfolio adjusted returns. The findings are in

line with the theorey of value premium effect because the growing (low book-market

ratio) firms produce higher returns in the market. The RMW is measured by the

difference between portfolio returns of robust profitability and weak profitability

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stocks in the month t. The correlation coefficient of profitability factor (RMW) is

found to be negative by (-0.0601) related to IPO portfolio adjusted returns. The results

are in sequence with the underlying theoretical assumptions of profitability effect

because firms having low operating profitability produce higher returns as compared

to the comparable firms. The CMA is measured by the difference between the

portfolio returns of conservative investment stocks and aggressive investment stocks

in the month t. The coefficient of investment factor (CMA) is found to be positive

(0.0247).

4.5.3 The Analysis of Asset Pricing Models

In this part, the study investigates the long-run performance using the Jenson’s

alpha (coefficient of intercept term) estimated through the CAPM model proposed by

the Sharpe-Lintner (1964), FF3F & FF5F models proposed by Fama & French

(1993,2015). It is widely discussed in the descriptive statistics analysis that the long

run, up to 5 years from the listing day, underperformance in Pakistan is in line with

existing literature of emerging and developed markets (see from Table 2.4 to Table

2.7). Generally, these models produce long run risk-adjusted performance of IPOs

using calendar-time methods. In the asset pricing models, Jenssen’s Alpha is observed

the negative valuvs as expected.

Table 4. 44: Regression Analysis of Capital Asset Pricing Models

CAPMY1 CAPMY2 CAPMY3 CAPMY4 CAPMY5

Panel-A: Equally Weighted

Intercept -0.0064 -0.0053 -0.0033 -0.0038 -0.0023

Rm-Rf 0.9573*** 0.8860*** 0.8917*** 0.8862*** 0.9211***

Adj. R2 0.1676 0.1675 0.1635 0.1663 0.1602

F Statistic 156.595*** 313.382*** 456.976*** 622.012*** 743.558***

Obs. 780 1,560 2,340 3,120 3,900

Panel-B: Value Weighted

Intercept 0.0024 -0.0003 0.0002 -0.0011 -0.0002

Rm-Rf 1.1432*** 0.9791*** 0.9611*** 0.9382*** 0.9611***

Adj. R2 0.0618 0.0764 0.0859 0.0969 0.1035

F Statistic 51.287*** 128.802*** 219.776*** 334.647*** 449.936***

Obs. 780 1,560 2,340 3,120 3,900


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Panel-A of Table 4.44 shows the findings of equally-weighted excess returns

after controlling the market excess returns factor. The coefficients of the intercepts for

year 1 to year 5 appear to be negative but statistically insignificant which indicates

that the IPOs underperform over the five year periods successive to formal listings.

The market excess returns (RM-RF) factor appears to be positive and significant for all

time periods at the 1% level. The findings of value-weighted excess returns validate

the long-run underperformance for all time periods except year 1 and year 3. These

results are similar with other emerging economy study (Ediriwickrama & Azeez,

2016) and developed economy studies (Thomadakis, Nounis & Gounopoulos, 2012,

Gompers, and Lerner, 2003; Espenlaub, Gregory & Tonks, 2000).

Table 4. 45: Regression Analysis of Fama-French Three Factor (FF3F) Models

FF3FY1 FF3FY2 FF3FY3 FF3FY4 FF3FY5


Intercept -0.0055 -0.0048 -0.0033 -0.0037 -0.0026

Rm-Rf 0.8987*** 0.8543*** 0.8799*** 0.8699*** 0.9231***

SMBFF3F -0.2170 -0.1332 -0.0610 -0.0723 -0.0038

HML 0.1136 0.1273* 0.0950 0.0757 0.0490

Adj. R2 0.1696 0.1693 0.1644 0.1669 0.1604

F Statistic 52.831*** 105.738*** 153.212*** 208.142*** 248.118***

Obs. 780 1,560 2,340 3,120 3,900


Intercept 0.0055 0.0003 -0.0003 -0.0016 -0.0007

Rm-Rf 1.0742*** 0.9788*** 0.9802*** 0.9526*** 0.9786***

SMBFF3F -0.1615 -0.0011 0.0406 0.0293 0.0252

HML -0.0728 0.0020 0.0147 0.0301 0.0439

Adj. R2 0.0626 0.0764 0.0860 0.0970 0.1037

F Statistic 17.283*** 42.879*** 73.2914*** 111.623*** 150.217***

Obs. 780 1,560 2,340 3,120 3,900


Panel-A of Table 4.45 demostrates the results of equally-weighted excess

returns after controlling the market excess returns, size and the value factors. The

coefficients of intercepts for year 1 to year 5 appear to be negative but statistically

insignificant which specifys that the IPOs underperform over the five year periods

successive to formal listings. The market excess returns (RM-RF) factor appears to be

positive and strongly significant for all time periods at the 1% level each. The

findings of value-weighted excess returns also validate the long-run

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underperformance for all time periods except year 1 and year 3. The monthly market

excess returns factors in both equally- and value-weighted panels show that IPOs are

subject to a much lower level of systematic risk. Table 4.45 reports that there is a

negative association between the IPO portfolio excess returns and the SMBFF3F

factors. This implies that the larger size firms produce lower returns than the smaller

size. The coefficient of HML appears to be positively related to IPO portfolio returns

but statistically insignificant. This implies that high growth firms generate large

returns than small firms. The findings of Fama-French three-factor analysis are

matched with existing literature of emerging economy studies (Ediriwickrama and

Azeez, 2017, 2016; Mumtaz, Smith and Ahmed, 2016; Liu, Uchida and Gao, 2014;

Moshirian, Ng and Wu, 2010; Ahmad‐Zaluki, Campbell & Goodacre,. 2007 ) and

developed economy studies (Boissin and Sentis, 2014; Brau, Couch and Sutton, 2012;

Choi, Lee and Megginson, 2010; Pukthuanthong and Varaiya, 2007; Rosa,

Velayuthen and Walter, 2003; Espenlaub, Gregory and Tonks, 2000).

Table 4.46 presents the regression results of equally- and value-weighted

Fama-French five-factor models for year 1 to year 5. In these models, the depended

variables are equally- and value-weighted monthly excess returns of IPOs portfolio.

The expnalatory determinants are RM-RF, SMBFF3F, HML, RMW (Robust profitability

minus weak profitability) and CMA (conservative investment minus aggressive

investment). The t-statistics have been estimated through the Huber/White standard

errors.

Table 4. 46: Regression Analysis of Fama-French Five Factor (FF5F) Models

FF5FY1 FF5FY2 FF5FY3 FF5FY4 FF5FY5


Intercept -0.0075 -0.0056 -0.0035 -0.0033 -0.0022

Rm-Rf 0.9109*** 0.8619*** 0.8873*** 0.8699*** 0.9187***

SMBFF5F -0.1869 -0.1362 -0.0522 -0.0575 0.0026

HML 0.1246 0.1597** 0.1149* 0.0826 0.0541

RMW 0.0789 0.0980 0.0599 -0.0341 -0.0809

CMA 0.1096 0.0927 0.0626 0.1033 0.1111*

Adj. R2 0.1708 0.1708 0.1651 0.1675 0.1612

F Statistic 31.876*** 64.035*** 92.279*** 125.337*** 149.694***

Obs. 780 1,560 2,340 3,120 3,900


Intercept -0.0010 -0.0014 -0.0006 -0.0012 -0.0004

199

Rm-Rf 1.0601*** 0.9667*** 0.9769*** 0.9502*** 0.9863***

SMBFF5F -0.1894 -0.0371 0.0341 0.0259 0.0531

HML -0.0085 0.0349 0.0499 0.0629 0.0665

RMW 0.2090 0.0495 0.0490 0.0158 -0.0425

CMA 0.4191 0.3774** 0.3214*** 0.3389*** 0.3151***

Adj. R2 0.0681 0.0817 0.0907 0.1020 0.1080

F Statistic 11.305*** 27.644*** 46.538*** 70.738*** 94.311***

Obs. 780 1,560 2,340 3,120 3,900


Panel-A of Table 4.46 demonstrates the results of equally-weighted excess

returns after controlling the market excess returns, size, value, profitability and

investment factors. The coefficients of the intercepts for year 1 to year 5 appear to be

negative, but statistically insignificant which indicates that the IPOs underperform

over the five year periods successive to formal listings. The market excess returns

(RM-RF) factor appears to be positive and strongly significant for all time periods at

the 1% level each. The coefficients of market risk indicate a lower level of systematic

risk. Table 4.46 reports a negative association of SMBFF5F factors which means large

firms produce lower returns than the smaller firms. The coefficient of HML appears to

be positive and statistically significant in equally weighted but insignificant in value-

weighted findings. This implies that the large value firms produce greater returns than

smaller firms. The coefficient of RMW appears to be positive but statistically

insignificant. This implies that the large profitable firms produce more excess returns

than the fewer profit firms. The coefficient of CMA appears to be positive and

statistically significant only in the value-weighted analysis. This implies that the firms

put more investment in the capital expenditure generates more returns than the firms

with stagnant investment strategy. The finding of Fama-French five-factor analysis is

consistent with the existing literature of emerging economy (Ediriwickrama and

Azeez, 2017).

200

5. Chapter 5

5. Summary and Conclusion

201

The summary and conclusion chapter is split into five sections. This chapter starts

with, after conversing the key research objectives and methodologies employed for

this study, the summary of comprehensive findings is conversed that begins with the

insights of alternative valuation methods used by underwriters and the power of pre-

IPO valuations to enlighten the cross-sectional variation in the post-IPO prices of each

valuation method, the role of prospectus information on the short-run mispricing and,

longer period underperformance, and to provide comparative analysis of long-run

price performance using asset pricing models as robust measures to address the issues

related long-run returns measurement. This chapter pursued by the policy

implications, results summary, limitations and future directions f or other scholars.

The key objectives of thesis includes; (1) to provide insights of preferred

valuation methods when underwriters valuing IPOs and the important cross-sectional

determinants inview of valuation theories, (2) to compare the bias and accuracy

attached with each valuation method by using the ‘real-world’ estimates disclosed in

the prospectus documents to study that how underwriter accurately price the IPOs, (3)

to provide insights of usefulness on the prospectus information during the initial

valuations, the initial market ‘mispricing’ and longer period underperformance, and

finally, (4) to validate the rubustness of long-run underperformance using event- and

calendar-time methods to address the issues and sensitivity related LRR

measurements.

To meet above-mentioned research objectives several econometric models

were used. In the analysis of the underwriter preference of several valuation methods,

the frequency distribution and binary logit regression model for each alternative

valuation method were used. The SPE and APE were used to calculate the bias and

the accuracy attached to each valuation method respectively. The accounting-based

valuation model was used to examine the effect of fundamental, risk and signaling

factors on the initial valuations and aftermarket price performance. The CAPM,

Fama-French three- and five-factor models were used to inspect and affirm the long-

run underperformance anomaly, and to address the issues related to long-run returns

measurements.

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5.1 The Analysis of Pre-IPO Valuation Dynamics

An extant literature on IPO valuation is especially thin regarding how underwriter

value the IPOs. In this section, the underwriter’s detailed valuation analysis data is

extracted from the prospectus documents of 88 IPOs which are analyzed for pre-IPO

valuation dynamics wrapping the period of 17 years from 2000 to 2016. This study

aims to address the questions of (i) How do investment bank select each valuation

method when valuing IPOs? (ii) To find bias and accuracy attached to each valuation

method that how underwriters accurately value the IPOs?

In this study lead underwriters repeatedly used DDM, DCF and multiples

valuation methods to value firms they bring public. This study finds that the Pakistani

lead underwriters prefer to select multiples valuation method when valuing mature

firms, the firms having less assets-in-tangibility and during bearish market sentiment

phase. The discounted cash flow method is used when valuing young firms, the firms

which gradually increase their fixed assets by investing more in capital expenditures,

profitable and rapidly growing firms. The dividend discount model is preferred by

underwriters when valuing firms that offer major part of their incomes as a corporate

payout to its shareholders. In the Pakistani primary market, the preference of DDM

should cater the demand of dividends paying equities.

In this study, Wilcoxon Sign Rank test and standard t-statistics on the medians

and mean values of signed prediction errors of each valuation method were used to

estimate the bias associated with each valuation method. The findings document that

most of the valuation methods were associated with positive values of mean and

median valuation prediction errors which were statistically significant different from

zero. The findings reveal that the DDM and DCF methods seem to be unbiased value

estimators because their median valuation prediction errors were only (-1.92%) and (-

0.50%) respectively. This implies that the lead underwriters accurately estimate the

intrinsic value of newly listed firms’ equity. These findings inline with Deloof,

Maeseneire & Inghelbrecht (2009, 2002) and Francis, Olsson & Oswald (2000) but

contrary to Roosenboom (2012); Cassia, Paleari & Vismara, 2004).

This study presents the mean, median and the percentage of valuation errors

are within 15% or less of actual estimates of absolute prediction errors to estimate the

accuracy attached to each valuation method. Our findings reveal that the DCF

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estimates were significantly smaller (20.28%) and the degree of central tendency of

percentage is within 15% was the highest (45.00%) than other methods. This implies

that the valuation accuracy of DCF was the highest. The results of DCF highest

valuation accuracy are consistent with Deloof et al., (2009, 2002), and Berkman,

Bradbury & Ferguson (2000). On the other side, the P/E ratio estimates were

significantly larger (34.28%) and the degree of central tendency of percentage is

within 15% was lowest (25.00%) than other methods. This implies that the valuation

accuracy of P/E ratio was smallest. The results of P/E lowest valuation accuracy is

consistent with Goh, Rasli, Dziekonski & Khan (2015), Deloof et al., (2009, 2002),

Cassia, Paleari & Vismara (2004), Berkman, Bradbury & Ferguson (2000) and Kim &

Ritter, (1999).

The findings conclude that how underwriters accurately value the IPOs and to

set the preliminary offer prices with the intention to better understand why initial

‘mispricing’ exists. This communicates to the observation of Ritter & Welch (2002)

who conclude that “the solution to the underpricing puzzle has to lie in focusing on

the setting of the offer price” (p. 1803). An important observation was that investment

banks deliberately offer discount to fair value estimates to set strike price for IPOs,

cause the reason of initial underpricing. Our findings reveal that the majority

investment banks don’t completely depend on comparable multiples valuation when

valuing IPOs. These findings contrary with the extant literature that most studies rely

on the multiples methodse (Houston et al., 2006; Purnanandam & Swaminathan,

2004; Kim & Ritter, 1999).

5.2 The Analysis of Post-IPO Price Performance

In this section, 86 IPOs were analyzed for initial valuations analysis and initial market

‘mispricing’ analysis floated during 2000-2016. The sample was reduced to 65 firms

for long-run price performance analysis, those who survive more than 5 years from

the first trading date in the stock exchange, listed during 2000-2012. The average

offer size offered to general public equals PKR 992.40 million during the sample

period. The Network Microfinance Bank Limited offered minimum offer size equals

PKR 40.00 million, while the Habib Bank Limited offered highest offer size equals

PKR 12,161.25 million. The purpose of this thesis to settle research questions of (i)

“Does the prospectus information contributes to value the IPOs during ex-ante pricing

204

decision process? (ii) Does the prospectus information have an explanatory power to

explain the short-run underpricing and long-run underperformance? and Does the

Calendar-time approach validate the IPOs long-run underperformance anomaly?”.

The initial valuations analysis has been carried out on the basis of accounting

based valuation models, both at the offer prices and the 1st trading day market prices.

This model theorizes that the preliminary prices of IPOs are an increasing function of

fundamental and signaling factors, while a decreasing function of risk factors. The

findings reveal that the initial prices mainly depend on the fundamental factors, such

as book value and dividend payouts. The dummy variable of negative earnings was

appear to be positive and significant by linked with initial prices. Only four out of six

risk factors, the financial leverage, capacity risk, firm beta and offer size, are

statistically significant and validate that preliminary offer prices are the diminishing

function of risk factors. From signaling variables, only firm age was statistically

significant but the association was not as expected.

The analysis of IPOs price performance seeks to unfold two anomalies: the

short-run underpricing and long-run underperformance. The level of underpricing was

observed to be 32.85% which was greater than the US, the UK and other developed

countries (Loughran and Ritter, 2003; Ljungqvist, 2009; Lee, et al., 2012). Though,

these initial excess returns were lower than in China, Jordan, India and Sri Lank (Yu

and Tse, 2006; Marmar, 2010; Shelly and Singh, 2008; Peter, 2007). This indicates

that the initial participants buy shares at IPO offer prices and sell them on 1st trading

day earn abnormal returns of 32.85% from their investments. This research extend

underpricing analysis in various aspects such as: (1) the findings show that highest

market capitalization IPOs produce returns of 19.64%, while lowest market

capitalization IPOs produce returns of 39.46%. This implies that there is inverse

relation between level of underpricing and firm size. These results were consistent

with the assumption of ex-ante uncertainty hypothesis, (2) this study argue that the

percentage of initial underpricing of IPOs issued in hot-issue market was significantly

higher than the IPOs issued in cold-issue market. The findings of the hot-cold issues

analysis are consistent with the changing risk composition hypothesis (Ritter, 1984)

and signaling hypothesis (Allen & Faulhaber, 1989), (3) the on average underpricing

(8.46%) of IPOs issued through bookbuilding auction was lower than the IPOs

(41.23%) issued through fixed price auction. This implies that the institutional

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investors, who are more informed, prefer bookbuilding mechanism because they get

more shares at a better price due to their active participation in the price discovery

process, (4) Even when the firms categorized to the privatization IPOs and non

PIPOs, the results show that the PIPOs underpricing was higher than the underpricing

of non PIPOs, (5) the results show that the underpricing of survivor IPOs (firms that

delist from the PSX during five years since the date of formal listing) was higher than

the non-survivor IPOs, (6) The analysis of year-wise reveal that, from 2006 to 2007,

the highest underpricing was observed because the investment community was

optimistic about economy due to high GDP growth rate, low inflation rate, stable

exchange rate and healthy FDI in that period, and (7) the findings of sector-wise

analysis revealed that the firms listed in Oil & Gas sector and Chemicals sector

produce higher initial excess returns than other sectors. The finding of IER regression

analysis reveals that the earnings disclosed before the IPO, financial leverage,

efficiency risk, firm beta and the underwriter reputation were the key determinants of

prospectus information to explain the variation in level of underpricing.

In the LRR analysis, this study observed that the newly listed firms didn’t

maintain their initial excess returns pattern and produce negative returns over five

year periods from the 1st trading date. The most common approaches such as BHAR

and CAR were employed to measure the long-run abnormal returns, both known as

Event-time technique. The BHARs produce negative returns of -23.52% and -65.22%

in year 3 and year 5 respectively. On the similar pattern, the CARs produce negative

returns of -24.62% and -29.37% in year 3 and year 5 respectively. These findings

were consistent with the existing literature of Pakistan, while this study contributes by

estimating value-weighted BHARs and CARs that validates the long run poor

performance measured by equally-weighted methods. This study extends long-run

performance analysis in various aspects such as: (1) in the long run year-wise

analysis, the large underperformance was observed often in the hot issue phases while

over performance was observed in the cold issues. This implies that the firms

outperform the market that went public after the US internet bubble crisis in 1999-

2001 and the US subprime mortgage crisis in 2008-2009 periods. The results of CARs

and BHARs were more or less similar, (2) the findings of sector-wise analysis depicts

that the firms listed in Automobile & Electrical Goods sector produce worst negative

returns, while the firms listed in Modaraba & Foods produce positive returns over

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longer periods, (3) the privatization IPOs produce better returns than the non-

privatization IPOs (see Table 4.33) in the long run, (4) the IPOs over-priced at formal

listing by lead underwriters perform worst than IPOs deliberately under-priced at

formal listing in the long run, and (5) the financial services IPOs perform more

negative than the non-financial IPOs over a longer time period after listing. The

finding of LRR regression analysis reveals that the book value of shareholder’s

equity, earnings disclosed before the IPO, capital availability risk, firm beta,

underwriter reputation, the percentage of shares offered and initial excess returns were

significant determinants that explain the variation in the LRR.

The calendar-time approach was used as robust measure to validate long-run

underperformance. The long term performance was estimated using the Jenson’s

alpha (coefficient of intercept term) estimated through the capital asset pricing model

proposed by the Sharpe-Lintner (1964) and Fama-French three and five factor models

proposed by Fama & French (1993, 2015). The coefficients of intercept produce

negative signs for all asset pricing models which represent the negative performance

in long run. The market risk premium was the most significant determinant in all asset

pricing models, while HML-value factor (in equally-weighted FF5F) and CMA-

investment factor (in value-weighted FF5F) were also significant determinants in the

Fama-French five-factor models.

This study concludes the preference and accuracy of each valuation method

with fair-value estimates using ‘real-world’ data disclosed in the offering documents.

The findings of model’s misspecification are also verified in PSX as already talked in

the existing literature of IPO with regards to the selection of methods. The long-run

poor performance was also observed irrespective of any measurement techniques

most common even-time and calendar-time.

5.3 Policy Implications of the Study

After the significant contributions of this thesis to the existing literature on IPO and

behavioral corporate finance field, the findings of this research have significant

implications for positive social financial reforms and draw attention to the foremost

significance of numerous stakeholders engaged in the IPOs implementation process

on the PSX.

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5.3.1 The Pakistan Stock Exchange

This study identifies a few irregularities which require some serious actions by PSX.

The findings of valuation prediction errors document that each valuation method

produces valuation bias; only DCF estimates produce consistent values. The second

issue, the deliberate discount in the offer prices by investment banks caused, the main

reason of large underpricing in the market. Due to this deliberate discount, the issuing

firms lose so much money as they expected to be from the IPO transactions. Third, a

number of IPOs could not survive more than 5 years from the 1st trading date. Due to

non-survivor IPOs, the investors community loses their major investments in the

shape of a significant drop in prices (capital loss). Fourth, non-privatization IPOs

produce more negative returns in the long run than PIPOs. Last but not least, the pace

of delisting of non-IPO firms is more than the pace of new offerings in the PSX.

A more critical review is required by PSX in order to ensure that the new

offerings are accurately valued and imitate the underlying fundamentals. The

aforementioned irregularities of issuing firms losses in terms of large underpricing

and valuation prediction errors could be irritating private firms to get listed on the

capital market. The PSX need more marketing and road shows to attract more private

firms get listed in order to control the pace of delisting and increase the pace of new

listings.

5.3.2 The Investment Banks/Underwriters

Based on the findings related to valuation dynamics, there is still more space for

further developments in the initial pricing and valuation process of IPOs by lead

underwriters. The results (see Table 4.3) show that the Pakistani underwriters

employed DDM, DCF and comparable multiples methods when valuing IPOs. The

findings also reveal that the choice of valuation methods did not inevitably rely on

firm-specific characteristics and market-related factors. The analysis related to the key

determinants of each valuation method show that the investment banks offered price

discounts without taking any special care to the firm-specific characteristics and the

economic environment of market. Due to this indiscretion, the findings of valuation

prediction errors show that each valuation method produces positive bias. This

analysis suggests that the forthcoming IPOs should be valued and priced based on

their unique firm-specific features and the economic environment of market.

208

The findings show that the IPOs sponsored by prestigious underwriters

produce higher offer prices (see Table 4.15) and less underpricing (see Table 4.24)

during the IPO implementation process, however, the less reputed underwriters

offered more discount in offer prices than the prestigious underwriters due to under-

subscription risk. The IER analysis also shows that the IPOs offered through

bookbuilding produces less underpricing than the IPOs offered through fixed price

without taking care of the level of underwriter reputation (see Table 4.20). The

findings can be contributed and strengthen the valuation process of underwriters in

two ways: (1) infrequent underwriters can launch potential IPOs in the period of Hot-

issue market when participants are intentionally to pay more for riskier assets without

paying more attention to fundamentals than looking for future prospects, and (2) the

IPOs offered through the bookbuilding mechanism can reduce the risk of under-

subscription because well-informed investors actively participate in the primary

market when they think prices are less than their fair value estimates.

The benefits of proposed measures continue in a way that the lead

underwriters could take the advantage of the corporate perception of their professional

services and investors confidence in their reliable valuations.

5.3.3 The Unlisted/Potential IPO-issuing Firms

The findings of this study provide learning opportunity to unlisted firms from the

oversights of previous IPO issuing firms. The oversights of previously issued IPOs

include: ‘window-dressing’ or artificially manipulate their previous financial

statements, overestimations about future prospects and over prediction about future

financial statements. These oversights caused the reason of over/underpricing

irregularities observed in the PSX. The continuation of above-mentioned oversights

by forthcoming IPO firms getting listed on the PSX could further lose the confidence

of investors in the PSX primary market, which leads to under subscription of new

offerings and loss of wealth as they expected to be.

Under the findings of this study, the issuing firm need to pay more attention

when appointing a lead underwriter, legal advisor, auditor, listing auction mechanism

(fixed price vs bookbuilding) and market sentiments before the IPO transaction.

209

5.3.4 The Investment Community

The key aim of this study was to enhancement of investors confidence and increase

the pace of new offerings on the PSX. The findings of this study have shown mixed

results as abnormal returns on the 1st trading date and capital loss in the long run such

as negative returns over three to five years.The individuals and portfolio managers

can devise their investment strategies under the findings of this study in order to earn

good returns and avoid losses.

The institutional investors can play important role to improve the accuracy of

initial valuations and pricing process in Pakistan. The institutional and high-worth

investors could add value by aggressively attending road shows and before

bookbuilding meetings with the lead underwriters to give suggestions about expected

price bands based on their own research reports. In this way, they can transform their

role in the ex ante valuations and pricing process of upcoming IPOs.

5.4 Limitations of the Study

There are a few limitations as: First, the key source of data for conducting analyses

for this study was the prospectus documents. Thus, the completion of this study only

relies on the ability to get the offering documents. Due to a limited track record of

IPOs activity in the Pakistan as compared to the other regional emerging economies,

the scholar only arranged 88 IPOs offering documents listed during 2000-2016. For

an empirical analysis, the number of IPOs taken as a sample was probably small, that

may come into some econometric and/or statistical issues in order to produce good

results when a more in-depth examination is required.

Due to the importance of behavioral finance in the decision of valuations and

investments, the behavioral finance should continue its evolution from broad

description of imperfect rationality and its consequences such as investors/market

sentiments, the analysis of valuation biases, overestimation of payoff and the

underestimation of risk. This study underweight above aspects due to more

concentration required in post-issue performance paradigms.

When estimating the long-run price performance as compared to the existing

literature, the LRR possibly sensitive to the measurement techniques and benchmarks

used. In this study, the majority IPO firms were small in size and small capitalization

210

firm’s benchmark index was needed to produce accurate long term performance,

which is unavailable in PSX. In this study when asset pricing models were engaged as

robust measures to validate the long run underperformance phenomenon, only 225

non-IPO firms instead all firms were used to estimating the market risk premium,

size, value, profitability and investment factors in order to produce more accurate

results due to time and the unavailability of complete data constraints.

5.5 Suggestions for Future Research

In this study, the extensive exertions have been done to examine the choice and

accuracy of valuation methods employed by lead underwriters and issues related to

post-issue price performance of the IPOs floated during 2000-2016. In reality, there is

always space for improvement because the world becomes a global village. Due to the

unavailability of resources and time constraints, a few suggestions for future research

directions have been discussed below.

First, a rigorous scrutiny of due diligence of company information, financial

feasibility/model, oversights of accounting standards and earnings management

related issues in the offering documents are required by regulatory institutions that

form the basis of initial valuations and post-issue price performance. This scrutiny

helps to assure the transparency of financial data reported in prospectus and

establishing the confidence of investors on IPO activity on the PSX. According to

Cervellati et al., (2013), the issues related to transparency intends to provoke a stock

crisis and the wipe out the confidence of investors on the whole financial system.

In future, the scholars can conduct a survey among investment

banks/underwriters along with the valuation information disclosed in the prospectuses

to shed more light on why and when investment banks employ a specific valuation

method. Further, the studies regarding the selection and accuracy of valuation

methods deserve more attention in similar contexts such as mergers & acquisitions

and private equity investments. According to the existing literature, the capital

expenditures used as a proxy for a need for financing, growth and cost of credit is still

debatable. Academic researchers have discussed the importance of macroeconomic

and capital market determinants when issuing firms motivated to raise capital for new

projects. In future, other variables can be added in the pre-IPO and post-IPO analysis

211

such as asset risk, total productivity factor, exchange rate and GDP growth used by

developed countries researchers.

The findings of this study reveal that the prospectus information have impact

on the initial valuations and observed underpricing in various esteems. An extant

literature on IPO also validates that the higher initial returns are taking place due to

higher demand and/or oversubscription of IPOs. So, it could be exciting to investigate

whether the prospectus information has the power to foresee the extent of subscription

of new offerings.

Last but not least, the IPO primary market of the PSX is not widely explored so far

and there are still different issues which can be examined such as impact of

macroeconomic, capital market and firm-specific determinants on in IPOs activity,

issues related to measurement of the reputation of underwriters and legal advisors, to

find the reasons of non-survivor IPOs, comparison between the IPOs issued through

the fixed price and bookbuilding mechanism, and pre- & post-issue operating

efficiencies.

212

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Appendix

243

Table A. 1: Characteristics of IPO Firms used in Sample

Sr.

No.

Listing

Year Symbol IPO Company Name Sector Auction Type

Offer

Price

1 2016 AWWAL Awwal Modaraba Modaraba Fixed Price 10.00

2 2016 HTL Hi-Tech Lubricants Ltd Oil & Gas “Bookbuilding 62.50

3 2016 TPLPL TPL Properties Ltd Property & Investment Bookbuilding 12.50

4 2016 LOADS Loads Limited Automobile & Electrical Bookbuilding 34.00

5 2015 SINDM Sindh Modaraba Modaraba Bookbuilding 10.00

6 2015 SYS Systems Limited Technology & Comm. Bookbuilding 40.00

7 2015 SPEL Synthetic Products Enterprises Ltd Engineering & Allied Bookbuilding 30.00

8 2015 MUGHAL Mughal Iron & Steel Industries Ltd Engineering & Allied Bookbuilding 34.00

9 2015 DCR Dolmen City "REIT" Property & Investment Bookbuilding 11.00

10 2015 ASC Al Shaheer Corporation Ltd Foods & Allied Bookbuilding 95.00

11 2015 ASTL Amerli Steels Ltd Engineering & Allied Bookbuilding 51.00

12 2014 EFERT Engro Fertilizer Ltd Fertilizer Bookbuilding 28.25

13 2014 AVN Avanceon Limited Technology & Comm. Bookbuilding 14.00

14 2014 HASCOL Hascol Petroleum Ltd Oil & Gas Bookbuilding” 56.50

15 2014 EPQL Engro Powergen Qadirpur Ltd Power Gen. & Dis. Fixed Price 30.02

16 2014 SPWL Saif Power Ltd Power Gen. & Dis. Bookbuilding 30.00

17 2013 LPL Lalpir Power Ltd Power Gen. & Dis. Bookbuilding 22.00

18 2012 NEXT Next Capital Ltd Investment Sec/Banks Bookbuilding 10.00

19 2012 TPL TPL Trakkar Ltd Automobile & Electrical Bookbuilding 10.00

20 2012 ASL Aisha Steel Mills Ltd Engineering & Allied Fixed Price 10.00

21 2011 ISL “International Steels Ltd Engineering & Allied Bookbuilding 14.06

22 2011 PKGP Pakgen Power Ltd Power Gen. & Dis. Fixed Price 19.00

23 2011 EFOODS Engro Foods Ltd Foods & Allied Fixed Price 25.00

24 2011 TDIL TPL Direct Insurance Ltd Insurance & Leasing Bookbuilding 10.00

25 2010 GGL Ghani Gases Ltd Chemicals Bookbuilding 14.00

26 2010 FATIMA Fatima Fertilizer Company Ltd Fertilizer Bookbuilding 13.50

27 2010 SMCPL Safe Mix Concrete Products Ltd Cement & Allied Fixed Price 12.50

28 2010 AGL Agritech Limited Chemicals Fixed Price 30.00

29 2010 AMTEX Amtex Limited Textile Bookbuilding 13.00

30 2010 WTCL Wateen Telecom Ltd Technology & Comm. “Fixed Price 10.00

31 2009 MDTL Media Times Ltd Technology & Comm. Fixed Price 10.00

32 2009 NPL Nishat Power Ltd Power Gen. & Dis. Fixed Price 10.00

33 2009 NCPL Nishat Chunian Power Ltd Power Gen. & Dis. Fixed Price 10.00

34 2008 AHBL Arif Habib Bank Ltd Commercial Banks Fixed Price 21.00

35 2008 IFSL Invest and Finance Securities Ltd” Investment Sec/Banks Fixed Price 10.00

36 2008 THCCL Thatta Cement Company Ltd Cement & Allied Fixed Price 22.50

37 2008 DEL Dawood Equities Ltd Investment Sec/Banks Fixed Price 17.50

38 2008 EPCL Engro Polymer & Chemicals Ltd Chemicals Fixed Price 18.00

39 2008 KASBSL KASB Securities Ltd Investment Sec/Banks Fixed Price 67.50

40 2008 FCIBL First Credit and Investment Bank Ltd Investment Sec/Banks Fixed Price 10.00

41 2008 AHIM Arif Habib Investment Management Ltd Investment Sec/Banks Fixed Price 125.00

42 2008 DOL Descon Oxychem Ltd Chemicals Fixed Price 10.00

43 2007 ARM Allied Rental Modaraba Modaraba Fixed Price 10.00

244

44 2007 AHL Arif Habib Limited Investment Sec/Banks Fixed Price 100.00

45 2007 HIRAT Hira Textile Mills Ltd Textile Fixed Price 12.50

46 2007 PACE PACE Pakistan Ltd Property & Investment Fixed Price 14.00

47 2007 JSIL JS Abamco Limited Investment Sec/Banks Fixed Price 65.00

48 2007 FLYNG Flying Cement Company Ltd Cement & Allied Fixed Price 14.00

49 2007 PASL Pervez Ahmed Securities Ltd Investment Sec/Banks Fixed Price 10.00

50 2007 SPL Sitara Peroxide Ltd Chemicals Fixed Price 10.00

51 2007 HBL Habib Bank Limited Commercial Banks Fixed Price 235.00

52 2007 DSL Dost Steels Limited Engineering & Allied Fixed Price 10.00

53 2006 BOK The Bank of Khyber Ltd Commercial Banks Fixed Price 15.00

54 2006 BIPL BankIslami Pakistan ltd Commercial Banks Fixed Price 10.00

55 2005 AMBL Network Microfinance Bank Ltd Commercial Banks Fixed Price 10.00

56 2005 IHFL International Housing Finance Ltd Commercial Banks Fixed Price 12.50

57 2005 JSCL Jahangir Siddiqui Capital Markets Ltd Investment Sec/Banks Fixed Price 52.50

58 2005 APL Attock Petroleum Limited Oil & Gas Fixed Price 57.75

59 2005 KAPCO Kot Addu Power Company Ltd Power Gen. & Dis. Fixed Price 30.00

60 2005 DFSML Dewan Farooque Spinning Mills Ltd Textile Fixed Price 10.00

61 2005 UBL United Bank Limited Commercial Banks Fixed Price 50.00

62 2005 ETNL Eye Television Network Ltd Technology & Comm. Fixed Price 10.00

63 2005 ZTL Zephyr Textiles Limited Textile Fixed Price 10.00

64 2005 CHENAB Chenab Limited Textile Fixed Price 18.00

65 2005 NETSOL Netsol Technologies Ltd Technology & Comm. Fixed Price 25.00

66 2005 WTL WorldCall Telecom Ltd Technology & Comm. Fixed Price 10.00

67 2005 DSIL D.S Industries Limited Textile Fixed Price 10.00

68 2005 STPL Siddiqsons Tin Plate Ltd Engineering & Allied Fixed Price 35.00

69 2004 OGDC Oil & Gas Development Company Ltd Oil & Gas Fixed Price 32.00

70 2004 WCBL WorldCall BroadBand Ltd Technology & Comm. Fixed Price 10.00

71 2004 MACFL MACPAC Films Ltd Automobile & Electrical Fixed Price 15.00

72 2004 CTTL Callmate Telips Telecom Ltd Technology & Comm. Fixed Price 10.00

73 2004 SNL Southern Networks Limited Technology & Comm. Fixed Price 10.00

74 2004 BAFL Bank Alfalah Limited Commercial Banks Fixed Price 30.00

75 2004 PPL Pakistan Petroleum Limited Oil & Gas Fixed Price 55.00

76 2004 FNEL First National Equities Limited Investment Sec/Banks Fixed Price 10.00

77 2003 ICL Ittehad Chemicals Limited Chemicals Fixed Price 10.00

78 2003 TRG TRG Pakistan Limited Technology & Comm. Fixed Price 10.00

79 2003 PICT Pakistan International Container Ltd Transporation & Comm. Fixed Price 10.00

80 2002 WCML WorldCall Multimedia Limited Technology & Comm. Fixed Price 10.00

81 2002 NBP National Bank of Pakistan Commercial Banks Fixed Price 10.00

82 2002 ACPL Attock Cement Pakistan Limited Cement & Allied Fixed Price 10.00

83 2002 BYCO Bosicor Pakistan Limited Oil & Gas Fixed Price 10.00

84 2001 BWCL Bestway Cement Limited Cement & Allied Fixed Price 10.00

85 2001 AHSL Arif Habib Securities Ltd Investment Sec/Banks Fixed Price 80.00

86 2000 WCPL WorldCall Payphone Limited Transporation & Comm. Fixed Price 15.00

87 2000 DFML Dewan Farooque Motors Ltd Automobile & Electrical Fixed Price 10.00

88 2000 MEBL Al Meezan Investment Bank Ltd Commercial Banks Fixed Price” 11.50

245

Table A. 2: The List of Non-Survivor IPO Firms During Sample Period

Sr.

No.

Listing

Year Symbol IPO Company Name Sector Action

1 2010 WTCL Wateen Telecom Ltd Technology & Comm. Delisted

2 2006 BIPL BankIslami Pakistan ltd Commercial Banks Merged

3 2005 CHENAB Chenab Limited Textile Delisted

4 2004 WCBL WorldCall BroadBand Ltd Technology & Comm. Merged

5 2004 CTTL Callmate Telips Telecom Ltd Technology & Comm. Delisted

6 2004 SNL Southern Networks Limited Technology & Comm. Delisted

7 2002 WCML WorldCall Multimedia Ltd Technology & Comm. Merged

8 2000 WCPL WorldCall Payphone Ltd Transporation & Comm. Merged

Table A. 3: The List of State Owned Enterprize (SOE) IPO Firms

Sr.

No.

Listing

Year Symbol IPO Company Name Sector

1 2015 SINDM Sindh Modaraba Modaraba

2 2007 HBL Habib Bank Limited “Commercial Banks

3 2006 BOK The Bank of Khyber Ltd Commercial Banks

4 2005 KAPCO Kot Addu Power Company Ltd Power Gen. & Dist.

5 2005 UBL United Bank Limited Commercial Banks”

6 2004 OGDC Oil & Gas Development Company Ltd Oil & Gas

7 2004 PPL Pakistan Petroleum Limited Oil & Gas

8 2002 NBP National Bank of Pakistan Commercial Banks

246

Table A. 4: Firms Post-IPO Cash and Stock Dividends (%) Announcements History

Sr.

No

Yea

r Company Name

Year t+1 Year t+2 Year t+3 Year t+4 Year t+5

Cas

h%

Stoc

k%

Cas

h%

Stoc

k%

Cas

h%

Stoc

k%

Cas

h%

Stoc

k%

Cas

h%

Stoc

k%

1 2016 Awwal Modaraba 2.00 0.00 Na na na na na na Na na

2 2016 Hi-Tech Lubricants Ltd 27.0 0.00 Na na na na na na Na Na

3 2016 TPL Properties Ltd 0.00 0.00 Na na na na na na Na Na

4 2016 Loads Limited 10.0 10.0 Na na na na na na Na Na

5 2015 Sindh Modaraba 1.60 0.00 3.50 0.00 na na na na Na Na

6 2015 Systems Limited 12.5 0.00 18.6 0.00 na na na na Na Na

7 2015 Synthetic Products Ent. . 10.0 0.00 15.0 0.00 na na na na Na Na

8 2015 Mughal Iron & Steel Ind. 5.00 15.0 30.0 0.00 na na na na Na Na

9 2015 Dolmen City "REIT" Ltd 0.80 0.00 10.4 0.00 na na na na Na Na

10 2015 Al Shaheer Corporation 0.00 0.00 0.00 50.0 na na na na Na Na

11 2015 Amerli Steels Ltd 0.00 0.00 20.0 0.00 na na na na Na Na

12 2014 Engro Fertilizer Ltd 30.0 0.00 60.0 0.00 70.0 0.00 na na Na Na

13 2014 Avanceon Limited 22.5 0.00 20.0 0.00 10.0 25.0 na na Na Na

14 2014 Hascol Petroleum Ltd 32.0 0.00 50.0 11.0 35.0 20.0 na na Na Na

15 2014 Engro Powergen Qadirpur 15.0 0.00 35.0 0.00 30.0 0.00 na na Na Na

16 2014 Saif Power Ltd 0.00 0.00 15.0 0.00 37.5 0.00 36.5 0.00 Na Na

17 2013 Lalpir Power Ltd 25.0 0.00 10.0 0.00 20.0 0.00 20.0 0.00 Na Na

18 2012 Next Capital Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

19 2012 TPL Trakkar Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.50 0.00

20 2012 Aisha Steel Mills Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

21 2011 International Steels Ltd 0.00 0.00 0.00 0.00 10.0 0.00 0.00 0.00 0.00 0.00

22 2011 Pakgen Power Ltd 65.0 0.00 30.0 0.00 25.0 0.00 10.0 0.00 20.0 0.00

23 2011 Engro Foods Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100 0.00

24 2011 TPL Direct Insurance Ltd 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

25 2010 Ghani Gases Ltd 0.00 0.00 0.00 0.00 0.00 0.00 5.00 25.0 0.00 0.00

26 2010 Fatima Fertilizer Co. Ltd 0.00 0.00 15.0 0.00 20.0 0.00 25.0 0.00 27.5 0.00

27 2010 Safe Mix Concrete Prod. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

28 2010 Agritech Limited 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

29 2010 Amtex Limited 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 30.0

30 2010 Wateen Telecom Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

31 2009 Media Times Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

32 2009 Nishat Power Ltd 0.00 0.00 0.00 0.00 0.00 0.00 20.0 0.00 30.0 0.00

33 2009 Nishat Chunian Power Ltd 0.00 0.00 20.0 0.00 35.0 0.00 60.0 0.00 65.0 0.00

34 2008 Arif Habib Bank Ltd 0.00 11.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

35 2008 Invest and Finance Sec. 0.00 0.00 0.00 0.00 11.5 0.00 0.00 0.00 0.00 10.0

36 2008 Thatta Cement Company 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

37 2008 Dawood Equities Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

38 2008 Engro Polymer & Chem. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

247

39 2008 KASB Securities Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.00 0.00

40 2008 First Credit and Inv. Bank 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

41 2008 Arif Habib Inv. Manag’t 25.0 80.0 0.00 0.00 0.00 20.0 15.0 0.00 22.5 0.00

42 2008 Descon Oxychem Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

43 2007 Allied Rental Modaraba 10.0 0.00 20.0 0.00 15.0 0.00 22.0 0.00 23.0 25.0

44 2007 Arif Habib Limited 100 10.0 25.0 25.0 15.0 25.0 0.00 20.0 0.00 0.00

45 2007 Hira Textile Mills Ltd 0.00 0.00 0.00 0.00 0.00 0.00 10.0 0.00 10.0 0.00

46 2007 PACE Pakistan Ltd 0.00 17.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

47 2007 JS Abamco Limited 0.00 0.00 0.00 25.0 0.00 0.00 0.00 0.00 0.00 0.00

48 2007 Flying Cement Company 0.00 10.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

49 2007 Pervez Ahmed Securities 0.00 0.00 20.0 27.5 0.00 0.00 0.00 0.00 0.00 0.00

50 2007 Sitara Peroxide Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

51 2007 Habib Bank Limited 40.0 10.0 55.0 20.0 60.0 10.0 65.0 10.0 70.0 10.0

52 2007 Dost Steels Limited 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

53 2006 The Bank of Khyber Ltd 0.00 0.00 0.00 0.00 0.00 25.0 0.00 0.00 0.00 0.00

54 2006 BankIslami Pakistan Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

55 2005 Network Microfinance Bnk 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

56 2005 Interl Housing Finance Ltd 12.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

57 2005 JS. Capital Markets Ltd 25.0 0.00 25.0 0.00 25.0 100 0.00 159.

7

0.00 243.7

8 58 2005 Attock Petroleum Limited 50.0 33.0 120 0.00 140 20.0 200 20.0 250 0.00

59 2005 Kot Addu Power Company 80.0 0.00 81.0 0.00 60.0 0.00 54.5 0.00 64.5 0.00

60 2005 Dewan Farooque Spinning. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

61 2005 United Bank Limited 25.0 25.0 30.0 25.0 30.0 25.0 25.0 10.0 25.0 10.0

62 2005 Eye Television Network 0.00 0.00 0.00 0.00 0.00 0.00 53.0 0.00 16.0 0.00

63 2005 Zephyr Textiles Limited 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

64 2005 Chenab Limited 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

65 2005 Netsol Technologies Ltd 0.00 0.00 0.00 0.00 0.00 37.0 10.0 40.0 0.00 0.00

66 2005 WorldCall Telecom Ltd 0.00 15.0 0.00 15.0 0.00 0.00 0.00 0.00 0.00 0.00

67 2005 D.S Industries Limited 0.00 0.00 0.00 0.00 10.0 0.00 0.00 100 0.00 0.00

68 2005 Siddiqsons Tin Plate Ltd 0.00 0.00 10.0 10.0 15.0 0.00 15.0 0.00 10.0 0.00

69 2004 Oil&Gas Development Co. 40.0 0.00 75.0 0.00 90.0 0.00 90.0 0.00 95.0 0.00

70 2004 WorldCall BroadBand Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

71 2004 MACPAC Films Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

72 2004 Callmate Telips Telecom 0.00 0.00 10.0 10.0 30.0 47.5 0.00 0.00 0.00 0.00

73 2004 Southern Networks Ltd 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

74 2004 Bank Alfalah Limited 0.00 25.0 12.0 33.0 0.00 30.0 15.0 23.0 0.00 12.5

75 2004 Pakistan Petroleum Ltd 55.0 0.00 90.0 0.00 110 10.0 155 10.0 130 20.0

76 2004 First National Equities Ltd 25.0 0.00 60.0 0.00 15.0 1500 0.00 0.00 0.00 0.00

77 2003 Ittehad Chemicals Limited 15.0 0.00 0.00 0.00 0.00 20.0 0.00 20.0 15.0 0.00

78 2003 TRG Pakistan Limited 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

79 2003 Pakistan Int’l Container 0.00 0.00 0.00 0.00 0.00 0.00 20.0 0.00 0.00 30.0

80 2002 WorldCall Multimedia Ltd 0.00 0.00 0.00 0.00 0.00 0.00 10.0 0.00 0.00 0.00

248

81 2002 National Bank of Pakistan 12.5 10.0 12.5 20.0 15.0 20.0 25.0 20.0 40.0 15.0

82 2002 Attock Cement Pakistan 15.0 10.0 10.0 0.00 12.5 0.00 12.5 0.00 50.0 0.00

83 2002 Bosicor Pakistan Limited 0.00 0.00 0.00 0.00 0.00 0.00 7.50 0.00 0.00 0.00

84 2001 Bestway Cement Limited 5.00 0.00 7.50 0.00 7.50 0.00 10.0 10.0 10.0 10.0

85 2001 Arif Habib Securities Ltd 50.0 0.00 50.0 20.0 100 33.3 150 150 100 50.0

86 2000 WorldCall Payphone Ltd 15.0 0.00 0.00 20.0 0.00 25.0 0.00 0.00 0.00 15.0

87 2000 Dewan Farooque Motors 0.00 0.00 0.00 0.00 10.0 0.00 10.0 5.00 10.0 0.00

88 2000 Al Meezan Inv. Bank Ltd 17.5 0.00 5.00 10.0 5.00 10.0 0.00 15.0 0.00 16.0

Note: ‘na’ - corporate payout announcements are pending. The value of Cash Dividends is w.r.t IPO

firm par value and Bonus Dividends are w.r.t total number of outstanding shares in the announcement

year.

249

Table A. 5: Cross-sectional Regressions of Bias of Valuation Methods

Model Bias DDM Bias DCF Bias Multiples Bias Fair_Value

Dep. Variable (1) (2) (3) (4)

Intercept 41.8111* -137.216 60.1619** 39.8287***

(2.134) (0.648) (4.404) (5.230)

Size -26.7512*** 14.405 -0.9427 -3.4499

(-4.321) (0.622) (-0.125) (-1.119)

Firm Age -23.8963 -2.6768

(-1.491) (-0.390)

Property, plant & Equip. -1.7184** -0.2489** 0.1184 -0.0147

(-2.428) (2.278) (0.484) (-0.169)

Operating Profitability 0.4778** 0.102 0.0427

(2.616) (0.839) (0.515)

Sales Growth 0.0249 -0.1162* -0.0376

(0.124) (-1.863) (-0.804)

Dividend Payout -0.2483 -0.2697*

(-1.171) (-1.819)

Technology 6.0638 -10.7649

(0.435) (-1.303)

Market Returns 1.7355** 0.7473** 0.3225

(2.963)

(2.772) (1.449)

Ex ante 13.1131 22.8946*

(0.624) (1.731)

Underwriter Reputation -33.6536** -5.9192

(-2.709) (-0.764)


(1.502) (0.609)

Adj. R-Square 0.7301 0.3520 0.14104 0.13928

F-Statistic 6.3123 3.3093 1.94 1.089

Prob(F-Statistic) 0.0211 0.044 0.5073 0.3893

N 15 20 65 88



250

Table A. 6: Value Relevance Regressions (at IPO Offer prices)

Indep. Variable Parameter Adj. R2

(%) N Wald test

Intercept Slop

Dividend Discount Model 0.1410 1.0147** 63.60 15 0.1072***

(0.303) (3.062)

Discounted Cash Flow 0.2096 0.8871*** 47.82 20 3.0879*

(1.300) (7.479)

Multiples Valuation 0.0690 1.0223*** 84.12 65 14.8495***

(0.929) (18.64)

P/E Ratio 0.1477 0.9737*** 80.93 36 10.5429**

(1.218) (12.379)

P/B Ratio 0.1397 0.9532*** 86.31 51 10.7959**

(2.165) (23.216)

Fair Value Estimate 0.0866 1.0032*** 82.29 88 15.8657***

(1.234) (18.684)

251

Table A. 7: Cross-sectional Regressions of Accuracy of Valuation Methods (IPO Offer

Prices)

Model Accuracy

DDM

Accuracy

DCF

Accuracy

Multiples

Accuracy

Fair_Value

Dep. Variable (1) (2) (3) (4)

Intercept 41.8111* -137.216 65.4363*** -91.5432

(2.1951) (-0.6485) (4.771) (-1.306)

Total Assets -26.7511** 14.405 -6.4498* 14.0793**

(-4.3211) (0.622) (-1.696) (2.119)

Firm Age -14.9159 -11.4821

(-0.956) (-0.932)

Property, plant & Equip. -1.7184** -0.2489** 0.0108 -0.0333

(-2.4289) (-2.278) (0.056) (-0.179)

Operating Profitability 0.4779** 0.0452 0.0114

(2.616) (0.342) (0.154)

Sales Growth 0.0249 -0.1519** -0.0116

(0.124) (-2.564) (-0.295)

Dividend Payout -0.1117 -0.2331

(-0.530) (-1.622)

Technology 10.6343 -17.8955*

(0.752) (-1.898)

Market Returns 1.7355** 0.5742** 0.1409

(2.963)

(2.288) (0.837)

Ex ante 4.0154 17.5754

(0.366) (1.839)*

Underwriter Reputation -16.6048 -4.2789

(-1.225) (-0.459)


(0.485) (0.675)

Adj. R-Square 0.7301 0.3521 0.25390 0.13275

F-Statistic 6.3123** 3.309** 1.578 1.029

Prob(F-Statistic) 0.002 0.044 0.1339 0.4296

N 15 20 65 88



252

Table A. 8: Empirical findings of basic valuation models

Panel-A: Full IPO Sample


Intercept 1.2438*** 1.6468***

9.9956 10.5149

Earnings 0.8296 1.8377**

1.5347 2.1922

D 0.2872** 0.5315**

2.3081 2.1810

Dividends 2.6308*** 4.9231***

3.1365 3.3355

Und_Contract -0.0088 -0.4231**

-0.0541 -2.1796

Adj. R2 0.128073 0.267279

F-Statistic 2.937702** 7.295537***

Wald-test 152.5427*** 158.7339

N 86 86

Panel-B: Non-Privatization IPO Sample


Intercept 1.2482*** 1.6349***

10.0407 9.9907

Earnings 0.6247 1.2762

1.1639 1.4377

D 0.2131*** 0.3228*

2.7446 1.8140

Dividends 2.5270** 4.6549**

2.3069 2.3390

Und_Contract 0.0160 -0.2924

0.0892 -1.3803

Adj. R2 0.11484 0.205175

F-Statistic 2.30286** 4.58195***

Wald-test 149.9320*** 130.8001***

N 78 78

253

Table A. 9: Cross-sectional Analysis of Full Sample Valuation Models

Variable Model 1 (FDCP/BV) Model 2 (FDCP/BV)

Fundamental Factors

Intercept (BV/BV) -3.6541* -3.4947

(-2.7298) (-1.6487)

Earnings (EPS/BV) -0.7769* -0.7972*

(-2.8134) (-2.8796)

D -0.2852 -0.2936

(-0.9180) (-0.9498)

Dividends (DPS/BV) 3.8685** 3.8313**

(2.2135) (2.1882)

Risk Factors

Financial Leverage 0.0012 0.0009

(0.2467) (0.1873)

Capital Availability Risk 0.0016 0.0022

(0.2969) (0.4042)

Efficiency Risk 0.0074** 0.0077**

(2.1028) (2.1959)


(-0.0051) (0.0530)

Firm Beta 0.0449** 0.0453**

(2.6713) (2.6886)

Offer Size 0.5114** 0.4822**

(2.1420) (2.0114)

Signal Factors

Underwriter Reputation 0.0026 0.0103

(0.0229) (0.0877)

Firm Age -0.0009 -0.0012

(-0.1215) (-0.1574)

Percentage of Shares

Offered 0.0147 0.0151

(1.1870) (1.2174)

Privatization - 0.2506

(0.6185)

Adj. R-square 0.39576 0.39814

F-Statistic 3.87531*** 3.56207***

N 86 86



254

Table A. 10: Equally and Value-weighted Monthly Returns using BHAR & CAR

Equally-Weighted Value-Weighted

Month BHARt CARt BHARt CARt

1 -0.0216 -0.0551 -0.0381 -0.0412

2 -0.0259 -0.0731 -0.0772 -0.0878

3 -0.0115 -0.0399 -0.0606 -0.0569

4 0.0077 -0.0226 0.0039 0.0229

5 -0.0022 -0.0411 -0.0862 -0.0422

6 -0.0281 -0.0534 -0.0907 0.0199

7 -0.0793 -0.0982 -0.1683 -0.0458

8 -0.1095 -0.1177 -0.1475 -0.0346

9 -0.1019 -0.1101 -0.1493 -0.0456

10 -0.0788 -0.1226 -0.0937 -0.0382

11 -0.0909 -0.1134 -0.0890 -0.0422

12 -0.0771 -0.1305 -0.1028 -0.0622

13 -0.0892 -0.1261 -0.1089 -0.0648

14 -0.1130 -0.1505 -0.1600 -0.1288

15 -0.1336 -0.1395 -0.0873 -0.0775

16 -0.1063 -0.1262 -0.0146 0.0550

17 -0.1148 -0.1515 -0.0039 0.0700

18 -0.1353 -0.1503 -0.0750 0.0349

19 -0.1383 -0.1515 -0.1096 -0.0013

20 -0.1444 -0.1479 -0.1021 -0.0128

21 -0.1363 -0.1656 -0.1038 -0.0157

22 -0.1503 -0.1798 -0.1249 -0.0104

23 -0.0916 -0.1551 -0.1297 0.0024

24 -0.1721 -0.1833 -0.1745 -0.0154

25 -0.2048 -0.1902 -0.1992 -0.0418

26 -0.1759 -0.1739 -0.2160 -0.0485

27 -0.1826 -0.2022 -0.2258 -0.0704

28 -0.1741 -0.2012 -0.2351 -0.0769

29 -0.1154 -0.1949 -0.2941 -0.1133

30 -0.0557 -0.2275 -0.3074 -0.1468

31 -0.1550 -0.2439 -0.3541 -0.1939

32 -0.1910 -0.2481 -0.3892 -0.1806

33 -0.2335 -0.2611 -0.4279 -0.2061

34 -0.1733 -0.2438 -0.4140 -0.2234

35 -0.1427 -0.2642 -0.4427 -0.2508

36 -0.2352 -0.2462 -0.4338 -0.2583

37 -0.2575 -0.2274 -0.4677 -0.2657

38 -0.2476 -0.2352 -0.4634 -0.2515

39 -0.3016 -0.2504 -0.5189 -0.2808

40 -0.2860 -0.2744 -0.4930 -0.2812

41 -0.3639 -0.3117 -0.4700 -0.3196

42 -0.3722 -0.3158 -0.4869 -0.3185

255

43 -0.3595 -0.2869 -0.4713 -0.2893

44 -0.4349 -0.2870 -0.5203 -0.2931

45 -0.3357 -0.2676 -0.4397 -0.2784

46 -0.3912 -0.2774 -0.4197 -0.2477

47 -0.4130 -0.3038 -0.3811 -0.2499

48 -0.3388 -0.3188 -0.3557 -0.2501

49 -0.4190 -0.3281 -0.4055 -0.2553

50 -0.4532 -0.3073 -0.4112 -0.2535

51 -0.5178 -0.3319 -0.4527 -0.2905

52 -0.5395 -0.3430 -0.3903 -0.2091

53 -0.6070 -0.3628 -0.3951 -0.2398

54 -0.6716 -0.3899 -0.4177 -0.2571

55 -0.5888 -0.3551 -0.4443 -0.2986

56 -0.5646 -0.3817 -0.4504 -0.3045

57 -0.5286 -0.3392 -0.4642 -0.2867

58 -0.6101 -0.3340 -0.5027 -0.3063

59 -0.5965 -0.2878 -0.4995 -0.2825

60 -0.6522 -0.2937 -0.5843 -0.3206

Figure A. 1: Equal- and Value-weighted Monthly returns using BHAR and CAR

-0.8000

-0.7000

-0.6000

-0.5000

-0.4000

-0.3000

-0.2000

-0.1000

0.0000

0.1000

0.2000

1 3 5 7 9 11131517192123252729313335373941434547495153555759

EWBHAR

VWBHAR

EWCAR

VWCAR

256

Figure A. 2: Year-wise Long run returns for year 1 using BHAR and CAR



-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

BHAR1

CAR1

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

BHAR2

CAR2

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

BHAR3

CAR3

257



-3.0000

-2.0000

-1.0000

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

6.0000

7.0000

8.0000

BHAR4

CAR4

-4.0000

-3.0000

-2.0000

-1.0000

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

6.0000

7.0000

BHAR5

CAR5

258

Figure A. 7: Sector-wise Long run returns for year 1, 3 & 5 using CAR

Figure A. 8: Sector-wise Long run returns for year 1, 3 & 5 using BHAR

-3.0000

-2.0000

-1.0000

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

BHAR1

BHAR3

BHAR5

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

CAR1

CAR3

CAR5

259

Table A. 11: Correlation Matrix of Variables used in LRR Models

Variable CAR1Y CAR3Y

CAR

5Y

BV/

OP

EPS/

OP Fin Lev Captl Rsk Eff Rsk Cpcty Rsk Firm Beta

Offer

Size Und Rep Firm Age POS Priv IERs

CAR3Y 0.563

5.41***

CAR5Y 0.489 0.815

4.45*** 11.17***

BV/ OP 0.044 0.240 0.239

0.35 1.96** 1.95**

EPS/ OP 0.340 0.493 0.403 0.455

2.87*** 4.51*** 3.5*** 4.06**

Fin Lev 0.033 0.005 0.064 0.180 -0.101

0.26 0.04 0.51 1.45 -0.81

Captl

Rsk -0.199 -0.219 -0.198 -0.042 -0.364 0.130

-1.61 -1.78* -1.60 -0.33 -3.11** 1.04

Eff Rsk -0.106 -0.168 -0.114 -0.178 -0.510 0.142 0.249

-0.84 -1.35 -0.91 -1.43 -4.71** 1.14 2.04**

Cpcty

Rsk 0.080 -0.027 -0.078 -0.186 -0.277 -0.084 0.246 0.113

0.63 -0.21 -0.62 -1.50 -2.29** -0.67 2.01** 0.90

Firm

Bbta 0.354 0.107 0.090 -0.266 0.236 0.043 -0.281 -0.181 -0.148

3.01*** 0.86 0.71 -2.1** *1.93 0.34 -2.32** -1.46 -1.19

Offer

Size 0.055 -0.132 -0.131 -0.213 0.114 0.184 -0.141 -0.169 -0.296 0.395

0.43 -1.05 -1.05 -1.73* 0.91 1.48 -1.13 -1.36 -2.46** 3.41***

Und

Rep -0.205 -0.076 -0.023 -0.142 -0.117 -0.150 0.008 0.106 0.201 -0.057 -0.161

-1.66 -0.61 -0.18 -1.14 -0.93 -1.20 0.07 0.85 1.62 -0.45 -1.29

260

Firm

Age 0.033 -0.034 -0.066 -0.068 0.105 0.101 -0.261 -0.116 -0.588 0.329 0.488 -0.208

0.266 -0.276 -0.525 -0.541 0.842 0.807 -2.14** -0.93 -5.77*** 2.76*** 4.44*** -1.69*

POS -0.061 -0.034 -0.034 -0.082 -0.066 -0.179 0.134 0.152 0.383 -0.055 -0.413 0.213 -0.390

-0.48 -0.27 -0.27 -0.65 -0.52 -1.44 1.07 1.22 3.29*** -0.44 -3.60*** 1.73* -3.36***

Priv 0.039 0.077 0.094 0.175 0.302 0.219 -0.318 -0.257 -0.403 0.346 0.577 -0.324 0.520 -0.319

0.31 0.61 0.75 1.41 2.51** 1.78* -2.67*** -2.11** -3.50*** 2.93*** 5.62*** -2.71*** 4.83*** -2.67**

IERs 0.127 -0.060 -0.024 -0.024 0.057 0.165 0.032 0.172 0.016 0.354 0.016 -0.185 -0.089 0.146 0.157

1.023 -0.478 -0.195 -0.193 0.453 1.333 0.256 1.393 0.128 3.008*** 0.134 -1.500 -0.711 1.172 1.270

Resi

-0.037 -0.060 -0.005 -0.278 -0.189 -0.073 0.035 0.003 0.048 -0.014 -0.031 -0.010 0.002 -0.024

-

0.151

-

0.112

-0.295 -0.479 -0.046 -2.3** -1.52 -0.58 0.282 0.023 0.381 -0.115 -0.249 -0.083 0.018 -0.198 -1.21 -0.89


261

Table A. 12: Regression Analysis of LRR Models using Full Sample

Variable CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

Fundamental Factors

Intercept 0.2316 1.9072 4.4199 2.9799 2.5394

(0.116) (0.533) (1.375) (0.945) (0.414)

Book Value (BV/OP) -0.3108*** -0.5179** -0.3160** -0.2668** -0.0820

(-2.99) (-3.213) (-2.472) (-2.411) (-0.515)

Earnings (EPS/OP) 1.5572** 4.1493*** 4.0465*** 3.9712*** 2.3118***

(2.464) (3.873) (2.914) (3.403) 2.331)

Risk Factors

Financial Leverage 0.0035** -0.0033 -0.0022 0.0013 -0.0064

(2.309) (-0.947) (-0.654) (0.191) (-1.126)

Capital Availability Risk 0.0006 0.0184** 0.0141** 0.0154*** 0.0276**

(0.147) (2.169) (2.373) (2.708) (2.211)

Efficiency Risk 0.0023 0.0014 -0.0007 -0.0055 -0.0006

(0.584) (0.222) (-0.182) (-1.070) (-0.072)

Capacity Risk 0.0053* 0.0080* 0.0023 0.0032 0.0041

(1.711) (1.781) (0.406) (0.648) (0.474)

Firm Beta 0.0162** -0.0107 -0.0174* -0.0246*** -0.0411***

(2.085) (-1.072) (-1.875) (-3.46) (-3.13)

Offer Size 0.0114 -0.3410 -0.6375* -0.4888 -0.5896

(0.050) (-0.801) (-1.707) (-1.291) (-0.800)

Signal Factors

Underwriter Reputation -0.6878** -0.0911 -0.4758 -0.1957 0.0897

(-2.772) (-0.241) (-1.136) (-0.574) (0.319)

Firm Age -0.0054 0.0134 0.0101 0.0104 0.0105

(-0.682) (1.361) (0.665) (0.738) (0.390)

Percentage of Shares Offered -0.0092 -0.0468** -0.0079 -0.0114 -0.0430**

(-0.805) (-2.065) (-0.951) (-0.918) (-2.015)

Initial Excess Returns -0.0041** -0.0018 -0.0037 0.0000 -0.0034

(-2.521) (-0.849) (-1.100) (0.001) (-0.636)

Residuals -0.2115** -0.4873** -0.1355 -0.0006 -0.0032

(-2.032) (-2.875) (-0.920) (-0.004) (-0.014)

Adj. R-square 0.36543 0.41774 0.37015 0.43021 0.32478

F-Statistic 2.171** 2.704*** 2.215** 2.845*** 1.813**

N 65 65 65 65 65



262

Table A. 13: Regression Analysis of LRR Models using Non-PIPO Sample

Variable CAR1Y CAR2Y CAR3Y CAR4Y CAR5Y

Fundamental Factors

Intercept 2.0607 4.3372 8.8667 8.5533 10.4274*

(0.938) (0.953) (1.323) (1.268) (1.695)

Book Value (BV/OP) -0.6195** -0.7017* -0.4252 -0.6007 -0.0897

(-2.485) (-1.784) (-0.605) (-0.898) (-0.357)

Earnings (EPS/OP) 1.7561** 4.4310*** 6.2795*** 6.7107*** 3.6182***

(2.409) (3.188) (3.206) (3.766) (4.136)

Risk Factors

Financial Leverage 0.0035 -0.0054 0.0011 0.0113 0.0059

(1.556) (-0.727) (0.113) (0.748) (0.800)

Capital Availability Risk 0.0015 0.0282** 0.0427*** 0.0470*** 0.0356**

(0.315) (2.621) (3.839) (3.873) (2.634)

Efficiency Risk -0.0010 -0.0091 -0.0129* -0.0144* -0.0179**

(-0.333) (-1.497) (-1.781) (-1.801) (-2.553)

Capacity Risk 0.0052 0.0136** 0.0165* 0.0196** 0.0102

(1.540) (2.653) (1.728) (2.269) (1.280)

Firm Beta 0.0143 -0.0029 -0.0212 -0.0389** -0.0433***

(1.351) (-0.204) (-0.999) (-2.165) (-2.81)

Offer Size -0.1806 -0.6703 -1.4913* -1.5802* -1.4772**

(-0.714) (-1.274) (-1.93) (-1.993) (-2.071)

Signal Factors

Underwriter Reputation -0.8780*** -0.0559 -0.6773 -0.1404 0.2827

(-3.28) (-0.105) (-0.979) (-0.211) (1.366)

Firm Age -0.0009 0.0218 0.0289 0.0149 -0.0227

(-0.092) (1.350) (0.814) (0.513) (-1.046)

Percentage of Shares Offered -0.0064 -0.0534** -0.0182 -0.0195* -0.0476**

(-0.547) (-2.111) (-1.327) (-1.682) (-2.315)

Initial Excess Returns -0.0049*** -0.0014 -0.0086* -0.0050 -0.0030

(-3.10) (-0.591) (-1.769) (-0.834) (-0.602)

Residuals -0.2405** -0.4971** -0.2568 0.0997 0.2657

(-2.53) (-2.484) (-1.026) (0.446) (1.408)

Adj. R-square 0.46106 0.42292 0.45913 0.45985 0.52469

F-Statistic 2.763*** 2.367** 2.742*** 2.751*** 3.566***

N 58 58 58 58 58



263

Table A. 14: Descriptive Statistics of Variables Used in Value-weighted Analysis

Variable Mean Min Percentiles

Max SD Obs. 25th 50th 75th

VWIPO_Rf 0.0067 -0.8236 -0.0818 -0.0105 0.0686 9.3853 0.2340 3,900

Rm_Rf 0.0071 -0.3853 -0.0180 0.0067 0.0516 0.3074 0.0780 3,900

SMBFF3F 0.0071 -1.5213 -0.0223 0.0086 0.0400 0.8702 0.0713 3,900

SMBFF5F 0.0071 -1.5213 -0.0222 0.0083 0.0390 0.8702 0.0679 3,900

HML 0.0062 -0.7036 -0.0235 0.0003 0.0304 0.8916 0.0619 3,900

RMW 0.0035 -0.3553 -0.0221 0.0044 0.0322 0.4820 0.0489 3,900

CMA -0.0016 -0.3231 -0.0265 -0.0013 0.0228 0.6573 0.0499 3,900

264

Table A. 15: Correlation matrix of variable used in value-weighted analysis

IPO_RF RM_RF SMBFF3F SMBFF5F HML CMA RMW

IPO_RF 1

RM_RF 0.3216*** 1

21.211

SMBFF3F -0.151*** -0.4907*** 1

-9.536 -35.160

SMBFF5F -0.1472*** -0.5006*** 0.9717*** 1

-9.295 -36.105 257.102

HML -0.0061 -0.0571*** 0.1159*** 0.1743*** 1

-0.382 -3.574 7.291 11.052

RMW -0.0555*** -0.1892*** 0.0862*** 0.0573*** -0.2714*** 1

-3.473 -12.032 5.407 3.584 -17.605

CMA 0.0618*** 0.0001 0.0153 -0.0097 -0.1491*** 0.2893*** 1

3.871 0.007 0.956 -0.608 -9.417 18.871


265

Table A. 16: Year-wise New-Listings and De-Listings in PSX

Year De-Listings New Listings PSX Listed Firms

1997 6 4 782

1998 11 1 779

1999 8 0 769

2000 6 3 762

2001 19 4 759

2002 40 4 725

2003 16 6 706

2004 57 17 666

2005 18 19 659

2006 19 9 658

2007 11 14 656

2008 11 10 656

2009 6 4 651

2010 13 6 651

2011 10 4 639

2012 69 4 573

2013 10 4 560

2014 9 6 557

2015 11 8 554

2016 0 4 558

Total 350 131 Source: PSX DataStream

Figure A. 9: The cumulative effect of New-Listings & De-Listings in PSX

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New Listings

Documents

Initial Public Offering Valuation Dynamics; Evidence …prr.hec.gov.pk/jspui/bitstream/123456789/11825/1/Thesis...Supervisor Dr. Muhammad Khalid Sohail Assistant Professor, Department