THE INFORMATION QUALITY OF DERIVATIVE DISCLOSURES …Mohamat Sabri Hassan B.Ac (Hons.) (Universiti Kebangsaan Malaysia), ... This thesis investigates the transparency or information

THE INFORMATION QUALITY OF DERIVATIVE DISCLOSURES IN CORPORATE ANNUAL REPORTS OF AUSTRALIAN FIRMS IN THE

EXTRACTIVE INDUSTRIES

Mohamat Sabri Hassan B.Ac (Hons.) (Universiti Kebangsaan Malaysia),

M.Social Science (Southampton)

A dissertation submitted for the degree of Doctor of Philosophy within the School of Accountancy at Queensland University of Technology

2004

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Keywords: Disclosure quality; transparency; disclosure index; financial instruments;

derivative instruments; market value; extractives industries.

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ABSTRACT

Recent events in the business world have focused attention on the importance of high

quality financial reporting. Of particular interest is where the collapse of prominent

companies such as Baring Plc. was due to the company’s involvement with derivative

instruments. In Australia, some derivative instruments are not recognised in the

balance sheet. However, the Australian accounting standard AASB 1033 Presentation

and Disclosure of Financial Instruments requires extensive disclosures to overcome

the lack of guidance with regard to the recognition and measurement. Therefore,

AASB 1033 may be regarded as a high quality disclosure standard.

This thesis investigates the transparency or information quality of derivative

disclosures of Australian firms in the extractive industries using 1998 to 2001

financial reports. The extractive industries play a major role in the Australian

economy, where they generated exports worth more than A$30billion in 2000 to 2002

(Department of Foreign Affairs and Trade, 2003a and 2003b). Further, firms in the

extractive industries extensively use derivative instruments for hedging purposes

(Berkman, Bradbury, Hancock and Innes, 1997). The objective of this study is, first,

to examine the relationship between the transparency or disclosure quality of

derivative information and firm characteristics. Second, this study investigates the

value relevance of derivative disclosures in particularly hedge information, net fair

value information and risk information. Quality is measured based on a disclosure

index developed from AASB 1033 Presentation and Disclosure of Financial

Instruments. A finding of concern is that the majority of firms in this study provide

less than complete information and therefore enforcement power is required to ensure

compliance (Kothari, 2000)

Prior studies have related disclosure quality of accounting information with firm

characteristics but no attempt has been made to relate those characteristics with the

disclosure quality of derivative instruments. The current study contributes to the

literature by examining the relationship between firm characteristics and the quality of

derivative disclosures. Firm characteristics investigated are size, profitability, price-

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earnings ratio, market-to-book ratio, research and development activity, auditor, debt-

to-equity ratio and type of extractive firm. This study finds that the variables, firm

size, price-earnings and debt-to-equity ratios are associated with the disclosure quality

of derivative information. To a lesser extent, the variables, market-to-book ratio and

profitability, are also associated with disclosure quality.

High disclosure quality has been argued to lead to a reduction in the cost of debt

(Sengupta, 1998) and equity (Botosan, 1997), resulting in higher security prices

(Miller and Bahnson, 2002). The results of this study indicate that high quality

derivative information, as represented by the disclosure index, is value relevant.

Market participants do consider hedge information and risk information components

as important for decision-making. However, examining the specific information

disclosed in the financial statements indicate that some of the disclosed information

such as the unrealised gain or loss on financial assets and liabilities and off-balance

sheet derivative financial instruments are not significant.

These results contribute to the value relevance literature as this study focuses on the

extractive industries which have been neglected in the literature. This study provides

important information for standard setters and regulators for future directions in

developing accounting standards and is particularly relevant for the impending

adoption of International Accounting Standards.

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Table of Contents

Page

ABSTRACT ......................................................................................................................................... III ACKNOWLEDGEMENTS ...............................................................................................................XII CHAPTER 1 INTRODUCTION .....................................................................................................1

1.1 PURPOSE OF THE STUDY.........................................................................................................1 1.2 PRIOR RESEARCH AND THE MAIN FINDINGS OF THE THESIS...................................................3 1.3 MOTIVATION..........................................................................................................................6 1.4 PLAN OF THESIS .....................................................................................................................9

CHAPTER 2 INSTITUTIONAL BACKGROUND .....................................................................12 2.1 RISK MANAGEMENT.............................................................................................................12

2.1.1 To Hedge or Not to Hedge?............................................................................................14 2.2 INTERNATIONAL ACCOUNTING PRACTICES ..........................................................................16

2.2.1 Financial Accounting Standard Board ...........................................................................17 2.2.1.1 SFAS 133 Accounting for Derivative Instruments and Hedging Activities. ............................ 19

2.2.2 International Accounting Standards Board ....................................................................20 2.2.2.1 IAS 32 Financial Instruments: Disclosure and Presentation..................................................... 20 2.2.2.2 IAS 39: Financial Instruments: Recognition and Measurement ............................................... 22

2.2.3 Accounting for Financial Instruments in Australia ........................................................23 2.2.3.1 AASB 1033 Presentation and Disclosure of Financial Instruments ......................................... 23

2.3 FAIR VALUE ACCOUNTING...................................................................................................31 2.4 STUDIES OF ACCOUNTING PRACTICES IN AUSTRALIA ..........................................................35 2.5 RISK MANAGEMENT PRACTICES IN THE EXTRACTIVE INDUSTRIES ......................................37 2.6 SUMMARY............................................................................................................................39

CHAPTER 3 LITERATURE REVIEW: DISCLOSURE QUALITY........................................41 3.1 DISCLOSURE QUALITY: THE DEFINITIONS ...........................................................................42 3.2 DISCLOSURE QUALITY: REGULATION, ENFORCEMENT AND COMPLIANCE...........................44 3.3 STUDIES ON DISCLOSURE QUALITY .....................................................................................46

3.3.1 Disclosure Quality of Accounting Standards..................................................................46 3.3.2 Disclosure Quality of Accounting Information and the Impact on Firms.......................48

3.3.2.1 Disclosure Quality and Firms Characteristics .......................................................................... 48 3.3.2.2 Disclosure Quality of Specific Information.............................................................................. 52

3.3.3 Disclosure Quality of Accounting Information and Benefits to the Investors.................54 3.3.4 Disclosure Quality of Derivative Information ................................................................57

3.4 SUMMARY............................................................................................................................59 CHAPTER 4 LITERATURE REVIEW: DERIVATIVE DISCLOSURES AND VALUE RELEVANCE STUDIES.....................................................................................................................60

4.1 DISCLOSURE QUALITY OF ACCOUNTING INFORMATION AND INVESTORS’ DECISIONS .........60 4.2 DISCLOSURE AND CAPITAL MARKETS RESEARCH................................................................62

4.2.1 Capital Markets Research ..............................................................................................62 4.2.2 Relevance and Reliability of Accounting Information and Capital Markets Research...65 4.2.3 Value Relevance Studies .................................................................................................68

4.2.3.1 Other Value Relevance Studies ................................................................................................ 69 4.2.3.2 Research on Value Relevance in Australia............................................................................... 70

4.3 VALUE RELEVANCE OF FINANCIAL INSTRUMENTS...............................................................73 4.3.1 Studies on Value Relevance of Fair Value Disclosures..................................................73 4.3.2 Studies on Value Relevance of Derivative Financial Instruments Disclosures ..............76

4.4 SUMMARY............................................................................................................................78 CHAPTER 5 RESEARCH QUESTIONS.....................................................................................79

5.1 DISCLOSURE QUALITY OF DERIVATIVE INFORMATION AND FIRM CHARACTERISTICS .........81

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5.1.1 Size..................................................................................................................................83 5.1.2 High Performance Firms................................................................................................84 5.1.3 Auditor............................................................................................................................85 5.1.4 Type of Firm in the Extractive Industries. ......................................................................85

5.2 VALUE RELEVANCE OF DERIVATIVE DISCLOSURES .............................................................86 5.2.1 Disclosure Quality and the Market Value of Firms........................................................86 5.2.2 Value Relevance of Disclosure of Hedges of Anticipated Transactions .........................88 5.2.3 Value Relevance of Fair Value Disclosures ...................................................................91

5.3 SUMMARY............................................................................................................................92 CHAPTER 6 RESEARCH DESIGN, DATA COLLECTION AND DESCRIPTIVE STATISTICS ...................................................................................................................................93

6.1 DATA SELECTION AND TEST PERIOD....................................................................................93 6.2 SPECIFICATION OF VARIABLES AND MODEL DEVELOPMENT................................................98

6.2.1 Disclosure Quality and Firm Characteristics (Firm Characteristics Model) ................99 6.2.1.1 Variables .................................................................................................................................. 99 6.2.1.2 Regression Model................................................................................................................... 107

6.2.2 Value Relevance of Derivative Disclosures (Market Value Model) .............................108 6.2.2.1 Dependent Variable................................................................................................................ 112 6.2.2.2 Independent Variables............................................................................................................ 112

6.2.3 Incremental Explanatory Power of the Net Fair Value and the Unrealised Gain or Loss on Financial Instruments Beyond the Book Value of Financial and Non-Financial Instruments and Earnings Valued at Historical Cost .....................................................................................126

6.2.3.1 Incremental Explanatory Power of Net Fair Value................................................................. 126 6.2.3.2 Incremental Explanatory Power of the Unrealised Gain or Loss on Financial Instruments ... 128

6.3 ESTIMATION PROCEDURES .................................................................................................130 6.4 DESCRIPTIVE STATISTICS ...................................................................................................132

6.4.1 Firm Characteristics Model .........................................................................................132 6.4.2 Market Value Models....................................................................................................135

6.4.2.1 Value Relevance of Disclosure Quality.................................................................................. 135 6.4.2.2 Value Relevance of Hedge Transaction, Net Fair Value and the Unrealised Gain or Loss on Financial Instruments ............................................................................................................................. 136

6.5 SUMMARY..........................................................................................................................136 CHAPTER 7 RESULTS: DISCLOSURE QUALITY AND FIRM CHARACTERISTICS...138

7.1 DIAGNOSTIC TESTS ............................................................................................................138 7.1.1 Normality Test ..............................................................................................................139 7.1.2 Autocorrelation Test .....................................................................................................139 7.1.3 Heteroscedasticity Test.................................................................................................140 7.1.4 Multicollinearity Test ...................................................................................................141

7.2 DESCRIPTIVE RESULTS.......................................................................................................143 7.2.1 Firms’ Disclosure Scores .............................................................................................143 7.2.2 Disclosure Components ................................................................................................145

7.3 VALIDITY OF THE DISCLOSURE QUALITY SCORE (DISCLOSURE INDEX).............................148 7.4 MULTIPLE REGRESSION RESULTS ......................................................................................153

7.4.1 Standard Regression Procedures..................................................................................153 7.4.2 Sensitivity Analyses.......................................................................................................155

7.4.2.1 Ranked Regression................................................................................................................. 155 7.4.2.2 Profit Vs Loss Making Firms ................................................................................................. 157

7.5 DISCUSSION AND ANALYSIS...............................................................................................160 7.5.1 Disclosure Quality ........................................................................................................160 7.5.2 Comparison with Prior Studies ....................................................................................162

7.6 SUMMARY..........................................................................................................................166 CHAPTER 8 RESULTS: VALUE RELEVANCE OF DERIVATIVE DISCLOSURES .......168

8.1 DIAGNOSTIC TESTS ............................................................................................................169 8.1.1 Normality Tests.............................................................................................................169 8.1.2 Autocorrelation Tests ...................................................................................................170 8.1.3 Heteroscedasticity Tests ...............................................................................................170 8.1.4 Multicollinearity Test ...................................................................................................171

8.2 MULTIPLE REGRESSION RESULTS ......................................................................................176

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8.2.1 Disclosure Quality of Derivative Information and the Market Value of Firms ............176 8.2.2 Value Relevance of Hedge Disclosures ........................................................................179 8.2.3 Value Relevance of Net Fair Value Disclosures...........................................................180 8.2.4 Value Relevance of the Unrealised Gain or Loss of Financial Instruments .................183

8.3 INCREMENTAL EXPLANATORY POWER OF NET FAIR VALUE AND THE UNREALISED GAIN OR LOSS OF FINANCIAL INSTRUMENTS ..................................................................................................186 8.4 DISCUSSION OF THE RESULTS.............................................................................................189

8.4.1 Disclosure Quality of Derivative Information and the Market Value of Firms ............191 8.4.2 Value Relevance of Hedge Disclosures ........................................................................192 8.4.3 Value Relevance and the Incremental Explanatory Power of Net Fair Value Disclosure and the Unrealised Gain or Loss of Financial Instruments........................................................192

8.5 SUMMARY..........................................................................................................................195 CHAPTER 9 SUMMARY AND CONCLUSIONS ....................................................................196

9.1 SUMMARY..........................................................................................................................196 9.1.1 Firm Characteristics Model .........................................................................................196 9.1.2 Market Value Model .....................................................................................................199

9.2 CONTRIBUTIONS OF THE STUDY .........................................................................................202 9.3 LIMITATIONS......................................................................................................................203 9.4 DIRECTIONS FOR FUTURE RESEARCH.................................................................................205

APPENDIX A: AUSTRALIAN FIRMS IN THE EXTRACTIVE INDUSTRIES LISTED ON THE ASX IN 1998 TO 2001 ..............................................................................................................206 APPENDIX B: FIRM CHARACTERISTICS MODEL YEAR-BY-YEAR AND AVERAGE FOUR YEARS ANALYSES..............................................................................................................218 APPENDIX C: RESULTS ON MARKET VALUE MODEL FOR YEAR-BY-YEAR ANALYSIS..............................................................................................................................................................223 APPENDIX D: FURTHER TEST.....................................................................................................230 APPENDIX E: FIRM CHARACTERISTICS MODEL ESTIMATION WITHOUT THE OUTLIERS (REFINED DATA)........................................................................................................247 APPENDIX F: RESULTS ON MARKET VALUE MODEL FOR FULL DATA........................252 REFERENCES ...................................................................................................................................262

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Table of Tables and Figure

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TABLE 2.1: FASB FINANCIAL INSTRUMENTS ACCOUNTING PRONOUNCEMENTS ....................................18 TABLE 2.2: SUMMARY OF DEVELOPMENT OF ACCOUNTING PRONOUNCEMENTS RELATED TO FINANCIAL

INSTRUMENTS IN AUSTRALIA ........................................................................................................24 TABLE 2.3: AASB 1033 VS RELATED FASB AND IAS STANDARDS..............................................28 TABLE 6.1: THE USE OF DERIVATIVE FINANCIAL INSTRUMENTS FOR HEDGING PURPOSES.....................97 TABLE 6.2: SUMMARY OF DATA SELECTION PROCEDURE .......................................................................98 TABLE 6.3: COMPONENTS OF DERIVATIVE DISCLOSURE INDEX ............................................................101 TABLE 6.4: SUMMARY OF INDEPENDENT VARIABLES EMPLOYED IN THE MARKET VALUE MODELS ....113 TABLE 6.5: DESCRIPTIVE STATISTICS AND CORRELATION MATRIX: FIRM CHARACTERISTICS MODEL .134 TABLE 6.6: DESCRIPTIVE STATISTICS: VALUE RELEVANCE OF DISCLOSURE QUALITY (N=253) ...........135 TABLE 6.7: DESCRIPTIVE STATISTICS: VALUE RELEVANCE OF HEDGE TRANSACTION, NET FAIR VALUE

AND UNREALISED GAIN OR LOSS ON FINANCIAL INSTRUMENTS. (N=253)...................................137 TABLE 7.1: CORRELATION COEFFICIENTS BETWEEN VARIABLES ..........................................................142 TABLE 7.2: NUMBER OF FIRMS THAT REPORT ALL INFORMATION REQUIRED BY AASB 1033 (100%

DISCLOSURE) ...............................................................................................................................143 TABLE 7.3: DISCLOSURE QUALITY OF FIRMS IN THE AUSTRALIAN EXTRACTIVE INDUSTRIES..............144 TABLE 7.4: DESCRIPTIVE STATISTICS OF DISCLOSURE COMPONENTS (POOLED SAMPLE) .....................145 TABLE 7.5: MEAN DISCLOSURE COMPONENTS OF USER FIRMS FOR THE PERIOD 1998 TO 2001 ...........147 TABLE 7.6: CORRELATION COEFFICIENTS BETWEEN VARIABLES ..........................................................151 TABLE 7.7: DISCLOSURE QUALITY OF DERIVATIVE INFORMATION OF EXTRACTIVE FIRMS ..................152 TABLE 7.8 :RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS (N=260) ............................................................154 TABLE 7.9: RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS: RANKED TRANSFORMATION (N=260)..............156 TABLE 7.10: RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS. (N=260) ...........................................................158 TABLE 7.11: RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS. .........................................................................159 TABLE 7.12: RESULTS ON THE ASSOCIATION BETWEEN DISCLOSURE QUALITY AND FIRM

CHARACTERISTICS.......................................................................................................................163 TABLE 8.1:NORMALITY TEST OF VALUE RELEVANCE MODELS ............................................................169 TABLE 8.2: CORRELATION COEFFICIENTS BETWEEN VARIABLES ..........................................................171 TABLE 8.3: CORRELATION COEFFICIENTS BETWEEN VARIABLES: VALUE RELEVANCE OF HEDGE

DISCLOSURES ..............................................................................................................................173 TABLE 8.4: CORRELATION COEFFICIENTS BETWEEN VARIABLES: VALUE RELEVANCE OF NET FAIR

VALUE.........................................................................................................................................174 TABLE 8.5: CORRELATION COEFFICIENTS BETWEEN VARIABLES: VALUE RELEVANCE OF UNREALISED

GAIN OR LOSS OF FINANCIAL INSTRUMENTS ...............................................................................175 TABLE 8.6: THE ASSOCIATION BETWEEN INFORMATION QUALITY OF DERIVATIVE DISCLOSURES AND

THE MARKET VALUE OF FIRMS (N=253)1 ....................................................................................177 TABLE 8.7: THE ASSOCIATION BETWEEN HEDGE DISCLOSURE AND MARKET VALUE OF THE FIRMS

(N=253)1......................................................................................................................................180 TABLE 8.8: THE ASSOCIATION BETWEEN NET FAIR VALUE AND MARKET VALUE (N=253)..................182 TABLE 8.9: THE ASSOCIATION BETWEEN THE MARKET VALUE OF FIRMS AND THE DIFFERENCE

BETWEEN NET FAIR VALUE AND BOOK VALUE OF FINANCIAL INSTRUMENTS (N=253)1.............184 TABLE 8.10: THE INCREMENTAL EXPLANATORY POWER OF NET FAIR VALUE BEYOND THE BOOK VALUE

OF FINANCIAL AND NON-FINANCIAL INSTRUMENTS AND EARNINGS VALUED AT THE HISTORICAL COST............................................................................................................................................187

TABLE 8.11: THE INCREMENTAL EXPLANATORY POWER OF UNREALISED GAIN OR LOSS OF FINANCIAL INSTRUMENTS BEYOND THE BOOK VALUE OF FINANCIAL AND NON-FINANCIAL INSTRUMENTS AND EARNINGS VALUED AT THE HISTORICAL COST (N=253)..............................................................188

TABLE 8.12: SUMMARY OF RESULTS OF VALUE RELEVANCE MODELS .................................................190 TABLE A 1: LISTED AUSTRALIAN FIRMS IN THE EXTRACTIVE INDUSTRIES ...........................................207 TABLE A 2: LIST OF DATA FIRMS IN THE STUDY...................................................................................215

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TABLE A 3: COMPONENTS OF DERIVATIVE DISCLOSURE INDEX FOR BHP BILLITON (2001).................217 TABLE B 1: THE ASSOCIATION BETWEEN FIRMS CHARACTERISTICS AND DISCLOSURE QUALITY ON A

YEAR-BY-YEAR BASIS AND AN AVERAGE OF FOUR YEARS DATA (N=65) ..................................220 TABLE B 2: RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS: RANKED TRANSFORMATION YEAR-BY-YEAR BASIS AND AN AVERAGE OF FOUR YEARS DATA (N=65).............................................................222

TABLE C 1: THE ASSOCIATION BETWEEN THE INFORMATION QUALITY OF DERIVATIVES DISCLOSURES

AND THE MARKET VALUE OF THE FIRMS: YEAR-BY-YEAR ANALYSIS ........................................225 TABLE C 2: THE ASSOCIATION BETWEEN HEDGE DISCLOSURES AND THE MARKET VALUE OF THE FIRMS:

YEAR-BY-YEAR ANALYSIS..........................................................................................................226 TABLE C 3:THE ASSOCIATION BETWEEN NET FAIR VALUE AND MARKET VALUE: YEAR-BY-YEAR

ANALYSIS ....................................................................................................................................227 TABLE C 4: THE ASSOCIATION BETWEEN THE MARKET VALUE OF FIRMS AND THE DIFFERENCE BETWEEN

NET FAIR VALUE AND BOOK VALUE OF FINANCIAL INSTRUMENTS (UNREALISED GAIN OR LOSS)....................................................................................................................................................228

TABLE D 1: THE ASSOCIATION BETWEEN INFORMATION QUALITY OF DERIVATIVE DISCLOSURES AND

THE MARKET VALUE OF THE FIRMS (N=156)1 .............................................................................232 TABLE D 2: THE ASSOCIATION BETWEEN HEDGE DISCLOSURE AND THE MARKET VALUE OF THE FIRMS

(N=156)1......................................................................................................................................233 TABLE D 3: THE ASSOCIATION BETWEEN NET FAIR VALUE AND MARKET VALUE (N=156).................234 TABLE D 4: THEASSOCIATION BETWEEN THE MARKET VALUE OF FIRMS AND THE DIFFERENCE BETWEEN

NET FAIR VALUE AND BOOK VALUE OF FINANCIAL INSTRUMENTS (N=156) ..............................235 TABLE D 5:THE ASSOCIATION BETWEEN THE INFORMATION QUALITY OF DERIVATIVES DISCLOSURES

AND THE MARKET VALUE OF THE FIRMS: YEAR-BY-YEAR ANALYSIS ........................................236 TABLE D 6: THE ASSOCIATION BETWEEN HEDGE DISCLOSURES AND THE MARKET VALUE OF THE FIRMS:

YEAR-BY-YEAR ANALYSIS..........................................................................................................238 TABLE D 7: THE ASSOCIATION BETWEEN NET FAIR VALUE AND MARKET VALUE: YEAR-BY-YEAR

ANALYSIS ....................................................................................................................................239 TABLE D 8: THE ASSOCIATION BETWEEN THE MARKET VALUE OF FIRMS AND THE DIFFERENCE

BETWEEN NET FAIR VALUE AND BOOK VALUE OF FINANCIAL INSTRUMENTS ............................240 TABLE D 9: INCREMENTAL EXPLANATORY POWER OF NET FAIR VALUE BEYOND THE BOOK VALUE OF

FINANCIAL AND NON-FINANCIAL INSTRUMENTS AND EARNINGS VALUED AT THE HISTORICAL COST............................................................................................................................................242

TABLE D 10: INCREMENTAL EXPLANATORY POWER OF UNREALISED GAIN OR LOSS ON FINANCIAL INSTRUMENTS BEYOND THE BOOK VALUE OF FINANCIAL AND NON-FINANCIAL INSTRUMENTS AND EARNINGS VALUED AT THE HISTORICAL COST............................................................................243

TABLE E 1: RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS (N=254) ............................................................248 TABLE E 2: THE ASSOCIATION BETWEEN FIRMS CHARACTERISTICS AND DISCLOSURE QUALITY ON A

YEAR-BY-YEAR BASIS AND AN AVERAGE OF FOUR YEARS DATA ..............................................249 TABLE E 3: RESULTS OF REGRESSION ANALYSIS OF THE ASSOCIATION BETWEEN DISCLOSURE

TRANSPARENCY AND FIRMS CHARACTERISTICS: RANKED TRANSFORMATION (N=254)..............250 TABLE E 4: REFINED DATA: ASSOCIATION BETWEEN DISCLOSURE TRANSPARENCY AND FIRMS

CHARACTERISTICS: RANKED TRANSFORMATION YEAR-BY-YEAR BASIS AND AN AVERAGE OF FOUR YEARS DATA .....................................................................................................................251

TABLE F 1: THE ASSOCIATION BETWEEN INFORMATION QUALITY OF DERIVATIVE DISCLOSURES AND

THE MARKET VALUE OF THE FIRMS (N=260)1 .............................................................................253 TABLE F 2: THE ASSOCIATION BETWEEN INFORMATION QUALITY OF DERIVATIVE DISCLOSURES AND

THE MARKET VALUE OF THE FIRMS – RANKED BASED ON LARGE AND SMALL (N=128) .............254 TABLE F 3: THE ASSOCIATION BETWEEN INFORMATION QUALITY OF DERIVATIVE DISCLOSURES AND

THE MARKET VALUE OF THE LARGE AND SMALL FIRMS .............................................................255 TABLE F 4: THE ASSOCIATION BETWEEN INFORMATION QUALITY OF DERIVATIVES DISCLOSURES AND

MARKET VALUE OF THE FIRMS: YEAR-BY-YEAR ANALYSIS (N=65) ...........................................256

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TABLE F 5: THE ASSOCIATION BETWEEN HEDGE DISCLOSURE AND MARKET VALUE OF THE FIRMS: YEAR-BY-YEAR ANALYSIS (N=65)..............................................................................................257

TABLE F 6: THE ASSOCIATION BETWEEN NET FAIR VALUE AND MARKET VALUE: YEAR-BY-YEAR ANALYSIS (N=65) ........................................................................................................................258

TABLE F 7: THE ASSOCIATION BETWEEN MARKET VALUE OF FIRMS AND DIFFERENCE BETWEEN NET FAIR VALUE AND BOOK VALUE OF FINANCIAL INSTRUMENTS (N=65) ........................................259

TABLE F 8: INCREMENTAL EXPLANATORY POWER OF NET FAIR VALUE BEYOND BOOK VALUE OF FINANCIAL AND NON-FINANCIAL INSTRUMENTS AND EARNINGS VALUED AT HISTORICAL COST....................................................................................................................................................260

TABLE F 9: INCREMENTAL EXPLANATORY POWER OF UNRECOGNISED GAIN OR LOSS BEYOND BOOK VALUE OF FINANCIAL AND NON-FINANCIAL INSTRUMENTS AND EARNINGS VALUED AT HISTORICAL COST .......................................................................................................................261

FIGURE 5.1: DIAGRAM ILLUSTRATING THE RESEARCH QUESTIONS.........................................................80

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THE STATEMENT OF ORIGINAL AUTHORSHIP

“The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made” Signed : ______________________________ Date : _____________________________

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ACKNOWLEDGEMENTS

I am deeply indebted to my supervisor, Dr. Majella Percy, for her excellent direction

and support provided to me throughout all stages of this dissertation. I am also very

grateful to my associate supervisor, Prof. Roger Willett, for his helpful comments and

support.

I would like to acknowledge Universiti Kebangsaan Malaysia and the Malaysian

Government for their moral support and assistance with a scholarship to undertake

study leave to do my Doctorate at the Queensland University of Technology. I would

also like to acknowledge the advice and support provided to me by Prof. Greg Clinch,

Assoc. Prof. Jenny Goodwin, Helen Kang, Christine Tan, Suzanna Yuen, Wilson

Tong and participants at AFAANZ 2003 in Brisbane, Doctorate Business Students

Symposium at the Queensland University of Technology and the APJAE Symposium

2004 in Kuala Lumpur.

I am particularly indebted to my fellow PhD students, Eko Suwardi, Steve Su,

Victoria Clout, Chun-Wei Huang and Teruyo Omura for their insightful comments

and support offered to me over this long process. I also would like to thank Danielle

Horton for providing me with some of the share price data.

Finally, I wish to express my appreciation for the encouragement and support of my

wife Anida Sidek, my children Muhammad Ameer Zharfan, Hanis Afifah, Hanis

Nadhirah, Hanis Aqilah Aiman and Muhammad Ammar Zhakwan. To my parents

Hassan Mat and Siti Fatimah Bidin and parents in-law Sidek Abd Rashid and Mariam

Marzuki, I would not be here without their love and support.

Chapter 1: Introduction

1

CHAPTER 1 INTRODUCTION

1.1 Purpose of the Study The objective of this study is, first to examine the relationship between the

transparency, or disclosure quality, of derivative information and firm characteristics.

Second, this study investigates the value relevance of derivative disclosures in

particular hedge information, net fair value information and risk information.

Derivative financial instruments are financial instruments whose value is derived from

the value of the underlying asset, liability, interest rate, index, or a hedge. Most of

these instruments, such as interest rate swaps and option contracts, are executory

contracts1 and require either no initial cash outlay or only a small initial outlay

(Johnson and Swieringa, 1996; Wilson and Smith, 1997).

Firms use derivative financial instruments to manage (hedge) exposure to foreign

exchange risk, interest rate risk and commodity price changes. However, due to their

nature, these instruments are not recognised as assets and liabilities in the balance

sheet and nor is the unrealised gain or loss recorded in the income statement.

Nevertheless, information about them (voluntary and mandatory) is disclosed in the

notes to the financial statements to enhance financial statement users’ understanding

of the significance of these derivative financial instruments and the associated risks.

To examine the significance of recognised and unrecognised financial information in

decision-making, researchers examine the quality of the information. The U.S.

1 A contract under which the obligations of both parties to the contract are so far unperformed and the failure of either to complete performance would constitute a material breach excusing the performance of the other (U.S. Department of Justice, 1998).


2

Security Exchange Commission (SEC) assesses the quality of financial statements of

cross-listed firms (non-U.S. firms listed on U.S. exchanges) based on three criteria:

transparency, comparability and full disclosure. However, the existing research in this

area investigates the quality of the information directly by studying the comparability

of non-U.S. Generally Accepted Accounting Principles (GAAP) to U.S. GAAP and

thus measuring indirectly the transparency of disclosures (Pownall and Schipper,

1999).

Prior studies indicate that disclosure quality (referred to in some studies as disclosure

level) is related to firm characteristics. This association has been linked to explanatory

variables from the research on agency costs, political costs, corporate governance and

information asymmetry (Ahmed and Courtis, 1999). Several studies investigate the

quality of accounting information based on the impact of corporate disclosure

practices and the usefulness of the information in decision-making. These studies

measure quality of disclosure based on the perception of the users, such as financial

analysts, shareholders, creditors and researchers, on the accounting numbers, and the

association of accounting information with share prices. In the association studies,

researchers examine the value relevance of accounting information, where value

relevance refers to the information being related to equity value.

A disclosure index is used in this thesis as a measure of disclosure quality. Five

categories of information required by AASB 1033 Presentation and Disclosure of

Financial Instruments are used to develop the index. These are disclosures of

accounting policy, hedges of anticipated transaction, risk information, net fair value

information and commodity contracts which are regarded as financial instruments. A


3

score is given for each item disclosed in each category. To make each category add

equally to the disclosure index, the score category is divided by the number of items

in each component of the index.

This study addresses the questions of whether the disclosure quality of derivative

information is related to specific firm characteristics and whether this quality is

perceived as an important factor in firm valuation (i.e. value relevant). This study

adds to the literature on the disclosure quality of derivative information in the

specialised setting of firms in the extractive industries.

The next section discusses prior research and the main findings of this thesis. Section

1.3 describes the motivation for this study. The structure of the thesis is outlined in

section 1.4.

1.2 Prior Research and the Main Findings of the Thesis

Prior studies indicate that disclosure quality is associated with certain firm

characteristics. Studies, dating back to 1971, have been providing evidence that a)

size of the firms (Singhvi and Desai, 1971; Firth, 1979; Cooke, 1989 and 1991;

Wallace, Naser and Mora, 1994; Ahmed and Nicholls, 1994; Riahi-Belkaoui, 2001;

Ali, Ahmed and Henry, 2003), b) auditor (Singhvi and Desai, 1971; Ahmed and

Nicholls, 1994; Wallace and Naser, 1995) and c) performance of the firm (Ali et al.,

2003) are positively related to disclosure quality. However, specific to the oil and gas

industries, Malone, Fries and Jones (1993) indicate that there is no association

between the size of the firm and auditor choice (Big 5 or Non-big 5) and disclosure

quality.


4

There is no direct evidence available on the association between disclosure quality

and the market value of the firm. Lang, Ready and Yetman (2003) and Gelb and

Zarowin (2002) provide indirect evidence on the association between disclosure

quality and share prices. Lang et al. (2003) provide evidence on the association

between disclosure quality and share prices based on the association between

accounting data (earnings) and share price. Gelb and Zarowin (2003) provide

evidence that the corporate level of disclosure is associated with share prices since

high disclosure firms have higher earnings response coefficients than low disclosure

firms.

The value relevance of financial instruments has been examined in the U.S. These

studies examine the value relevance of fair value under different accounting

standards. Barth (1994), Eccher, Ramesh and Thiagarajan (1996), Barth, Beaver and

Landsman (1996) and Park, Park and Ro (1999) provide evidence on the value

relevance of banks’ fair value disclosures under SFAS 107 Disclosures about Fair

Value of Financial Instruments. Simko (1999), on the other hand, extends the research

to non-financial firms. Venkatachalam (1996) extends these studies by examining the

implications of fair value disclosures under SFAS 119 Disclosure about Derivative

Financial Instruments and Fair Value of Financial Instruments. Mixed results are

reported.

The results of the current study indicate that the quality of derivative disclosures

among firms in the extractive industries has increased since the accounting standard,

AASB 1033 Presentation and Disclosure of Financial Instruments, was applicable.


5

However, firms still use discretion in the disclosure of derivative information,

especially in relation to net fair value. Overall, the multivariate analysis indicates that

larger firms tend to provide transparent derivative information within the extractive

industries. These findings hold for both the ranked regression technique and the

average of four years’ data (see Appendix B). Other variables significantly related to

derivative disclosure quality include the price-earnings ratio, profitability, market-to-

book ratio and the debt-to-equity ratio (leverage).

The multiple regression results from the market value models indicate that market

participants regard derivative information as value relevant. However, when

comparing net fair value information with other derivative information components,

i.e. hedge information and risk information components, the net fair value information

component is not value relevant. The incremental explanatory power of net fair value

and the unrealised gain or loss of financial instruments is very low compared to the

incremental explanatory power of the book value of financial and non-financial

instruments and earnings valued at historical costs. Nevertheless, the incremental

explanatory power of net fair value and the unrealised gain or loss on financial

instruments beyond book value of financial and non-financial instruments and

earnings valued at historical costs has increased from 1998 to 2001. However, the

opposite direction is reported for the incremental explanatory power of book value of

financial and non-financial instruments and earnings valued at historical cost. To a

limited extent, net fair value of financial instruments is value relevant. However, the

unrealised gain or loss of financial liabilities and off-balance sheet derivative financial

instruments are recorded as value relevant in the year-by-year analysis (see Table C 4

Appendix C).


6

1.3 Motivation Recent events in the business world, for example, the collapse of prominent

companies such as Enron, HIH Insurance and Barings Plc. have focused attention on

the importance of high quality financial reporting. Of relevance to this study is where

the collapses were due to the involvement of the company with derivative

instruments. In Australia some derivative instruments are not recognised in the

balance sheet. Therefore, extensive disclosures are required to ensure financial

statement users are aware of the significance of these instruments to an entity’s

financial position. The first motivating factor of this study is the limited research on

derivative disclosures in Australia. Prior studies have examined the quality of

accounting information in general, but only a limited number provide evidence on

derivative disclosures in Australia. The finance literature provides evidence on the

association between the use of derivatives and firm characteristics (e.g. Berkman,

Bradbury, Hancock and Innes, 2002; Nguyen and Faff, 2002). No attempt has been

made to examine the relationship between the disclosure quality of derivatives and

firm characteristics. The few Australian studies on derivative disclosures have been

surveys of accounting practice before the implementation of the AASB 1033

Presentation and Disclosure financial Instruments, for example Hancock (1994),

Berkman, Bradbury, Hancock and Innes (1997), Chalmers and Godfrey (2000) and

Chalmers (2001). The current study provides evidence of the association between

disclosure quality of derivative information and firm characteristics.

The second source of motivation justifies selecting firms in the extractive industries.

Prior studies have indicated that firms in the extractive industries extensively use


7

derivative instruments for hedging purposes (Berkman, Bradbury, Hancock and Innes,

1997) as compared to other industries. This is because of the significant exploration

and production risks inherent in the extractive industries2. Also derivatives are used

by extractive firms to underwrite and protect revenue. Moreover, Chalmers (2001) has

indicated that prior to AASB 1033 these firms provided information in their annual

reports. Therefore, it is relevant to examine the association between disclosure

practice and firm characteristics and the importance of the information in firm

valuation. Perhaps the most important factor is that these industries play a significant

role in the Australian economy, where they generate exports worth more than $30

billion in 2000 to 2002 and represent approximately 25% of the listed companies on

the Australian Stock Exchange (ASX). Therefore, examining this specific industry

may affect inferences regarding the value relevance of accounting information

(Simko, 1999).

The third motivating factor is that fair value accounting and hedge disclosures have

become significant topics of study in the U.S. since the Statement of Financial

Accounting Standards No 107 (SFAS 107) Disclosures about Fair Values of

Financial Instruments and SFAS 119 Disclosure about Derivative Financial

Instruments and Fair Value of Financial Instruments issued in 1991 and 1994,

respectively. However, the findings of these studies were based on samples from

banking industries in the U.S. and may not represent other industries and jurisdictions.

The reliability of these disclosures is questionable because fair value is based on

subjective estimates with potential for significant measurement error (Simko, 1999).

Therefore, research on the value relevance of derivative financial instruments, in

2 Includes oil, gas and mining industries, as per the IASC (2000). Please refer to section 2.5 chapter 2.


8

particular fair value disclosures and hedge disclosures, in the context of the extractive

industries in Australia will provide useful information on this complex area for both

Australian and international standard setters.

The fourth source of motivation is the impending harmonisation of accounting

standards. In 2005 Australia is expected to adopt most of the accounting standards

issued by the International Accounting Standards Board3. This includes IAS 39

Financial Instruments: Recognition and Measurement. The standard deals with

recognition and measurement of financial instruments at fair value. Given the fact that

ED 59 Financial Instruments, issued by AASB, has been rejected due to the

recognition and measurement issues, results from this study will provide evidence on

the readiness of Australian firms and investors to adopt IAS 39, i.e. to move towards a

fair value accounting regime.

The fifth source of motivation is that most regression models adopted in previous

value relevance studies (such as Barth 1994; Venkatachalam, 1996) are based on the

balance sheet model4. In this study the value relevance models are developed based on

the Ohlson (1995) model. The model provides a direct link between accounting

amounts and firm value, which is absent from other models (Barth, 2000). Results

from this study provide evidence on the robustness of prior results within this

methodology.

3 The International Accounting Standards Committee (IASC) became the International Accounting Standard Board (IASB) in 2001. 4 Where market value of equity represented by the market value of assets minus market value of liabilities.


9

1.4 Plan of Thesis

This thesis is structured in the following way. The following chapter describes the

institutional background surrounding financial reporting of derivative instruments in

Australia. The chapter begins by explaining the motivation for firms to use derivative

instruments. The comparative practices of accounting for financial instruments

between the U.S. and the IASC are then reviewed. This is followed by a discussion of

accounting for financial instruments in Australia. The discussion is based on the

disclosure and presentation of financial instruments required by the AASB 1033. One

important issue which has been investigated in the U.S. is the value relevance of fair

value required by various standards issued by the Financial Accounting Standard

Board (FASB). Surveys of Australian accounting practice are also discussed. Finally,

the chapter discusses the risk management practices in the extractive industries,

providing background evidence of the importance of derivatives for this industry. This

evidence indicates that studies on disclosure practice in this industry are important for

the economy.

Chapters three and four review prior studies on disclosure quality and value

relevance. Chapter three examines the association between disclosure quality and firm

characteristics. Studies investigating the quality of accounting information are

reviewed. The chapter begins by reviewing studies that examine disclosure quality in

different ways. Several studies indicate that to ensure high quality disclosures of

mandatory information, enforcement is an important issue. Selected empirical studies

are then reviewed as evidence of the association between disclosure quality


10

(disclosure level) and firm characteristics. Two Australian studies that have examined

the quality of derivative disclosures are also reviewed.

Chapter four discusses prior research on the value relevance of financial instruments.

The review provides a basis for understanding the benefits of disclosure to capital

market participants and firms. This chapter describes how capital market research has

emerged in accounting and how researchers use capital market data to explain the

relevance and reliability of financial statement information. Studies examining the

value relevance of financial instruments in the U.S., especially the fair value of

financial instruments, are reviewed. This helps to explain the need for extending value

relevance studies in other jurisdictions.

Chapter five describes the research questions developed based on the Australian

institutional environment and the prior literature. This study proposes questions that

relate to the disclosure quality of derivative information to the size of the firm,

performance of the firm, auditor, type of the extractive firm (limited liability or no-

liability firm), leverage and growth opportunities. Five research questions are

proposed that relate to the value relevance of derivative disclosures in firm valuation.

Chapter six describes the research methods and data collection procedures adopted.

Two main models are developed based on research questions presented in chapter

five. These are the firm characteristics model and market value model. Results on the

firm characteristics model are presented in chapter seven. Chapter seven provides

evidence that the quality of derivative disclosures has increased. The higher quality


11

derivative disclosures are associated with larger firms, performance of the firms and

high leverage firms.

Chapter eight presents the results of the market value model. The results indicate that

market participants regard disclosure quality as value relevant. However, the net fair

value information component is not significant, whereas the hedge information and

risk information components are value relevant. Also significant, to a limited extent,

is the net fair value of financial instruments. However, the incremental explanatory

power of net fair value and the unrealised gain or loss on financial instruments beyond

the book value of financial and non-financial instruments and earnings valued at

historical cost is very low. Nevertheless, the incremental explanatory power of net fair

value and the unrealised gain or loss on financial instruments has increased from 1998

to 2001.

Chapter nine provides some concluding comments. The chapter discusses the

contributions of the thesis to the literature, the limitations of the study and some

potential avenues for future research.

Chapter 2: Institutional Background

12

CHAPTER 2 INSTITUTIONAL BACKGROUND This chapter describes the institutional background surrounding financial instruments,

particularly derivative instruments. The organisation of this chapter is as follows. The

following section provides the institutional background, focusing on the current

situation for financial reporting of derivative instruments. Section 2.1 provides

background on the risks attached to the use of financial instruments and how firms

can manage their exposure to risks. Subsection 2.1.1 discusses whether firms should

use derivative instruments to hedge their exposure. Section 2.2 describes the

accounting standards issued by both the Financial Accounting Standards Board

(FASB) and the International Accounting Standard Board (IASB). This is followed by

a discussion of the accounting standard on derivative disclosures issued by the

Australian Accounting Standards Board (AASB). Section 2.3 describes fair value

accounting and section 2.4 presents research on derivative instruments in Australia.

Section 2.5 discusses risk management practices commonly used in the extractive

industries. Section 2.6 summarises the chapter.

2.1 Risk Management

Financial instruments5 expose firms to financial, economic and operational risks.

Changes in market conditions or the financial position of the parties to the financial

instruments or transactions expose firms to financial and economic risks. These risks

5 AASB 1033 defines a financial instrument as any contract that gives rise to both a financial asset of one entity and a financial liability or equity instrument of another entity.


13

are credit risk6, interest rate risk7, foreign exchange risk8, market risk9 and liquidity

risk10. Firms generally use the most common and practical methods to reduce or

eliminate the risks. These include limiting the exposure to both individual

counterparties and the number of specific instruments held, or through hedging the

risks with sophisticated instruments such as forward contracts or swaps. Operational

risks include fraud, failure to collect the amount due and human error (Sarwal, 1989).

These risks can be reduced through internal control. The focus of this section is on the

financial and economic risks, whereby derivative instruments are used to reduce these

risks.

Banks and financial institutions first introduced derivative instruments to help firms

manage their exposure to these risks. It has been documented in the U.S. that

derivatives are used by large corporations to reduce their exposure to a variety of risks

(Géczy, Minton and Schrand, 1997). Competition among banks and financial

institutions led to the development of innovative derivative instruments. To enable

firms to better manage the risks and uncertainties, financial institutions in particular,

have developed a variety of complex financial instruments (Scott, 1997). These

innovative instruments are based on four basic derivative instruments, including

forward contracts, future contracts, options and swaps. A recent market survey by the

International Swaps and Derivatives Association (ISDA) indicates that the global

6 The risk that a borrower will not able to meet its obligations. The risk can be classified into country risk, industry risk, counterparty risk, settlement risk and transfer risk. 7 The risk of loss through mismatching the interest bases of assets and liabilities. The risk can be classified further into net interest risk, spread risk and basis risk 8 The risk of loss as a result of an unfavourable movement in exchange rates. 9 The risk of loss resulting from changes in the market value of negotiable instruments due to factors other than interest and exchange rates. 10 The risk can be classified as cash liquidity or market liquidity. Cash liquidity is the risk of loss resulting from the inability to meet financial obligations as they arise, due to a lack of liquid resources. Market liquidity refers to the risk of loss from not being quickly able to sell financial instruments at full market value, when required.


14

over-the-counter (OTC) derivatives volumes increased by 8.14% in 2000. The volume

globally totalled US$63.009 trillion at year-end 2000. According to Mr. Thomas K.

Montag, the vice-chairman of the ISDA, this is due to the continuing growth in

interest rates and currency products that influence managers to use the most effective

tools to manage potentially adverse economic movements (ISDA, 2002).

2.1.1 To Hedge or Not to Hedge?

Exposure to the risks can cause earnings volatility (Nance, Smith and Smithson, 1993;

and Pincus and Rajgopal, 2002) and financial managers can manage the risks in

several ways. They can either do nothing, or alternatively they can hedge the risks.

Managers can hedge the exposure internally or externally (Hassan, 1994, p. 11;

Hassan, Mohd Saleh and Ismail, 1996; 1998). Internal hedging requires firms to use a

variety of in-house options such as matching techniques, intercompany netting

systems, pricing considerations and asset and liability management. On the other

hand, external hedging requires firms to use a variety of hedging products, such as

forward contracts, swaps and futures contracts11, available in the market to minimise

or offset the risk.

Previous studies have examined the motivation for firms to purchase hedging

instruments. These include the perception that hedging can increase firm value, by

reducing expected taxes, expected costs of financial distress or other agency costs

(Nance et al., 1993). Guay (1999) indicates that firms use derivatives to hedge the

entity risks. Perhaps by hedging managers are able to present the true earnings

11 These instruments are known as derivative financial instruments.


15

capacity of the firms since hedging reduces factors that are outside of managerial

control (DaDalt, Gay and Nam, 2001). However, Koonce, McAnally and Mercer

(2000) indicate that investors are less willing to invest in a company that uses

derivatives as they judge derivatives as riskier than non-derivatives.

Under certain circumstances hedging firm risk increases a firm’s value (Nance et al.,

1993). The value of the firm increases by reducing expected taxes, the costs

associated with financial distress and agency costs. Koonce et al. (2000) conduct three

experimental studies to provide evidence on risk. They document that investors do

consider traditional risk factors, i.e. probabilities and outcomes, when judging the risk

of financial items. However, investors put greater weight on loss probabilities and loss

outcomes than on gain probabilities and gain outcomes.

Barnes (2001) indicates that two factors motivating firms to hedge the risks are: a)

maximisation of shareholder value and b) maximisation of managerial utility. Prior

studies investigate three issues that are associated with corporate risk management

within the shareholder maximisation hypotheses. These are financial distress,

investment policy and taxation. Managerial risk aversion and signalling managerial

skills are two variables that are associated with corporate risk management within the

managerial utility maximisation hypothesis.

Tufano (1996) extends research by Smith and Stulz (1985), Stulz (1984, 1990),

DeMarzo and Duffie (1995), Nance et al. (1993), Lessard (1990) and Breeden and

Viswanathan (1996) (in Tufano, 1996) in the gold mining industry. He found that the

predictions of shareholder maximisation hypothesis are not well supported by the


16

data. However, Tufano found that firms whose managers own more stock options

manage less gold price risk and those firms whose managers have more wealth

invested in common stock manage more gold price risk (managerial utility

maximisation hypothesis).

Géczy, Minton and Schrand (1997) extend previous studies by examining the use of

currency derivatives of firms that have ex ante exposure to foreign exchange-rate risk.

They found that firms with greater growth opportunities and tighter financial

constraints are more likely to use currency derivatives. Further, they found that firm

characteristics were related to the costs of implementing a specific derivatives

strategy. They also provide evidence that the benefits of using currency derivatives

are related to the general decision to use currency derivatives and the specific choice

between the various types of currency instruments.

2.2 International Accounting Practices

Investors have been alerted to the importance of transparent financial reporting of risk

and uncertainty as recent significant losses experienced by prominent companies, such

as Barings Plc., Proctor and Gamble and Gibson Greeting, resulted from the

inappropriate use of derivatives. Since then, financial reporting has witnessed an

increase in the disclosure of risk information by U.S. and U.K. companies. Most

accounting standard setters (especially in the U.S.), the International Accounting

Standard Board (IASB) and those in the U.K. have been forced to respond by

requiring more disclosures. The following subsections discuss the development of the


17

respective accounting standard issued by the Financial Accounting Standard Board

(FASB) and the IASB.

2.2.1 Financial Accounting Standard Board

The Financial Accounting Standards Board (FASB) in the United States has issued

seven accounting pronouncements pertaining to financial instruments since 1990.

Compared to other accounting standards boards, the FASB is more advanced in

regulating the accounting treatment for derivative instruments, even though the

approach employed has been piecemeal (Blankley and Scroeder, 2000). However, the

development of the regulation for derivative instruments (SFAS 133 Accounting for

Derivative Instruments and Hedging Activities) took the FASB 10 years.

Table 2.1 summarises the progress of the FASB financial instruments accounting

pronouncements. Prior to the issuance of SFAS 133, the FASB has issued SFAS 119

Disclosure about Derivative Financial Instruments and Fair Value of Financial

Instruments, to improve the previous standards12. In 1998 the FASB issued SFAS

133. However, its implementation was deferred until 2000. The standard requires all

derivative instruments to be shown on the balance sheet at fair market value.

Nevertheless, the FASB issued SFAS 138 as an amendment to SFAS 133, wherein

certain technical changes from SFAS 133 are introduced.

12 Summary of this standard is presented in Table 2.3.


18

Table 2.1: FASB Financial Instruments Accounting Pronouncements Year SFAS Title Accounting and Disclosure Requirements 1990 105 Disclosure of Information

about Financial Instruments with Off-Balance Sheet Risk and Financial Instruments with Concentrations of Credit Risk

Required companies to make quantitative disclosures about market risks and credit risks related to unsettled financial instruments.

1991

107

Disclosures about Fair Values of Financial Instruments

Required companies to disclose the fair market value of unsettled financial instruments.

1993

115

Accounting for Certain Investments in Debt and Equity Securities

Required that trading and available-for-sale securities be shown on the balance sheet at fair market value, with changes in market value included in income [for trading securities] or in the equity section of the balance sheet as a component of other comprehensive income [for available-for-sale securities].

1994

119

Disclosure about Derivative Financial Instruments and Fair Value of Financial Instruments

Required disclosures about the purposes of derivative financial instruments and about how the derivatives are reported in financial statements. For derivatives used to hedge risks associated with anticipated transactions, required disclosure about the nature of the anticipated transactions and the amounts of deferred hedging gains and losses.

1998

133

Accounting for Derivative Instruments and Hedging Activities

Required that all derivative instruments be shown on the balance sheet at fair market value with the accounting for changes in fair value depending on the purpose of the derivative. Established new disclosure requirements superseding those in Statements 105 and 119 and amending those in Statement 107.

1999

137

Accounting for Derivative Instruments and Hedging Activities-Deferral of the Effective Date of FASB Statement 133 –An Amendment of FASB Statement no 133

Delayed the effective date of Statement 133 to fiscal years beginning after June 15, 2000.

2000

138

Accounting for Certain Derivative Instruments and Certain Hedging Activities-An Amendment of FASB Statement no 133

Made certain technical changes in the way Statement 133 is to be applied to specific types of hedges.

Adopted from Trombley (2003)


19

2.2.1.1 SFAS 133 Accounting for Derivative Instruments and Hedging Activities.

This standard requires that all derivative instruments are to be measured at fair value

and recognised in the statement of financial position either as assets or liabilities. The

derivatives can be designated into three types of exposures: fair value exposure, cash

flow exposure and exposure to changes in the value of net investment in a foreign

operation (Trombley, 2003, p.34), if certain conditions are met13. The derivatives are

to be accounted for in the financial statements based on their intended use and their

resulting designation14. The gain or loss for a fair value hedge15 is recognised in

earnings in the period of change together with the offsetting loss or gain on the

hedged item attributable to the risk being hedged. The treatment also applies to a

derivative designated as a hedge of the foreign currency exposure of an unrecognised

firm commitment or available for sale security.

The total gain or loss for a cash flow hedge16 has to be separated into the effective and

ineffective portions. The effective portion of the gain or loss is initially reported as a

component of other comprehensive income and subsequently reclassified as an

earnings component when the forecasted transaction affects earnings. However, the

ineffective portion will immediately be reported in the earnings component. This

treatment also applies to a derivative designated as a hedge of the foreign currency

exposure of a foreign-currency-denominated forecasted transaction.

13 The conditions where hedge accounting is allowed relate to: a) the nature of the hedged risk (interest rate risks, price risks, foreign currency exchange risks and credit risks), b) the hedge effectiveness and c) documentation. 14 The intended use or designation refers to the purpose of the hedging. The results from the designation is the gain or loss that can off-set the loss or gain from the hedged item. 15 Refers to derivatives designated as hedging the exposure to changes in the fair value of a recognised asset, liability or an unrecognised firm commitment. 16 A derivative designated as hedging the exposure to variable cash flows of a forecasted transaction.


20

The gain or loss for a foreign currency hedge on foreign currency exposure of a net

investment in a foreign operation is to be reported in other comprehensive income as

part of the cumulative translation adjustment. The gain or loss for a derivative not

designated as a hedging instrument is recognised in earnings in the period of change.

2.2.2 International Accounting Standards Board

2.2.2.1 IAS 32 Financial Instruments: Disclosure and Presentation IAS 32 was approved by the International Accounting Standards Committee (IASC)17

Board in March 1995 to deal with the disclosure and presentation of financial

instruments. This standard was the result of a joint project with the Canadian Institute

of Chartered Accountants. The standard was amended twice, (once in 1998 and again

in 2000) to be consistent with IAS 39 Financial Instrument: Measurement and

Recognition, issued after IAS 32 and to eliminate any disclosure requirements made

redundant by IAS 39. Basically, IAS 32 deals with the: a) classification of financial

instruments as liabilities or equity, by the issuers, and the classification of related

interest, dividends and gain or loss, b) offsetting of financial assets and financial

liabilities and c) disclosure of information about financial instruments.

The standard requires:

the issuer of a financial instrument to classify the instrument (or its component parts) as a liability or as equity in accordance with the substance of the contractual arrangement on initial recognition and the definitions of a financial liability and an equity instrument.

17 The IASC became the International Accounting Standard Board (IASB) in 2001.


21

The instrument is classified as a financial liability when the issuer is obliged to deliver

cash or another financial asset to another party. The classification continues until the

financial instrument is removed from the enterprise’s balance sheet. As an example, a

company issues a mandatory redemption preferred share for a fixed amount at a fixed

date. Since there is a contract that obliges the issuer to redeem the instrument at the

predetermined amount and date, the instrument must be reported as a financial

liability.

However, when the issuer issues a financial instrument that contains both a liability

and equity element, the standard requires a separate presentation of the instrument’s

components on the issuer’s balance sheet. A convertible bond is an example of such a

financial instrument.

The standard also requires the enterprise to present a financial asset and a financial

liability on a net basis when it:

i.has a legally enforceable right to set off the recognised amounts; and

ii.intends either to settle on a net basis, or to realise the asset and

settle the liability simultaneously. The standard requires firms to disclose: a) risk management policies, including the

policy for hedging each major type of forecasted transaction (paragraph 43A, IAS 32),

b) terms, conditions and accounting policies for each class of financial asset, financial

liability and equity instruments, both recognised and unrecognised (paragraph 47), c)

interest rate risk exposure (paragraph 56), d) credit risk exposure (paragraph 66), e)

fair value of each class of financial assets and liabilities, recognised and unrecognised


22

(paragraph 77) and f) financial assets carried at an amount in excess of fair value

(paragraph 88).

2.2.2.2 IAS 39: Financial Instruments: Recognition and Measurement

The second phase of the joint project between the IASC and the Canadian Institute of

Chartered Accountants addresses the issues of recognition, de-recognition,

measurement and hedge accounting. As a result, IAS 39 was issued in December

1998. The standard addresses the issues of recognition and measurement only. The

standard requires: a) all financial assets and financial liabilities, including derivatives,

to be recognised on the balance sheet and measured at cost (which is the fair value of

the consideration given or received to acquire the financial asset or liability), b)

subsequent to initial recognition, all financial assets should be re-measured at fair

value (except for loans and receivables originated by the enterprise, other mixed

maturity investments and financial assets whose fair value cannot be reliably

measured), c) most financial liabilities (except for derivatives and liabilities held for

trading which should be remeasured to fair value) be measured at their original

recorded amount less principal repayments and amortisation and d) those financial

assets and liabilities that are remeasured to fair value, a firm has the option to either: i)

recognise the entire adjustment in net profit or loss for the period, or ii) recognise

changes in fair value (the period only) of financial assets and liabilities held for

trading in net profit or loss, and for non-trading instruments, the change in value is

reported in equity until the financial asset is sold, at which time the realised gain or

loss is reported in net profit or loss.


23

2.2.3 Accounting for Financial Instruments in Australia

2.2.3.1 AASB 1033 Presentation and Disclosure of Financial Instruments

AASB 1033 Presentation and Disclosure of Financial Instruments was issued in 1996

and developed based on ED 65 Presentation and Disclosure of Financial Instruments.

The predecessor of ED 65, ED 59 Financial Instruments, was released in March 1993.

However, ED 59, which attempted to introduce recognition and measurement rules for

financial instruments in addition to disclosure requirements, was withdrawn.

Extensive lobbying against the recognition and measurement of financial instruments

caused the Australian standard setters to defer the recognition and measurement issue

until an equivalent international standard was issued.

All publicly listed companies in Australia, which issue or hold financial instruments,

should comply with the requirements of AASB 1033. The standard focuses only on

the presentation and disclosure of financial instruments. AASB 1033 was

subsequently amended in 1999 to include the requirement of converting financial

instruments to achieve greater harmonisation with the international standard, IAS 32

Financial Instruments: Disclosure and Presentation, which was amended to reflect

the issuance of IAS 3918 as discussed in a previous subsection. Table 2.2 presents a

summary of the development of accounting standards for financial instruments in

Australia.

18 With the move to full harmonisation in 2005, it is planned that Australia will adopt the requirements of IAS 39 Financial Instruments: Recognition and Measurement issued in 1999.


24

Table 2.2: Summary of Development of Accounting Pronouncements Related to Financial Instruments in Australia

Date

issued Pronouncement Title Application

Because AASB 1033 does not specify rules for the recognition and measurement of

financial instruments, its disclosure requirements are extensive. These disclosures are

expected to enhance financial statement users’ understanding of the significance of

recognised and unrecognised financial instruments to an entity’s financial position,

financial performance and cash flows. They should also assist investors in assessing

19 Instead of settlement through physical receipt or delivery of a commodity, commodity-linked financial instruments require settlement through cash payments that are determined according to a formula contained in the contract. 20 Converting financial instruments are financial instruments that mandatorily convert to equity instruments of the issuer.

browna2

This table is not available online. Please consult the hardcopy thesis available from the QUT library.


25

the amounts, timing and certainty of future cash flows associated with those

instruments (AASB 1033, paragraph 5.1.1).

Many derivative financial instruments are not recognised as assets and liabilities in the

balance sheet and the unrealised gain or loss on these instruments is not recorded in

the income statement. Therefore, firms are required to disclose information related to

the instruments. This includes the objectives of holding or issuing derivative financial

instruments (paragraph 5.3). The disclosure will help users understand why entities

use derivatives (by explaining the risks attached to the entity) and what they want to

achieve by the use of the derivatives. In addition, firms are required to disclose

information about hedge activities if they use financial instruments to manage risk

associated with anticipated future transactions. Paragraph 5.8 requires firms to

disclose:

a) a description of the anticipated transactions, including the period of time until they are expected to occur,

b) a description of the hedging instruments,

c) the amount of any deferred or unrecognised gain or loss21 and

the expected timing of recognition as revenue or expense.

The amount included in paragraph (c) includes all accrued gains and losses on hedge

instruments. The unrealised gain or loss may result from the difference between the

net fair value and the historical cost of financial instruments. The net fair value

amount must be disclosed in accordance with paragraph 5.6 AASB 1033. This

21 The amount includes all accrued gains or losses on financial instruments designated as hedges of anticipated transactions. The accrued gain or loss may be unrealised (as a results of carrying the hedging instrument at net fair value) but recorded in the statement of financial position, or it may be unrecognised (if the instrument is carried at cost), or it may be realised. However, the accrued gain or loss has not been recognised in the calculation of net profit or loss pending completion of hedging transaction (paragraph 5.8.2 AASB 1033).


26

information permits the users of an entity’s financial report to understand the nature

and effects of hedges of anticipated future transactions.

Disclosure of net fair value is not restricted to the recognised financial assets and

liabilities. It also applies to unrecognised derivative financial instruments, which are

normally used for hedging or managing risks. Paragraph 5.6 AASB 1033 requires

firms to disclose:

a) the aggregate net fair value as at the reporting date, showing separately the aggregate net fair value of those financial assets or financial liabilities which are not readily traded on organised markets in standardised form,

b) the method or methods adopted in determining net fair

value, and

c) any significant assumptions made in determining net fair value.

Further, the standard requires more information when one or more financial assets are

recognised at an amount in excess of their net fair value. Paragraph 5.7 of AASB 1033

requires firms to disclose:

a) the carrying amount and the net fair value of either the individual assets or appropriate groupings of those individual assets, and

b) the reasons for not reducing the carrying amount, including the

nature of the evidence that provides the basis for management’s belief that the carrying amount will be recovered.

In addition to the above, firms are also required to disclose terms, conditions, the

accounting policies adopted (paragraph 5.2), interest rate risk (paragraph 5.4), credit

risk (paragraph 5.5) and also commodity contracts which are regarded as financial

instruments (paragraph 5.9). Paragraph 5.2 will help users understand the effect of

instruments on the amount, timing and certainty of future cash flows. Paragraphs 5.4

and 5.5 will help users understand the risks to which the entity is exposed and thus the

effect of these risks on future profits.


27

For the purposes of this study, AASB 1033 is assumed to be a “high quality”

disclosure standard. This is a reasonable claim because of the extensive nature of the

disclosure requirements, designed to overcome the lack of guidance with regard to

recognition and measurement. Therefore, firms that prepare their annual reports based

on this standard, are said to provide “high quality” derivative information.

Correspondingly, failure to comply with this standard would suggest that derivative

disclosures are of low quality. Table 2.3 compares the disclosure requirements of

AASB 1033 with related FASB and IASB standards.


28

Table 2.3: AASB 1033 Vs RELATED FASB AND IAS STANDARDS

Main issue SFAS 119 Disclosure About

Derivative Financial Instruments (DFI) and

Fair Value (FV) of Financial Instruments

IAS 32 Financial Instruments: Disclosure

and Presentation

IAS 39 Financial Instruments: Recognition and

Measurement

AASB1033 Presentation and disclosure of FI

Scope

Applies for DFI such

as forward, future, swap and option contracts. (p. 5)

Applies to all financial

instruments except a) interest in subsidiaries, b) interest in associates, c) interest in joint venture, d) employers’ and plans’ obligations for post retirement benefits, e) employers’ obligations under employee stock option and stock purchase plans and f) obligations arising under insurance contracts (p. 1)

Applies to all financial instruments ( FI)

except a) interest in subsidiaries, interest in associates and interest in joint venture, b) right and obligations under leases, c) employers’ assets and liabilities under employee benefits plans, d) right and obligations insurance contracts, e) equity instruments issued by reporting enterprise, f) financial guarantee contracts, g) contracts for contingent consideration in a business combination and h) contracts that require a payment based on climatic, geological, or other physical variables. (p. 1)

Applies to all FI (Financial Assets,

Financial liabilities, equity, DFI) other than: interest in subsidiaries, b) interest in associates, c) interest in joint venture, d) operating leases, e) employers’ obligations for post employment benefits, f) employers’ obligations under employee share option and share purchase plans and g) obligations arising under insurance contracts (p. 1.3)

Disclosure about the purpose of FI issued or held

Distinguish between

trading or other than trading (p. 9)

Discussion of the extent to

which FI are used (p. 42)

Financial statements should include all of

the disclosures required by IAS 32, except for requirements in paragraph 77 and 88. (p. 166)

Entity’s objectives for holding and

issuing DFI (p. 5.3)

Disclosure about DFI held /issue for trading

Average FV of DFI

together with related end-of-period FV, distinguish between

Nil

A recognised gain or loss arising from a

change in the fair value of a financial assets (FA) and financial liabilities (FL) that is not part of a hedging relationship should be

Nil


29

Main issue SFAS 119

Disclosure About Derivative Financial Instruments (DFI)

and Fair Value (FV) of Financial Instruments

IAS 32 Financial Instruments:

Disclosure and Presentation

IAS 39 Financial Instruments: Recognition and

Measurement

AASB1033 Presentation and Disclosure of FI

assets and liabilities. Net gains or losses arising from trading, disaggregated by class, business activity, risk, or other category (p. 10)

reported as follows: a) a gain or loss on a FA and FL held for trading should be included in net profit or loss for the period in which it arises and b) a gain or loss on an available for sale FA should be either: i) included in net profit or loss for the period in which it arises or ii) recognised directly in equity, through the statement of changes in equity (p. 103)

Disclosure about DFI held /issue for other than trading

A description of the entity’s

objectives for holding or issuing the DFI

A description of how each class of DFI is reported in financial statements including the policies for recognizing and measuring the DFI held or issued, and where those instruments and related gains or losses are reported (p. 11)

Nil

DFI are always deemed to be held for trading

unless they are designated and effective hedging instruments (p. 10)

Nil

Disclosure of hedges of anticipated transaction

A description of anticipated

transactions whose risks are hedged, including the period of time until the anticipated transactions are expected to occur

Cancelled and replaced by

IAS 39

Describes the enterprise’s financial risk

management objectives and policies, including its policy for hedging each major type of forecasted transaction (p. 169 a)

A description of the anticipated

transaction, including the period of time until they are expected to occur

A description of the hedging instruments


30

Main issue

SFAS 119 Disclosure About Derivative Financial Instruments (DFI)

and Fair Value (FV) of Financial Instruments

IAS 32 Financial Instruments:

Disclosure and Presentation

IAS 39 Financial Instruments: Recognition and Measurement

AASB1033 Presentation and Disclosure of

FI

Description of classes of

DFI used to hedge anticipated transaction

Amount of hedging gains or losses explicitly deferred,

A description of the transaction or other events that result in the recognition in earnings of gains or losses deferred by hedge accounting (p. 11)

Disclose separately for designated fair value hedges, cash flow hedges and hedges of a net investment: i) a description of the hedge, ii) a description of the financial instruments designated as hedging instruments for the hedge and their fair values at the balance sheet date and iii) the nature of the risks being hedged

The amount of any deferred

or unrecognised gain or loss and the expected timing of recognition as revenue or expense (p. 5.8)

Net FV disclosure

Average FV of DFI

together with related end-of-period FV, distinguish between assets and liabilities (p.10)

For each class of FA

and FL, both recognised and unrecognised, an enterprise should disclose information about fair value (p. 77)

The FI are measured at cost, which is the fair value

of the consideration given or received (p. 66) After initial recognition, an enterprise should

measure FI, including derivatives that are assets, at their fair values, without any deduction for transaction costs that it may incur on sale or other type of disposal (p. 69).

After initial recognition, an enterprise should measure liabilities held for trading and derivatives that are liabilities at fair value, except for a derivative liability that is linked to and that must be settled by the delivery of an unquoted equity instrument whose fair value cannot be reliably measured, which should be measured at cost (p. 93)

The aggregate NFV as at the

reporting date, showing separately the aggregate NFV of those financial assets (FA) or financial liabilities (FL) which are not readily traded in organised markets in a standardised form

the method or methods adopted in determining NFV

any significant assumptions made in determining NFV (p. 5.6)


31

2.3 Fair Value Accounting

Fair value accounting22 has become the preferred option of accounting for financial

instruments as opposed to historical cost. The major reasons for this preference are: a)

cost is not relevant or understandable, b) the practicality in measuring financial

instruments at fair value, c) fair value eliminates issues which arise from using the

cost method, d) fair value is not overly different to the current practice and e) the

benefits of fair value are obtainable at a reasonable cost (Hancock, 1996). As a result

the FASB has issued SFAS 107 Disclosures about Fair Value of Financial

Instruments which requires firms to disclose the fair value of financial instruments. A

move to fair value appears to be due to the belief that market-based information is the

most relevant financial data for financial statement users. The standard was amended

by SFAS 119 Disclosure about Derivatives Financial Instruments and Fair Value of

Financial Instruments. However, these standards failed to provide adequate fair value

information and the disclosure about derivatives has not been uniform (Feay and

Abdullah, 2001). Therefore, the FASB 133 was issued to overcome these issues. The

disclosure of fair value information is expected to provide more useful information for

users to assess the effects of derivative transactions (Rasch and Wilson, 1998).

However, critics of fair value accounting are concerned that fair value may be less

reliable than historical costs since managers may use their discretion to manipulate the

information (Ahmad, 2000). As a result investors could be reluctant to base valuation

decisions on these subjective estimates (Barth, 1994). Another concern is that fair

22 AASB 1033 defines fair value as the amount for which an asset could be exchanged, or liability settled, between knowledgeable, willing parties in an arm’s length transaction. The term ‘fair value’ is used interchangeably with mark-to-market, market value-based and market value accounting.


32

values may increase the volatility of income as compared to historical costs (Barth,

Landsman and Wahlen, 1995; Feay and Abdullah, 2001). For example, in Australia

ED 59 was criticised by the banking industries who opposed market value

measurement method. The banks were concerned that market value may increase the

volatility of earnings (Hancock, 1996).

The FASB in the U.S. and the IASB are ahead of the AASB in requiring firms to

measure financial instruments based on fair value (Chalmers and Godfrey, 2000).

Nevertheless, the net fair value23 disclosures required by the AASB reduce the gap

between the Australian and international jurisdictions. The reason for these disclosure

requirements is the net fair value is relevant to financial statement users. Net fair

value:

reflects the judgement of financial markets as to the present value of expected

future cash flows relating to an instrument,

permits the comparison of financial instruments having substantially the same

economic characteristics, and

provides a neutral basis for assessing management’s stewardship by indicating the

effect of its decisions to buy, sell or hold financial assets and to incur, maintain or

discharge financial liabilities (AASB 1033 paragraph 5.6.1).

Disclosure of net fair value is not restricted to the recognised financial assets and

liabilities, it also applies to unrecognised derivative financial instruments, which are

normally used for hedging or managing risks. The AASB 1033 allows management to

23 Para 7 of AASB 1033 defines net fair value as, the fair value of asset (liability) after deducting (adding) costs expected to be incurred were the asset (liability) to be exchanged (settled).


33

use their discretion in the assumptions made in determining the valuation method as

described in paragraph 5.6. Hence, the reliability of net fair value remains

questionable.

The issue of fair value accounting is the lack of specific definition of the term ‘fair

value’, especially when the capital markets are not perfect and complete (Bradbury,

2000). For example, IAS 39 Financial Instruments: Recognition and Measurement,

paragraph 8 and AASB 1033 paragraph 7, refer to fair value as the amount for which

an asset could be exchanged, or a liability settled, between knowledgeable, willing

parties in an arm’s length transaction. However, the FASB defines fair value as the

price at which the asset could be sold or liability could be settled. Hence, the first

approach is based on a user’s perspective rather than the seller’s perspective, which is

adopted by the FASB (Bradbury, 2000).

Barth and Landsman (1995) indicate that fair value can be measured based on: a)

value in use24, b) entry value25, c) exit value26 or d) value to the entity (combination of

measures). Value in use has the potential to provide more information than market

prices if an imperfect market exists (Barth and Landsman, 1995). However, the value

is very subjective and subject to potential manipulation (Bradbury, 2000). The FASB

has adopted exit price as the measure of fair value, while the Australian Accounting

Research Foundation (AARF) supports the value to the entity measurement. However,

the exit value and value to the entity may not be material, in an active and liquid

market, because the entry price and exit price will converge (Bradbury, 2000).

24 IAS 36 Impairment of Assets defines value in use as the present value of estimated future cash flows expected to arise from the continuing use of an asset and from its disposal at the end of its life. 25 This refers to purchase price. 26 This refers to the price at which the asset could be sold or liability could be settled.


34

A recent study by Deloitte Touche Tohmatsu (2000) indicates that there is no clear

consensus for a move to fair value accounting for all financial instruments in

Australia. However, Fargher (2001) indicates that managers of financial institutions

are not unanimously opposed to the use of fair value accounting for all financial

instruments. This is contradictory to their counterparts in the U.S. who are opposed to

the application of fair value accounting across the board to financial instruments

(Smith, 2000). However, the final decision of whether to move or not to move to fair

value accounting should take into consideration the users’ perspective. A recent study

on the views of two groups of financial statement user by Tan, Tower, Hancock and

Taplin (2003) indicate that there is no evidence on the agreement among Australian

investors as to the usefulness of fair value accounting for financial instruments.

However, they acknowledge that this might be due to the limitation of a small sample

size.

Australian firms have accepted the requirement to make quantitative disclosures

concerning the fair values of derivative instruments. However, the quality of these

disclosures is less than satisfactory. Based on their study of the accounting practices

among Australia’s largest 500 firms, Chalmers and Godfrey (2000) report that the

major weaknesses are the lack of accounting policy disclosures relating to specific

types of instruments and incompleteness in fair value disclosures. These hinder the

understandability, comparability and consistency of derivative instruments

information.


35

Fair value accounting and hedge disclosure have become considerable topics of study

in the U.S. since the Statement of Financial Accounting Standards No 107 (SFAS

107) Disclosures about Fair Values of Financial Instruments and SFAS 119


Instruments were issued in 1991 and 1995, respectively. However, the findings of this

research are based on banking industries in the U.S. and may not represent other

industries and jurisdictions. For example, the study by Cornett, Rezaee and Tehranian

(1996) provides evidence that market participants view the announcements of fair

value accounting as detrimental to the value of commercial banks. Moreover the

reliability of these disclosures is questionable because the fair value is based on the

subjective estimates with potential for significant measurement error (Simko, 1999).

Therefore, research on the value relevance of derivative financial instruments, in

particular fair value disclosures and hedge disclosures, in the context of the extractive

industries in Australia will provide useful information on this complex area for both

Australian and international standard setters.

2.4 Studies of Accounting Practices in Australia Research on financial instruments in Australia is still at an early stage and much of it

normative. Hancock (1994), Berkman et al. (1997), Chalmers and Godfrey (2000) and

Chalmers (2001) are such examples. Berkman et al. (1997) describe the reporting

requirements and practices for derivative financial instruments in New Zealand and

Australia. By examining the 1994 annual reports of 116 New Zealand companies and

the 1995 annual reports of 200 Australian listed companies, they provide evidence


36

that the disclosure of information about financial derivatives by Australian companies

is less detailed than New Zealand companies.

Chalmers and Godfrey (2000) explore the disparity between the derivative

instruments accounting treatments encouraged by the standard (AASB 1033 issued in

1996) and firms’ current accounting practices based on the 30 June 1998 financial

statements of Australia’s largest 500 firms. The study extended previous survey

research by identifying firms’ derivative accounting policies and approaches to fair

value determination. The study indicates that the quality of the disclosures is less than

satisfactory.

Chalmers (2001) examines Australian firms’ derivative instruments disclosures over

three phases, prior to the release of AASB 1033. This study analyses a firm’s response

to information demands in a changing regulatory environment from 1992 to 1998.

Chalmers used a voluntary reporting disclosure index to capture derivative

disclosures. Firms were found to be responsive to quasi-contractual disclosure

regulation. The release of ED 65 Disclosure and Presentation of Financial

Instruments, combined with the increased probability of the development of a

standard, has been found to be influential in achieving enhanced reporting of

derivative instruments.


37

2.5 Risk Management Practices in the Extractive Industries

Extractive industries are defined by the IASC27 as:

“those industries involved in finding and removing wasting natural resources located in or near the earth’s crust”

These industries are involved in finding and removing natural resources that cannot

be replaced such as sand, coal, oil, natural gas, sulphur, etc. The definition limits the

activities by excluding extraction of minerals from seawater or from the air (IASC

Steering Committee on Extractive Industries, 2000, paragraph 1.5).

Firms involved in the extractive industries may be involved in upstream activities,

downstream activities, or both. In upstream activities, firms are exploring, finding,

acquiring and developing resources (mineral reserves) up to the point the reserves are

capable of being sold or used. Firms involved in refining, processing, marketing and

distributing of petroleum, natural gas or mined mineral are classified as being engaged

in downstream activities. However, in certain cases firms may be involved in both

activities. These firms are referred to as integrated enterprises.

The uniqueness of the extractive industries, compared to other industries is the

exposure to potential exploration and production risks, and this is especially so for

upstream activities. Firms in the extractive industries are faced with exploration risks

when funds are spent to acquire the resources (mineral reserves) which may result in

no commercially recoverable reserves. At the same time, these firms are exposed to

the high risks of production. Production risks are the risk that the quantities produced

may be different to those estimated quantities. Beside these risks, extractive firms are

27 IASC released an “Issues Paper” on the extractive industries for comment in 2000.


38

also exposed to the volatility of commodity prices. These risks can cause earning

volatility, which leads firms to engage in risk management.

Firms may enter into hedging transactions to fix the selling price of their resources

and to protect against price fluctuations. This may take place before the resource is

produced. The three most commonly used hedging instruments are forward sales,

options and gold loans (IASC Steering Committee of Extractive Industries, 2000). In

forward sales, firms have to commit to deliver a fixed quantity of a commodity at a

fixed price on a specific date. Options allow firms to purchase a put or sell a call to

establish a minimum price while retaining the ability to participate if prices rise.

Firms may borrow gold and subsequently repay the loan in ounces of gold from future

production.

Two studies that examine the risk management practices in the extractive industries

are documented in Tufano (1996) and Pincus and Rajgopal (2002). Tufano (1996)

examines risk management practices in the gold mining industry. He documents that

firms whose managers own more stock options managed less gold price risks, and

those firms whose managers have more wealth invested in common stock manage

more gold risk. Further, Tufano documents that firm risk management levels appear to

be higher for firms with smaller outside block holdings and lower cash balances, and

whose senior financial managers have shorter job tenures. However, the study

concludes that the initial predictions of shareholder maximisation hypothesis is not

well supported by the data.


39

Pincus and Rajgopal (2002) examine the relation between hedging with derivatives

and discretionary accrual choices, and with income smoothing within oil and gas

firms. They identify two types of industry-specific risks that affect the volatility of

earnings. The risks are fluctuation in oil prices and the firm’s drilling success, which

require different risk management policies. Their study examines whether

discretionary accrual choices and hedging with derivative instruments are used as

substitute mechanisms to mitigate the impact of oil price and exploration risks on

earnings volatility. They report that the extent of smoothing with abnormal accruals is

not a significant determinant of the amount of hedging. However, the extent of

hedging is a significant determinant of the extent of smoothing with abnormal

accruals. This indicates that the more managers hedge with derivatives the less they

smooth earnings with abnormal accruals.

2.6 Summary This chapter provides background information related to derivative instruments. Firms

use derivatives either for investment or to hedge potential risks, especially in the

extractive industries where firms are exposed to exploration and production risks.

Because of the risks attached to the instruments, and due to significant losses

experienced recently by prominent companies, accounting standard setters have been

forced to require more disclosures related to derivatives. As AASB 1033 does not

specify rules for the recognition and measurement of financial instruments, its

disclosure requirements are extensive. These disclosures are expected to enhance

financial statement users’ understanding of the significance of recognised and


40

unrecognised financial instruments to an entity’s financial position, financial

performance and cash flows.

The two chapters that follow review the prior literature on the disclosure quality of

accounting information and the value relevance of financial instruments. Chapter

three discusses literature on the association between disclosure quality and firm

characteristics, and chapter four summarises the literature on value relevance studies.

Both Australian and international studies, particularly U.S. studies, are discussed.

Chapter 3: Literature Review: Disclosure Quality

41

CHAPTER 3 LITERATURE REVIEW: DISCLOSURE QUALITY

This chapter presents a review of the literature that relates to the research questions

discussed in chapter five. This review provides a basis for understanding the area of

research on disclosure quality. The organisation of this section is as follows. The

literature review begins with a discussion of corporate disclosure quality. This section

presents the differences between definitions of disclosure quality used in prior studies.

This is followed by section 3.2 which describes research discussing the importance of

enforcement power in ensuring a high level of compliance with accounting standards.

Subsection 3.3.1 discusses the research on attributes of high quality accounting

standards, especially when comparing foreign Generally Accepted Accounting

Principles (GAAP) to U.S.-GAAP. Subsection 3.3.2.1 discusses the quality of

accounting information disclosed in the financial statements. This subsection presents

research on the association between corporate disclosure and firm characteristics

based on disclosure indices and analyst ratings. Subsection 3.3.2.2 reviews previous

studies focusing on disclosure quality of environmental information and other

accounting information, respectively.

Subsection 3.3.3 discusses research on disclosure quality of accounting information

and investor decisions. Subsection 3.3.4 provides evidence on the descriptive results

of the disclosure quality of derivative information. This subsection indicates the

opportunity for a contribution of this thesis. Section 3.4 provides a summary of this

chapter.


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3.1 Disclosure Quality: The Definitions

The annual report is one of the channels available for firms to report their financial

performance. However concern about the usefulness of financial information has

caused pressure groups to lobby accounting standard setters to require greater detail

and more extensive information especially concerning derivative financial instruments

so that high quality accounting standards are produced. High quality standards should

lead to high quality financial reports and as a result investors’ confidence in the

credibility of financial reporting is enhanced (Levitt, 1998). Therefore, firms that

comply with the standards would be expected to produce high quality accounting

information.

The term “quality” has been used interchangeably with the term “transparency”.

Because the concepts of quality and transparency are elusive (Kothari, 2000),

different interpretations have been placed on the meaning of high quality financial

information. Ball, Robin and Wu (2002), Ball, Kothari and Robin (2000) and Lang,

Raedy and Yetman (2003) assess quality based on the timeliness28 of the recognition

of economic income in accounting income. Bradshaw, Richardson and Sloan (1999)

examine quality based on the level of accruals, while Lang et al. (2003) look for

evidence of earnings management and of a higher association of earnings with share

price.

28 Ball et al. (2000) define timeliness as the extent to which current-period accounting income incorporates current-period economic income. This definition is consistent with Lang et al. (2003) and Ball et al. (2002).


43

Pownall and Schipper (1999) refer to financial statements as being of high quality if

they possess three attributes: transparency, full disclosure and comparability.29

Transparent financial statements are statements that “reveal the events, transactions,

judgments and estimates underlying the statements and their implications” (Pownall

and Schipper, 1999, p. 262). Transparency allows users to see the results and

implications of the decisions, judgments and estimates of statement preparers. Full

disclosure relates to the provision of all information necessary for decision-making,

thereby providing reasonable assurance that investors are not misled. Finally,

comparability means that similar transactions and events are accounted for in the

same manner, both cross-sectionally among firms and over time for a given firm.

Most literature on disclosure quality uses either a disclosure index or disclosure

ratings produced by the Financial Analysts Federation (FAF), the Association for

Investment Management Research (AIMR) and the Center for International Financial

Analysis and Research (CIFAR) to measure disclosure quality as defined by Pownall

and Schipper (1999). This research is discussed in subsection 3.3.2.1.

While these views of quality focus on the financial statements as a whole, the

definition of Pownall and Schipper (1999) can be readily adapted to individual

disclosures within the financial statements. For this reason, their interpretation of

quality is used in this study. That is, this study defines financial information as being

of high quality when it possesses the attributes of transparency, full disclosure and

comparability.

29 Different definitions of accounting quality abound because, as Ball et al. (2002) claim, quality is an elusive concept especially where there are a great number of uses of accounting information.


44

3.2 Disclosure Quality: Regulation, Enforcement and Compliance

Financial statements are the principal means through which financial information is

communicated to investors, lenders, suppliers, customers, employees, government and

the public. Users of the statements require comprehensive information for their

decisions. Financial statements are prepared and presented according to the objectives

of financial reporting, i.e. usefulness for decision-making.

Financial information is recognised in the financial statements if it meets the

definition of an element of the financial statements, for example an asset, and satisfies

the criteria for recognition. Disclosure of information about the items in financial

statements and their measures are detailed where required in the notes to the financial

statements. Although there is some concern of potential disclosure overload,

nevertheless greater disclosure reduces information asymmetry (Hope, 2003a).

Accounting information is presented based on accounting standards issued by

accounting standards bodies. In Australia there are several parties involved in

regulating30 the accounting standards. These include the Australian Securities

Commission, the Australian Accounting Standards Board (AASB), the Australian

Accounting Research Foundation (AARF) and the Australian Stock Exchange (ASX).

Prior to the establishment of the AASB (which replaced the Accounting Standards

Review Board in 1991), Australian accounting and financial reporting was regulated

by professional bodies such as the Institute of Chartered Accountants in Australia

30 Taylor and Turley (1986, p. 1) define regulation as the imposition of constraints upon the preparation, content and form of external financial reports by bodies other than the preparers of the reports, or the organisations and individuals for which the reports are prepared.


45

(ICAA), the Australian Society of Certified Practicing Accountants (CPA) and the

AARF (Whittred, Zimmer and Taylor, 2000, p. 3). However, these bodies did not

have the required legal power to enforce the compliance of companies with the

standards (Whittred et al., 2000, pp. 4). According to Hope (2003a) “…without

adequate enforcement the standards will be inconsequential, and will remain on

paper”. To ensure the high quality of information being presented, both the quality of

accounting standards and the enforcement of the regulations must exist (Kothari,

2000). Hope (2003a) provides evidence that the U.S., the U.K. and Canada have the

highest enforcement score. Australia is tenth on the list of 22 countries. Hope also

provides evidence that enforcement disclosure scores are positively related with

enforcement. This indicates that enforcement encourages managers to follow the

accounting standards and therefore, high quality accounting information is presented.

Enforcement power is essential to ensure better compliance. Therefore, in this case

the AASB and any standards approved by it have been granted statutory force by the

Corporations Law and the Australian Securities and Investment Commission (ASIC)

Act. Compliance with accounting standards will assist in the comparability of

accounting information. Existing research has studied the extent of compliance within

an international and local setting. Tai, Au-Yeung, Kwok and Lau (1990) and Ahmed

and Nicholls (1994) examine compliance within the local setting. Both studies

provide evidence that the level of compliance of mandatory disclosure in developing

countries (i.e. Hong Kong and Bangladesh respectively) is low. Lack of enforcement

mechanisms is one of the reasons non-compliance occurs. Similar arguments may

apply in the case of the international setting where Tower, Hancock and Taplin (1999)

and Taplin, Tower and Hancock (2002) report that the extent of compliance with the


46

International Accounting Standards (IAS) in six Asia-Pacific Countries varies, with

Australian companies recorded the highest level compliance score. The International

Accounting Standards Board (IASB) does not have enforcement powers to ensure

firms in these countries comply with its requirements. The IASB has to rely on the

individual countries’ enforcement mechanisms, which apparently do not exist or are

very weak in most of these countries. Bradshaw and Miller (2002) investigate the

level of compliance among non-U.S. firms adopting U.S. GAAP. They provide

evidence that the level of compliance among these firms is lower than U.S. firms.

Failure to comply with the requirements of the standards indicates that the quality of

information disclosed in the annual reports is low.

3.3 Studies on Disclosure Quality

3.3.1 Disclosure Quality of Accounting Standards Studies on the comparability of accounting standards have been extensively

documented in the U.S. These studies examine the quality of annual reports of foreign

firms listing on the U.S. exchanges. Currently, foreign firms have to reconcile

earnings and book value of owners’ equity to U.S. GAAP. This is to ensure a similar

quality of reporting as that required by U.S. reporting standards.

These studies examine quality based on attributes31 of comparability and indirectly

transparency and full disclosure (Pownall and Schipper, 1999). Amir, Harris and

Venuti (1993), Barth and Clinch (1996) and Harris and Muller (1999) provide mixed

results when they compare the quality of non-U.S. GAAP to U.S. GAAP firm 31 Pownall and Schipper (1999) refer to comparability, transparency, and full disclosure as attributes of financial reports rather than the standards per se.


47

disclosures. The analysis was based on information provided on Form 20-F. Amir et

al. (1993) provide evidence that approximately one-third of the reconciliations

examined reported no material differences between non-U.S. GAAP and U.S. GAAP

income. This indicates that non-U.S. GAAP income is comparable to U.S. GAAP

income.

Barth and Clinch (1996) provide evidence that the line-item adjustments from U.K.,

Australian and Canadian GAAP to U.S. GAAP are non-comparable. The largest

single line item difference recorded is goodwill for the U.K. firms averaging –7

percent of U.S. GAAP net income and –20 percent of U.S. GAAP shareholders’

equity. However, Harris and Muller (1999) provide evidence of relatively small mean

differences, after deleting several influential observations, between IAS and U.S.

GAAP net income and shareholders equity. The mean differences are 0.28 percent

and 0.21 percent of IAS shareholders’ equity, respectively.

Ball, Robin and Wu (2002) investigate the interaction between the accounting

standards under which financial statements are prepared and the incentives of

managers and auditors who prepare them of four Asian countries, i.e. Hong Kong,

Malaysia, Singapore and Thailand. They indicate that although these countries adopt

high quality (high transparency) accounting standards, external factors such as

political influence may force preparers to produce low quality transparency32 financial

statements. Therefore, they hypothesise that the demand for transparent accounting is

higher in countries, which are more market-oriented than countries that are more

politicised, planning-oriented institutions or with more extensive family or other 32 The authors interpret transparency in the financial statements as the timely incorporation of economic income. They give particular emphasis to the incorporation of economic losses or negative economic income when measuring transparency.


48

networks. The results indicate that the hypothesis, that the reporting information in

these countries generally lacks transparency, is supported.

Lang et al. (2003) compare the quality of earnings of cross-listed non-U.S. firms on

U.S. exchanges with a sample of non-cross-listed firms. The quality of earnings was

assessed based on the existence of evidence of earnings management, timely

recognition of losses and the association between share prices and earnings. They

provide evidence that cross-listed firms report high quality earnings since they appear

to be less aggressive in earnings management, exhibit timely recognition of losses and

whose earnings are more highly associated with share price.

3.3.2 Disclosure Quality of Accounting Information and the Impact on Firms

3.3.2.1 Disclosure Quality and Firms Characteristics

Prior research examines the usefulness of financial information by investigating the

extent of firms’ disclosures and the quality of accounting information disclosed in

annual reports. Studies that relate the level of disclosure to firm characteristics include

Firth (1979), Cooke (1989, 1991and 1992), Imhoff (1992), Malone, Fries and Jones

(1993), Singhvi and Desai (1971), Heflin, Shaw and Wild (2001) and Wallace and

Naser (1995). These studies provide evidence of the association between disclosure

level and firm characteristics such as firm size, listing status, firm auditor, scope of

business, risk of trading and industry type. Most studies in this area use a disclosure

index to measure disclosure level or disclosure quality.


49

Singhvi and Desai (1971) provide evidence on the association between the quality of

corporate disclosure and firm characteristics. Annual reports of 100 listed and 55

unlisted firms were examined. The quality of corporate disclosure was measured by

the disclosure index, which represents adequate corporate disclosure. The explanatory

variables represent size, number of stockholders, listing status, CPA firms, rate of

return and earnings margin. The results indicate that size, listing status, number of

stockholders and type of CPA are positively related to disclosure quality. However,

the multivariate analysis indicated that only listing status and earnings margin are

significant at p < 0.01and 0.05 < p < 0.10, respectively.

Firth (1979) has developed a disclosure index to investigate the impact of size, stock

market listing and auditors on voluntary disclosure in the U.K. Voluntary items were

weighted using a five point scale based on the importance of the items to financial

analysts. The disclosure index, based on 48 items and their weightings, for each of the

sample companies was developed. The study found that firms that listed on stock

exchanges made greater disclosure than unlisted firms. Further, larger firms provide

greater disclosure than small firms no matter whether the companies were listed or

not. In contrast to the findings of other studies, Firth reported that auditors had little

influence on the levels of disclosure.

Cooke (1989, 1992) investigates the association between disclosure quality of

national annual reports and firm characteristics. In Cooke (1989), results on the

association between the quality of annual reports of Swedish companies and firm

characteristics were presented. Unlike the above studies, this study used a

dichotomous procedure in developing a disclosure index. The study employed a step-

wise approach to investigate the association between the extent of disclosure and firm


50

characteristics, namely quotation status, parent company relationship, annual sales,

total assets as a proxy for size and number of shareholders. The results indicate that

firm size and listing status are significantly related to the level of disclosure.

Cooke (1992) examines the impact of size, stock market listing and industry type on

disclosure in the annual reports of Japanese listed companies. He uses a similar

approach to Cooke (1989). However, instead of using a step-wise technique the study

used factor analyses and the principal factors as regressors to resolve multicollinearity

issues in the size variables. Manufacturing companies were found to disclose more

information than non-manufacturing companies. However, there was no significant

difference in mandatory disclosures between these companies. Also, significantly

related to level of disclosure, were multiple listed firms and firm size.

Unlike the above studies, Imhoff (1992) employed analysts’ judgments of accounting

quality issued by the Financial Analysts Federation33 (FAF) as the basis for relative

quality measures. The study investigates the association between differences in

judgements among security analysts and firm characteristics. The results indicate that

firms that were judged by analysts as producing high quality accounting had more

predictable earnings, more accurate earnings forecasts, smaller annual earnings

forecast revisions after first-quarter results were announced, a lower likelihood of a

bad-news annual earnings announcement, larger firm size, lower debt-to-equity ratios

and had more a stable relation between changes in sales and changes in operating

income over time.

33 Detailed discussion on procedures undertaken by FAF can be found in Sengupta (1998, p. 462).


51

Wallace and Naser (1995) investigate the association between firm characteristics and

mandatory disclosures of Hong Kong firms. Both mandatory and voluntary disclosure

items were identified, with a total of 30 items contained in the disclosure index. The

underlying criterion for scoring firm annual reports was comprehensiveness and

therefore, a higher score was awarded for greater detail of information disclosed. To

estimate the relationship between comprehensiveness of mandatory disclosure and

firm characteristics ranked and unranked estimation procedures were performed.

However, the conclusions were based on the ranked OLS, as the results were more

robust. The results indicate that total assets, profit margin, auditor and scope of

business contribute to the understanding of variation in the disclosure index.

Malone et al. (1993) examine the association between the extent of corporate

disclosure with characteristics of firms in the oil and gas industry. The study used a

disclosure index, which was weighted by the industry analysts. Analysts were asked

to weight 129 items according to the importance of each item in the investment

decision. The disclosure index was represented by the total actual score as a

percentage of total possible score. The step-wise procedure was employed to examine

the association between the extent of disclosure and firm characteristics. The results

indicate that firms with high debt-to-equity ratios, firms listed on a major stock

exchange and firms with a greater number of shareholders disclose more financial

information.


52

3.3.2.2 Disclosure Quality of Specific Information

While studies in the previous subsection examine the disclosure quality of overall

accounting information, several studies have examined disclosure quality for

particular accounting information, such as environmental disclosures. Other studies

have examined the association between disclosure, earnings management and

corporate restructuring. Fekrat, Inclan and Petroni (1996), Choi (1999) and Jaffar,

Mohd Iskandar and Muhammad (2002) examine the disclosure quality of corporate

environmental disclosures.

Jaffar et al. (2002) measure quality based on two indicators. The first indicator is

based on specific categories such as environmental policy, product and service. The

second indicator measures quality based on the location of the information in the

annual report. In addition, they also examine the quality of disclosure based on the

number of pages used to report the environmental information. The study examines

the relationship between quality and quantity of environmental disclosures and

environmental performance, company size and financial performance. The results

indicate that companies that performed badly in terms of environmental performance

provided detailed information in important locations in the annual report in order to

reduce political costs. Further, the researchers provide evidence that large and poor-

environmental performance firms tend to provide large volumes of environmental

information in the annual report.

Based on a similar approach, Choi (1999) investigates the relationship between

quality and quantity of disclosure and corporate characteristics. Content analysis was

used to develop three categories of information. These categories were economic


53

factors, pollution abatement and other general information. A score ranging from 0 to

3 was given for each item in each category based on the quantitative and qualitative

information provided in the financial statements. As an alternative, an ordinal scale, a

score between 0 and 3, was developed for an individual firm as an overall measure for

the evaluation of disclosure. Finally, the amount of disclosure per company was

measured by the number of lines in the section. Firms were divided into high-profile

industries and low-profile industries. Choi reports that: a) firms in high profile

industries disclose systematically greater quantities of information than their

counterparts in lower profile industries, b) corporate size is found to be positively

associated with the propensity to disclose and finally, c) when the analysis is confined

to the discloser group, financial leverage and corporate age emerged as significant

variables.

Lobo and Zhou (2001) examine the relationship between disclosure quality and

earnings management. This study was motivated by two streams of research that

identify the relationship between: a) information asymmetry and disclosure quality

and b) earnings management and information asymmetry. The authors contribute to

the literature by successfully predicting the relationship between disclosure quality

and earnings management. Corporate disclosure was measured based on the ratings

published by the Association for Investment Management and Research (AIMR),

while discretionary accruals from the modified Jones model were used to measure

earnings management. The results indicate that corporate disclosure was negatively

associated with earnings management.


54

Bens (2002) examines the quality of information voluntarily disclosed about corporate

restructuring. The disclosure measures were based on the scores of the FASB

Emerging Issues Task Force (EITF) 94-3 requirements. Six independent variables

were chosen to regress on disclosure quality. These are financial performance, CEO

changes, increased monitoring, proprietary costs, regulatory action and additional

control variables that may affect disclosure. Bens indicates that: a) the disclosure

levels increased dramatically as a result of the SEC targeted corporate restructuring

for greater scrutiny, b) the level of disclosure is lower when the restructuring is

preceded by a routine CEO change and c) increases in shareholder monitoring is

associated with higher disclosure levels.

3.3.3 Disclosure Quality of Accounting Information and Benefits to the Investors

Investors require firms to disclose high quality information in order to make economic

decisions. Greater disclosure is assumed to enhance investor’s welfare and therefore,

will attract investors to trade more aggressively. Managers are claimed to have better

information about the economic performance of the firm and incentives exist for

managers to withhold value-relevant unfavourable information (Sengupta, 1998).

Failure to meet investors’ and creditors’ information needs would have a huge impact

on firms. Investors and creditors may take actions that are disadvantageous to firms

such as increasing the cost of capital or to some extent reducing their market

participation. Lack of information may cause market participants to seek other

investment opportunities reducing the firm’s share price. Even though investors may

invest in a low quality disclosing company, they might require comparatively higher

rates of return which could lead to a higher cost of capital and lower share price


55

(Miller, 2001). As a result firms may find it difficult to grow and remain competitive

(Jenkins, 1994). Nevertheless, increased disclosure may benefit some investors more

than others, as well as increase the price efficiency of some firms more than others

(Price, 1998). For example, informed traders benefit more by the release of public

information. This is because they can purchase more concentrated private information

(Lundholm, 1991 in Price 1998).

Several studies investigate the effect of disclosure quality and practices on investors.

The quality of corporate disclosure can be measured by examining the timeliness,

detail and clarity of information (Sengupta, 1998). Prior studies measure corporate

disclosure quality based on the corporate disclosure practices measured by the

Financial Analysts Federation (such as Sengupta, 1998; Heflin, Shaw and Wild, 2001)

and the Association for Investment Management and Research (Bushee and Noe,

2000; Price, 1998). Both organisations evaluate disclosure quality along with different

disclosure categories: annual reports, quarterly reports and informal communication

such as public releases or discussions. In a different approach Botosan (1997)

developed a disclosure index to represent disclosure levels.

Price (1998) examines the response of informed or institutional investors on financial

statement disclosure. This study provides evidence that informed investors make

greater use of accounting disclosures to form more precise earnings expectations.

Bushee and Noe (2000) investigate whether a firm’s disclosure practices affect the

composition of institutional ownership and stock return volatility. They provide

evidence that institutional investors are attracted to firms with greater disclosure.


56

Further, they report that there was no net impact of disclosure practices on return

volatility.

Heflin, Shaw and Wild (2001) examine the relationship between disclosure quality,

the risk of informed trading and market liquidity. They provide evidence that higher

quality disclosures reduce the risk of informed trading and enhance market liquidity,

as firms with high quality disclosures lead to smaller information asymmetry spread

components.

Sengupta (1998) provides evidence on the association between disclosure quality and

the cost of debt. Sengupta argues that lenders and underwriters look at corporate

disclosure to assess the degree of detail and clarity in the annual and quarterly reports,

the management discussion with financial analysts and the frequency of press releases

to calculate the default risk. Therefore, a firm’s cost of issuing debt is related to the

quality of its disclosures. Overall, a firm’s disclosure quality was obtained from

reports published by the FAF. Three groups of control variables include: issue

characteristics, market conditions and issuer characteristics. The results indicate that

the disclosure score is associated with the cost of debt, i.e. the highest disclosure score

firm incurred the lower cost of debt. This is because timely and detailed disclosures

may reduce the perception of default risk which leads to a lower cost of debt. This

indicates that lenders and underwriters consider firms’ disclosure quality in their

default risk estimates.

Botosan (1997) develops a disclosure index to measure the disclosure level of

manufacturing companies. In the study she directly examines the association between

firm’s voluntary disclosure levels and the cost of equity capital. She argues that since


57

cost of equity capital is difficult to obtain, prior research has adopted an indirect

approach by investigating the impact of disclosure on variables that are expected to be

positively related to the cost of debt. Five categories of voluntary information were

included in the disclosure index. These were background information, summary of

historical results, key non-financial statistics, projected information and management

discussion and analysis. Results from this study indicate that greater disclosure is

associated with a lower cost of equity capital for firms that attract a low analyst

following. However, there is no evidence of such association for firms with a high

analyst following.

3.3.4 Disclosure Quality of Derivative Information While the majority of studies discussed in previous sections examine the quality of all

financial information disclosed in the annual reports or other media, limited studies

have documented disclosure quality with regard to specific information, particularly

financial instruments. Two Australian studies on the quality of derivative disclosure

have been conducted by Chalmers and Godfrey (2000) and Chalmers (2001).

However, neither of these studies examine the benefits of derivative disclosure to the

firms and investors. Chalmers and Godfrey (2000) explore the disparity between the

accounting treatments of derivative instruments encouraged by the standard (AASB

1033 issued in 1996) and firms’ current accounting practices based on the 30 June

1998 financial statements of Australia’s largest 500 firms. This study extends

previous survey research by identifying firms’ derivative accounting policies and

approaches to fair value determination. The study indicates that the quality of the

disclosures were less than satisfactory. The major weaknesses were:


58

The lack of accounting policy disclosures relating to specific types of

instruments and incompleteness in fair value disclosures;

Variability in the information contained in the notes, both across firms and

within firms. These factors hinder the understandability, comparability and

consistency of derivative instruments information.

The limited variation in firms’ derivative instruments accounting policies, with

most sample firms employing hedge accounting techniques.

The study also suggests that firms had accepted the requirement to make quantitative

disclosures concerning the fair values of derivative instruments. However, disclosures

concerning fair value determination vary in detail and clarity.

Chalmers (2001) examines Australian firms’ derivative instruments disclosures over

three phases, namely a pure voluntary disclosure phase, a coercive voluntary

disclosure phase and a mandatory reporting period. The study examines the firms’

response to information demands in a changing regulatory environment from 1992 to

1998. Chalmers used a voluntary reporting disclosure index to capture derivative

disclosures. The index was constructed using the disclosures suggested in the

Australian Society of Corporate Treasurer's Industry Statement34 and ED65:

Presentation and Disclosure of Financial Instruments. The results indicate that firms

were responsive to quasi-contractual disclosure regulation since the number of firms

registering a positive voluntary reporting disclosure index increases from phase to

phase. The release of ED 65, combined with the increased probability of the

34 The industry statement was issued in March 1995 and requested firms to include information on derivatives in their financial statements.


59

development of an AASB standard on this issue, has been found to be influential in

achieving enhanced reporting of derivative instruments.

3.4 Summary Chapter three discusses prior studies on disclosure quality. The chapter begins by

presenting different views of disclosure quality in the literature. The elusiveness of

the concept “quality” results in varying definitions of “quality” (Ball et al. 2002). This

chapter has discussed prior studies that provide evidence of the relationship between

high quality accounting information and a high degree of enforcement. Generally, the

studies indicate that without adequate enforcement the standard is treated as voluntary

by managers (Hope, 2003a). While prior studies that compare the quality of financial

statements of firms cross-listed in the U.S. have provided mixed evidence, some

studies indicate that the quality of financial statements of firms in some countries is

not as high as expected even though they have adopted high quality accounting

standards. The low quality of financial statements in these countries is due to external

factors, such as political influence. Some studies have investigated the association

between disclosure quality and firm characteristics. These studies provide evidence

that high disclosure quality accounting information is positively related to variables

such as size of the firms, profitability and listing status. While prior studies presented

in this chapter investigate the effect of disclosure quality on the firms, the following

chapter provides a review of prior studies that investigate the effect of disclosure on

market participants. These studies are known as value relevance studies.

Chapter 4: Literature Review: Derivative Disclosures and Value Relevance Studies

60

CHAPTER 4 LITERATURE REVIEW: DERIVATIVE DISCLOSURES AND VALUE RELEVANCE STUDIES

While chapter three discusses prior studies on disclosure quality and the association

with firm characteristics, this chapter presents literature on the benefits of disclosure

to capital markets, especially to investors. This review provides a basis for

understanding the area of research on the value relevance of derivative instruments.

Research questions related to the value relevance of derivative disclosures are

discussed in chapter five.

This chapter is organised as follows. Section 4.1 relates the effect of disclosure quality

to the decision-making of investors. To investigate this effect researchers use capital

markets data. Section 4.2 discusses prior studies on capital markets research and value

relevance studies. The specific studies on value relevance of financial instruments are

discussed in section 4.3. Section 4.4 concludes the chapter.

4.1 Disclosure Quality of Accounting Information and Investors’ Decisions Chapter 3 discusses the benefits of high quality information on both firms and

investors. Sengupta (1998), Botosan (1997) and Botosan and Plumlee (2002) provide

evidence that firms providing high quality information incur lower costs of debt and

equity capital. High quality information reduces the uncertainty faced by investors

and creditors (Miller and Bahnson, 2002) and this increases their confidence in the

financial information provided by firms, leading to increased investment in these


61

firms (as found in Price, 1998; Bushee and Noe, 2000). As a result firms will

experience higher security prices.

While subsection 3.3.3 provided evidence on the effect of high quality information to

both firms and users, limited studies have examined the association between high

quality information and share prices. Lang, Ready and Yetman (2003), Eccher and

Healy (2000) and Gelb and Zarowin (2002) are among the researchers who

investigate the association between accounting quality and share prices. Lang et al.

(2003) provide evidence that cross-listed firms, compared to non-cross-listed firms,

have higher accounting quality as their accounting data are more highly associated

with price.

Eccher and Healy (2000) investigate the usefulness of IAS standards in the People’s

Republic of China. They measure the usefulness of accounting information based on

the relevance of earnings and accruals for predicting future cash flows and the relation

of earnings and accruals to contemporaneous stock price changes. The research

question is motivated by the weak findings of previous studies comparing IAS

standards and foreign standards, and due to the unique opportunity to examine similar

issues in China. The results indicate that the information produced using IAS

standards is no more useful than information prepared using Chinese standards. This

is because: a) there is no difference in the explanatory power of IAS and Chinese

accruals for future cash flows, b) international investors value IAS and Chinese

earnings and accruals in an equivalent manner and c) domestic investors value

earnings based on domestic standards differently to IAS earnings.


62

Perhaps the study of Gelb and Zarowin (2002) is of more relevance to the current

study since this examines the association between the level of corporate disclosures

and stock prices. In their study, firms are grouped into two groups based on the

disclosure quality of the firms; i.e. high for a disclosure rating above the industry

median or low for a disclosure rating below the industry median. This study

compares the groups based on the association between current stock returns and future

earnings changes. In this case a stronger relationship between current returns and

future earnings are expected from high disclosure firms. They hypothesise that high

disclosure firms have higher earnings response coefficients (ERCs). Their results

indicate that high disclosure firms have significantly higher future ERCs (i.e. greater

price informativeness) than low disclosure firms.

The main idea in the above discussion is how researchers infer the importance of

accounting information by relating this to share price. These kinds of studies are

known as capital markets research. The following section discusses in more detail

capital markets research.

4.2 Disclosure and Capital Markets Research

4.2.1 Capital Markets Research

Capital markets research emerged in the accounting literature in the late 1960’s after

considerable doubt had been expressed about the usefulness of historical cost

accounting numbers to convey a firm’s financial health. This research uses security

prices to infer whether information in accounting reports is useful to market

participants (Brown and Howieson, 1998). Ball and Brown (1968) and Beaver (1968)


63

are the pioneers in capital markets research in accounting (Kothari, 2001). Both

studies examine the effect of accounting announcements on share prices. Ball and

Brown (1968) found that accounting earnings (accounting information recognised in

the financial statements) is part of the information used in forming share prices

(Brennan, 1991). According to Kothari there are three concurrent developments that

helped them (Ball and Brown, 1968; Beaver, 1968) to develop empirical capital

markets research in accounting. These are: a) positive economic theory, b) the

efficient market hypothesis and the capital asset pricing model (CAPM), as well as c)

the event study methodology.

Kothari (2001) classifies the demand for capital markets research in accounting into

four areas, namely: a) fundamental analysis and valuation, b) tests of capital markets

efficiency, c) the role of accounting in contracts and in the political process and d)

disclosure regulation. Capital markets data is used: a) to help fundamental analysis

researchers in identifying mispriced securities, b) to examine market efficiency based

on event studies and cross-sectional tests of return predictability, c) to predict how the

use of accounting numbers in compensation and debt contracts and in the political

process affects a firm’s accounting choices (positive accounting theory) and d) to help

ascertain whether the objectives of standards issued by accounting standard bodies are

served and to explain the possibility of harmonising the standards (Kothari, 2001).

Capital markets research in accounting has been developed based on the efficient

market hypothesis (EMH) and the capital asset pricing model (CAPM)35. According

35 The CAPM identifies factors, such as future cash flows and a firm’s risk, that affect the share prices of the firms. It predicts that the expected rate of return of a security is increasing in the covariance risk of its cash flows. The CAPM along with the EMH facilitated the estimation of the firm-specific return component.


64

to Brown (1994) the market is efficient with respect to information if the set of market

prices is exactly the same whether or not it is conditioned on the information. Hence

the efficiency refers to how the information is portrayed in the firm’s share prices.

There are three forms of efficiencies: weak form, semi-strong form and strong form.

In the weak form of efficiency, investors cannot depend on historical data to earn

returns other than those that are to be expected given the investment risk (abnormal

returns). In the semi-strong form of efficiency investors cannot expect to earn

abnormal returns by analysing publicly available information. In the strong form of

efficiency investors cannot expect to earn abnormal returns by analysing information

from any source. To investigate whether accounting information affects a firm’s

share price, the share price can be compared immediately before and after information

is released to the public. This is the first approach of capital markets research in

accounting. Changes in share prices are used as an objective to infer the usefulness to

market participants of accounting information published in the annual reports (Brown,

1994 and Kothari, 2001).

Capital markets research can also help ascertain whether accounting standard bodies’

stated objectives are served. Capital markets research may explain whether financial

statement numbers prepared in accordance with a new standard convey new

information to the capital markets, and whether the accounting numbers are highly

associated with contemporaneous stock returns and prices. The rapid globalisation of

capital, product and labour markets has created a strong demand for international

accounting standards. Research in international accounting using capital markets data

can inform the standard setters in their deliberations on the development of

harmonised international accounting standards. Brown and Howieson (1998) indicate


65

that there are five research areas where capital markets research could lead to better

informed decision in the standards arena especially in Australia. These areas are

corporate regulation, international harmonisation, research and development, goodwill

accounting and equity accounting. Value relevance studies fall within this context.

4.2.2 Relevance and Reliability of Accounting Information and Capital Markets Research

Statement of Accounting Concepts 3 (SAC 3) Qualitative Characteristics of

Financial Information identifies relevance and reliability as the primary qualitative

characteristics which financial information should possess in order to be the subject of

general purpose financial reporting (paragraph 7). However, paragraph 7 cautions

that the Statement does not rank either characteristic above the other. Nevertheless,

studies on the qualitative characteristics that determine the usefulness of information

indicate that the concept of relevance appears to have emerged as the primary

qualitative characteristic, followed by reliability (Stanga, 1980).

Relevant information must have value in assisting users in making decisions. SAC 3

Paragraph 8 defines financial information to be relevant as follows:

For financial information to be relevant it must have value in terms of assisting users in making and evaluating decisions about the allocation of scarce resources and in assessing the rendering of accountability by preparers. If information is to assist users in making decisions about the allocation of scarce resources, it must assist them in making predictions about future situations and in forming expectations, and/or it must play a confirmatory role in respect of their past evaluations.

The information is relevant to a decision maker if he or she can use the information in

determining alternative courses of action. Without such knowledge he/she might take


66

a different decision that might cause a different outcome. The information must help

decision-makers to evaluate the past or present events, which have occurred in the

organisation, and help them to predict future events, before making their decisions.

Relevant information should provide feedback to confirm or to correct past

evaluations conducted by the users.

Ideally, relevant information about assets or liabilities disclosed in the financial

statements can be used to measure future cash flows generated from each asset or

liability. However, due to the uncertain nature of future events, this qualitative

characteristic is not a sufficient condition for usefulness. Therefore, relevant

information depends on how reliable the information is in terms of its measurement

and sources.

The financial statement users depend upon the reliability of information when making

their decisions. To be reliable, the information must represent the economic

conditions or events to which it relates. According to SAC 3 paragraph 16:

The reliability of financial information will be determined by the degree of correspondence between what that information conveys to users and the underlying transactions and events that have occurred and been measured and displayed. Reliable information will, without bias or undue error, faithfully represent those transactions and events.

Such information will enhance the users’ confidence in making decisions because it is

free from error or bias toward particular people.

Stanga (1980) suggests that the financial accounting concepts of relevance and

reliability are complementary rather than conflicting in nature. An increase in


67

relevance tends to be associated with an increase in reliability and vice versa. Duchac

(1998), in his study on the effectiveness36 of footnote disclosures of end-user interest

rate derivatives, identifies three criteria37 for the disclosure to be effective. These are:

a) the disclosure must have a reasonable purpose, b) the disclosure should not overlap

with an existing disclosure and c) sufficient demand must exist for the disclosed

information. Beside these, one important factor that must be considered is the

relevance38 of the disclosed information.

To examine the relevance and reliability of financial statement information39, capital

markets data is required. Ball and Brown (1968) and Beaver (1968) assert that capital

markets efficiency provides justification for selecting the behaviour of security prices

as an operational test of usefulness of information in financial statements. In the area

of capital markets research in accounting, researchers examine the relevance and

reliability of information to inform the standard setters regarding the effects of the

selection of rules on accounting numbers and their relation to firm values. Researchers

tend to adopt an investor perspective as investors represent a large class of financial

statement users (Barth, 2000).

36 Usefulness and effectiveness of footnote disclosures is critical because too much information disclosed will burden the management with the costs of generating and presenting this information. 37 He uses these criteria as a framework to evaluate the footnote. 38 Based on Hudack and McAllister (1994), relevance is much more frequently cited by the FASB in developing disclosure type accounting standards. 39 The information recognised and disclosed is based on the requirements of the respective accounting standards.


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4.2.3 Value Relevance Studies

Value relevance studies examine the relevance and reliability of information

recognised and disclosed in the financial statements required by the accounting

standards. These studies are an empirical operationalisation of the criteria, relevance

and reliability. Information can be value relevant if it reflects the relevance of

information to investors in valuing the firm, and it is measured reliably enough to be

reflected in share prices (Barth, Beaver and Landsman, 2001). Because of the

difficulty involved in testing relevance and reliability separately, the researchers use a

joint test of relevance and reliability. These are known as value relevance tests (Barth

et al., 2001).

Holthausen and Watts (2001) classify value relevance studies into three categories.

These are:

• Relative association studies: – These compare the association between stock

market values and alternative bottom-line measures. These studies usually test

for differences in the R squared (R2) of regressions using different bottom line

accounting numbers.

• Incremental association studies: – These investigate whether the accounting

number of interest is helpful in explaining value or return (over long windows)

given other specified variables. The accounting number is typically deemed to

be value relevant if its estimated regression coefficient is significantly

different from zero (e.g. Eccher, 1996; Barth, 1994; Barth et al. 1996)

• Marginal information content studies:– These investigate whether a particular

accounting number adds to the information set available to investors. These

studies typically use an event methodology to determine if the release of


69

accounting numbers is associated with value changes. Price reactions are

considered evidence of value relevance.

The majority (94%) of value relevance studies are in the form of the first and second

types of studies (Holthausen and Watts, 2001). These studies are normally motivated

by standard setting problems. Many value relevance studies focus on fair value

disclosures. These studies relate: a) fair value to pensions and other postretirement

obligations (OPEB) (e.g. Amir, 1993 and Barth, 1991), b) fair values of debt and

equity securities held by banks and insurance companies (e.g. Barth, 1994), c) fair

value estimates of bank loans (Barth et al. 1996, Eccher et al., 1996 and Nelson, 1996)

and d) fair value estimates of derivatives (e.g. Venkatachalam, 1996). These value

relevance studies also address the issue of non-financial intangible assets. Most of

these studies relate the relevance and reliability of information with equity valuation.

Barth (1994), Barth et al. (1996), Eccher et al. (1996) and Nelson (1996) are a few

examples of studies which use both (reliable and relevant) characteristics in

examining the statistical relation between the disclosure of financial instruments

information and stock prices. They claim that if there is a significant correlation

between share prices and disclosed information then the information is value relevant.

This notion of value relevance is a combination of reliability and relevance, because

relevant information is not useful in making decisions if it is not reliable. The

following subsections provide empirical research related to these studies.

4.2.3.1 Other Value Relevance Studies

Other value relevance studies include Amir and Lev (1996) and Hughes (2000). Amir

and Lev (1996) investigate the value relevance of financial and non-financial


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information in the wireless communications industry. They provide evidence that

financial and non-financial information are complementary to each other; i.e. on a

stand alone basis financial information is largely irrelevant for firm valuation,

however, some of the variables do contribute to the explanation of stock prices when

they are combined with non-financial information. The results indicate that for certain

events investors rely on non-financial information when making decisions.

Hughes (2000) examines the relation between the market value of equity and non-

financial pollution measures; i.e. sulfur dioxide emissions that capture firms’ exposure

to future environmental liabilities. The results indicate that a non-financial pollution

proxy is value relevant for high polluting utilities targeted by Phase One of the 1990

Clean Air Act Amendments (CAAA). However, there is no significant relation

between the relative pollution of utilities that are not targeted by Phase One emissions

and their share prices. Further, the study also provides evidence that for the targeted

utilities, the value relevance of the non-financial pollution proxy: a) increases in

response to the passage of the stringent 1990 CAAA environmental legislation and b)

then declines as the market subsequently reduces estimated compliance costs in

response to changing economic and technological factors.

4.2.3.2 Research on Value Relevance in Australia

Research on value relevance in Australia has been documented in the area of

superannuation and intangible assets. Ang, Gallery and Sidhu (1999) investigate the

value relevance of superannuation disclosures required under AASB 1028 Accounting

for Employee Entitlements. The paper extends U.S. research in three ways: a) it


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provides an opportunity to confirm or refute the claims of ED 53 Accounting for

Employee Entitlements lobbyists with respect to the relevance of superannuation

related information, b) it addresses the question of whether disclosure of

superannuation assets and liabilities are valued in a similar fashion to other assets and

liabilities and c) it examines the relative value relevance of accrued versus vested

benefit measures of superannuation liabilities.

The results of Ang et al. (1999) indicate that the Australian market participants: a)

value the disclosures of superannuation assets and liabilities, b) place higher weight

on the superannuation items, which is contrary to the U.S. evidence and c) do not

value superannuation related disclosures to be value relevant when the sample is

widened to include all complying firms in the Top 200. The results also indicate that

the accrued benefits measure of superannuation liabilities does not provide higher

explanatory power relative to the vested benefits measure. Their findings indicate that

the U.S. evidence does not necessarily generalise to an Australian setting. These

findings provide support for the idea of the current study in an Australian setting.

Barth and Clinch (1998) investigate whether relevance, reliability and timeliness of

Australian asset revaluations differ across types of assets: tangible and intangible. The

study also investigates whether they differ if the valuation amount is determined by

the Board of Directors or an independent appraiser. Their study examines the

association between different types of revalued assets and share prices, as well as the

non market based estimates of firm value.


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Overall, the study indicates that revalued financial, tangible and intangible assets are

value relevant. As expected they find that revalued investments are consistently

significant in their association with share prices, except for investments of non-

financial firms in associated companies. Their findings for revalued intangible assets

contradict the view that the estimates are unreliable. However, the findings regarding

property, plant and equipment are less consistent. While the revalued aggregate

property, plant and equipment is significantly positively associated with share prices

for firms in all three industries, the revalued plant and equipment is value relevant for

mining firms, insignificantly related to share price for non-financial firms and is

negatively and significantly related to share price for financial firms. There is little

evidence to indicate that director and independent appraiser-based valuations are

viewed differently by investors. This indicates that directors’ private information

enhances value estimates despite their potential incentives to act in their own self-

interest.

Smith, Percy and Richardson (2001) investigate the value relevance of Australian and

Canadian firms’ decision to selectively capitalise research and development (R&D)

costs. The study attempts to answer two research questions. Does the market place: a)

a value on capitalised R&D and b) a higher value on the current year R&D

expenditures of capitalisers than those of expensers? They observe that the capitalised

development costs are valued by the market and this indicates that the act of

capitalising provides information to the market.

Godfrey and Koh (2001) extend the above studies by investigating whether the

capitalisation of R&D, other identifiable intangibles as a group and unidentifiable

intangible assets (goodwill) affects the market value of equity of Australian


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companies. They find that capitalised intangible assets, as a whole, provides

information that is relevant for investors. The significance of this finding is that the

two different categories of intangibles (goodwill and other identifiable intangible

assets) provide relevant valuation information incremental to other balance sheet

items. Hence this study provides new evidence supporting accounting regulation that

permits capitalisation of both identifiable and unidentifiable assets, and separate

disclosure of two different categories of intangibles.

4.3 Value Relevance of Financial Instruments40

4.3.1 Studies on Value Relevance of Fair Value Disclosures

While there are a limited numbers of value relevance studies in Australia, many

studies on value relevance of accounting information have been conducted in the U.S.

over the last decade. The number has increase dramatically in the late 1990s41. Most

of the studies address the empirical relation between accounting numbers and share

market values either with or without drawing standard-setting inferences.

Barth (1994) investigates how disclosed fair value estimates of banks’ investment

securities and securities gains and losses (based on those estimates) are reflected in

share prices in comparison with historical costs. The sample comprises the U.S. banks

whose financial statement data are available on the 1990 Compustat Annual Bank

Tape. The data was collected for the period 1971-1990. Using measurement error and

earnings capitalisation models, Barth reports that: 40 Financial instruments consist of primary instruments (such as cash, receivable, investment, payable) and derivative financial instruments (such as swaps and options). Derivatives are normally used for hedging of risks. 41 See Holthausen and Watts (2001) for a comprehensive summary of this research.


74

Fair value estimates of investment securities provide significant explanatory

power beyond that provided by historical costs;

Historical costs provide no significant explanatory power incremental to fair

value;

Fair values of investment securities are found to have less measurement error

than historical costs vis-a-vis the amount reflected in share prices; and

Fair values securities gains or losses have no significant incremental

explanatory power.

Eccher et al. (1996) examines the value relevance of fair value data disclosed under

SFAS 107 Disclosures about Fair Value of Financial Instruments by banks42 for the

years 1992 and 1993. The study focused on fair values of all the major components

whether on- and off-balance sheet. The sample comprises 296 bank holding

companies for the fiscal year 1992 and 328 for 1993. Eccher et al. hypothesise that:

The fair values of securities are not incrementally value-relevant over and

above their historical cost values;

The fair values disclosures for financial instruments other than securities are

not incrementally value relevant over and above their historical cost values;

The fair values of market related off-balance sheet items are not value relevant

over and above their notional values;

The fair values of securities are not incrementally value relevant over and

above their historical cost values and other disclosures in the historical cost

financial statements;

42 SFAS 107: Disclosures about Fair Value of Financial Instruments requires banks to disclose fair value estimates for all financial instruments, both recognised (such as banks’ loan portfolios, deposits, and borrowings) and off-balance sheet items (such as interest rate swaps, commitments, and derivative contracts).


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The fair value disclosures for financial instruments other than securities are

not incrementally value relevant over and above their historical cost values

and other disclosures in the historical cost financial statements; and

The notional values of off-balance sheet instruments are not incrementally

value relevant over and above the other disclosures in the historical cost

financial statements.

The findings of the Eccher et al. study are:

The fair values of investment securities are value relevant;

The fair value of net loans has a weaker association with market-to-book ratio

than does the fair value of securities;

The off-balance sheet instruments are value relevant in limited settings, and

the fair value of deposits is not significant; and

All three hypotheses on incremental explanatory power for fair values are

rejected at conventional levels for 1992. Hence this indicates that the fair value

and notional value disclosures are each incrementally value relevant.

However, in 1993 the results are contrasting for some fair value disclosures.

Barth et al. (1996) examine the value relevance of banks’ fair value disclosures under

the SFAS 107. Additional variables, to those used by Eccher et al. (1996) and Nelson

(1996), such as non-performing loans and interest sensitive assets and liabilities, are

included. The study is conducted on a sample of 136 banks over the period 1992 and

1993. The results indicate that fair value estimates of loans, securities and long-term

debt provide significant explanatory power for bank share prices beyond that provided

by book values. The finding is stronger when additional variables are included.


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Unlike prior studies under SFAS 107, which combine all securities into one class,

Park, Park and Ro (1999) examine whether the intent-based fair value disclosures by

security type under SFAS 115 Accounting for Certain Investments in Debt and Equity

Securities explain the value relevance of bank equity. Their findings are consistent

with the hypotheses and with the view of SFAS 115 on the relevance and usefulness

of the fair value disclosures to investors.

While those studies examine the value relevance of fair value on banks and financial

institutions, Simko (1999) examines the fair value of financial instruments of non-

financial firms under SFAS 107. He concludes that SFAS 107 liability disclosures for

1993 and 1995 are significantly associated with equity values. However financial

instrument assets and related derivatives do not have incremental explanatory power.

This is due to the lack of economic significance of fair value and book value

differences typical in the case of non-financial firms.

4.3.2 Studies on Value Relevance of Derivative Financial Instruments Disclosures

Value relevance studies that address questions relating to derivative financial

instruments disclosures include Venkatachalam (1996), Schrand (1997) and Wong

(2000). These studies differ in methodology employed and samples used. While

Venkatachalam measures the direct association between firm value and the use of

derivative financial instruments in a traditional framework, Schrand measures the

association in the nominal contracting literature. Venkatachalam extends research on

SFAS 107 by examining the implications of fair value disclosures under SFAS 119


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Instruments. The objective of his study is to investigate whether the disclosures

relating to derivative financial instruments used for purposes other than trading are

useful to investors in equity valuation. Findings of this research suggest that the fair

value estimates for derivatives help explain cross-sectional variation in bank share

prices. Further, the fair values have incremental explanatory power over and above

the notional amounts of derivatives.

Schrand (1997) examines the association between stock-price sensitivity to

unexpected changes in interest rates and the use of interest rate derivative instruments

for a sample of savings and loan association (S&Ls) firms. Using nominal contracting

hypothesis Schrand provides evidence that off-balance sheet derivative activities are

positively associated with lower stock-price interest rate sensitivity.

Wong (2000) investigates whether the quantitative disclosures about notional amount

and fair value of foreign exchange derivatives are associated with the information

used by investors to assess the sensitivity of equity returns to currency fluctuations.

The results are mixed and only weakly consistent with predictions for both the

association and usefulness tests. The evidence suggests that neither aggregated nor

disaggregated fair value disclosures complement notional amount in assessing

currency risk exposure. The study also indicates that the usefulness of accounting

disclosures for assessing firms’ overall currency exposures is limited and additional

disclosures are potentially useful.


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4.4 Summary

This chapter discusses prior studies on the value relevance of financial instruments

and other value relevance studies. This study uses capital markets data to indicate

whether the accounting information is relevant and reliable. While this current study

is the first study investigating the value relevance of derivative financial instruments

information in Australia, most studies on the value relevance of financial instruments

have been documented in the U.S. The studies focus on the value relevance of fair

value under different accounting pronouncements. Prior studies provide evidence that

the fair value of various financial instruments is value relevant and provides

incremental explanatory power above historical costs. The next chapter develops

research questions from the preceding literature chapters and the institutional

background chapter.

Chapter 5: Research Questions

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CHAPTER 5 RESEARCH QUESTIONS

Chapter five describes the research questions developed to investigate firstly, the

association between the disclosure quality of derivative disclosures and firm

characteristics and secondly, the value relevance of derivative disclosures. Four

research questions were developed to investigate the association between disclosure

quality and firm characteristics and two research questions are developed to examine

the value relevance of disclosure quality (research questions 5(a) and 5(b)). Given the

fact that prior research provides evidence that disclosed items, especially derivative

financial instruments are not value relevant, two more research questions (research

questions 6 and 7) are developed to investigate the value relevance of hedge

disclosures, which include the unrealised gain or loss and the off-balance sheet

derivative financial instruments.

Prior U.S. studies indicate the value relevance of fair value information. However, the

results of these studies are based on banking industries in the U.S. Therefore, the

results from these studies might not represent other industries and jurisdictions.

Research question 8 is developed to provide evidence on the value relevance of net

fair value disclosures in the extractive industries in Australia. Figure 5.1 presents the

diagram illustrating research questions used in this study. Section 5.1 discusses the

research questions related to disclosure transparency measured by a disclosure index.

Research questions related to the value relevance of derivative disclosures are

discussed in section 5.2. Section 5.3 summarises the chapter.


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Figure 5.1: Diagram Illustrating the Research Questions

Firm Characteristics Model

Research Questions

Market Value Model

Value relevance of Disclosure Quality. (RQ 5(a))

NFV* info. (RQ 5(b))

Risks info. (RQ 5(b))

Hedge information (info.) (RQ 5(b))

VR** of hedge information (RQ 6)

VR of UR***

Hedging gain or loss (RQ 7)

VR of net fair value disclosures and the incremental explanatory power (RQ 8)

Size (RQ 1) Performance (RQ 2) Auditor (RQ 3) Type of firm (RQ 4)

* NFV = Net Fair Value ** VR = Value Relevance *** UR = Unrealised


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5.1 Disclosure Quality of Derivative Information and Firm Characteristics

This research aims to examine the association between the information quality of

derivative disclosures and firm characteristics. Prior Australian studies have examined

the relationship between the use of derivatives and firm characteristics. However, no

attempt has been made to examine the relationship between disclosure quality and

firm characteristics. Berkman, Bradbury, Hancock and Innes (2002) and Nguyen and

Faff (2002), for example, examine the relationship between firm characteristics and

derivative use. Both studies indicate that firm size and leverage contribute to

derivative usage. In addition, Nguyen and Faff (2002) indicate that liquidity43 is also

associated with usage. These findings are consistent with prior studies in the U.S.

such as Nance, Smith and Smithson (1993) and Smith and Stulz (1985).

Géczy, Minton and Schrand (1997) provide evidence that the characteristics of firms

contribute to the decision to use derivative instruments. They indicate that the firms

more likely to use currency derivatives are: a) firms with the greatest economies of

scale in implementing and maintaining a risk management program, b) firms that have

currency exposure resulting from foreign operations and c) firms that have relatively

higher levels of foreign-denominated debt.

Users of financial statements employ several techniques when evaluating accounting

information. Among these techniques they may include an assessment of the quality

of the information (Imhoff, 1992). Prior studies have measured quality based on the

corporate disclosure practices measured by the Financial Analysts Federation (FAF)

43 Berkman et al. (2002) reported that less liquid industrial firms tend to use derivatives.


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and the Association for Investment Management and Research (AIMR) or a self-

constructed disclosure index developed based on voluntary and/or mandatory

disclosures. These studies have examined the relationship between corporate

disclosure practices and firm characteristics.

The Australian Accounting Standards Board (AASB) is responsible for developing

and promulgating approved standards. Once an AASB standard becomes an

applicable accounting standard firms are expected to comply with its requirements.

The Corporations Law and the Australian Securities and Investment Commission

(ASIC) Act have granted statutory force to the AASB standards. This legal power

should ensure firms’ compliance with the standards, and this should lead to high

quality accounting information being reported. This statutory force indicates that

AASB standards are stringent disclosure standards. For the purposes of this study,

AASB 1033 is assumed to be a “high quality” disclosure standard. This is reasonable

because the extensive nature of its disclosure requirements is designed to overcome

the lack of guidance with regards to recognition and measurement. Thus, firms that

prepare their annual report based on this standard are said to provide “high quality”

derivative information. Therefore, it can be argued that the disclosures required by

AASB 1033 are of high quality and firms that hedge externally are expected to

disclose all the information required by the standard. In other words, the financial

statements reveal the events, transactions, judgements and estimation44 of derivative

instruments so that investors can make better decisions.

44 Pownall and Schipper (1999) defined these as transparency, which is often termed “disclosure quality”.


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The following research questions are developed based on prior research that has

explored the quality of other disclosures in financial statements (Firth, 1979; Cooke,

1989, 1991 and 1992; Imhoff, 1992; Malone, Fries and Jones, 1993; Singhvi and

Desai, 1971; Ahmed and Nicholls, 1994; Wallace, Naser and Mora, 1994; Wallace

and Naser, 1995). These studies provide evidence on the association between

corporate disclosure practices and firm characteristics such as size, leverage,

profitability, listing status, external auditor, scope of business and industry type.

Researchers use several theories to explain these characteristics in relation to

corporate disclosure practices. These theories include agency costs45, political

costs46,47, proprietary costs, corporate governance and information asymmetry

(Ahmed and Courtis, 1999).

5.1.1 Size

Firm size is one of the characteristics that has been extensively related to disclosure

policy. There are many reasons why large firms might disclose more information

(Cooke 1991). Singhvi and Desai (1971) indicate that larger firms are expected to

provide more transparent information as they incur lower costs of accumulating

detailed information, they have more marketable securities and they have greater ease

of financing. Cooke (1989) suggests that a further incentive for greater transparency

is to reduce political costs. Cooke (1989, 1991), Firth (1979), Singhvi and Desai

(1971), Wallace et al. (1994), Wallace and Naser (1995), Ahmed and Nicholls (1994),

45 Agency costs comprise monitoring expenditures by the principal, the bonding expenditures by the agent and the residual loss (Jensen and Meckling, 1976). 46 Whittred, Zimmer and Taylor (2000, p. 43) define political costs as “the ability of the government and its regulatory agencies or other interest groups to effect wealth redistributions between claimants in the firm or between the firm and other sectors of its industry or the economy”. 47 Watt and Zimmerman (1986) contended that the management’s choice of accounting methods might be influenced by political costs.


84

Riahi-Belkaoui (2001) and Ali, Ahmed and Henry (2003) provide evidence that firm

size is positively associated with disclosure level. With respect to the oil and gas

industry, however, Malone et al. (1993) reports that there is no association between

size and disclosure quality. However, in this study, it is expected that large firms will

provide high quality derivative information because they use derivatives extensively,

there are economies of scale associated with disclosure and they may be subject to

political and monitoring costs48. This leads to the first research question:

Research Question 1: Do larger firms in the extractive industries provide more transparent derivative disclosures in their financial statements than smaller firms?

5.1.2 High Performance Firms

The performance of firms has also been identified as a factor affecting disclosure

quality. A profitable firm may provide more detailed information to communicate

good news to investors in order to improve firm value (Ali et al., 2003) and to boost

management compensation (Wallace et al., 1994). However, while Ali et al. (2003)

provide evidence of a positive relationship between profitability and compliance level,

Wallace and Naser (1995) identify a negative relationship between these variables.

Therefore the second research question is:

Research Question 2: Do higher performance firms in the extractive industries provide more transparent derivative disclosures in their financial statements than lower performing firms?

48 According to Jensen and Meckling (1976), the costs are not restricted to measuring or observing the behaviour of the agent, but also include the efforts of the principal to control the behaviour of the agent through budget restrictions, compensation policies, operating rules etc.


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5.1.3 Auditor

Auditors play an important role in determining the quality of information disclosed by

their clients. Large audit firms are associated with high quality reporting. DeAngelo

(1981) and Fama and Jensen (1983) indicate that this is because large audit firms tend

to have many clients, and have incentives to maintain independence from their clients.

Therefore, they tend to report mis-statements and non-compliance with mandatory

reporting requirements. Moreover, the reputations of large audit firms are diminished

when their clients provide low quality annual reports (Ali et al., 2003) or when they

commit fraud or mislead by certifying the annual reports of their clients (Owusu-

Ansah, 1998). The best example of this is the collapse of Arthur Andersen, the auditor

of Enron. Therefore, larger audit firms tend to influence their clients to provide high

quality information. However, empirical studies provide mixed results. Singhvi and

Desai (1971), Ahmed and Nicholls (1994) and Wallace and Naser (1995) found that

auditor size is positively associated with disclosure level but no significant association

is documented in Firth (1979), Malone at al. (1993), Wallace et al. (1994) and Ali et

al. (2003). This leads to the following research question:

Research Question 3: Is the disclosure quality of derivative information in the financial statements of firms in the extractive industries influenced by the choice of auditor?

5.1.4 Type of Firm in the Extractive Industries.

A unique feature of Australian firms in the extractive industries, especially the mining

industry, is that they are permitted by legislation to form a no-liability company. This


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is due to the uncertain or speculative nature of the industry, especially in the

exploration phase. In a no-liability company, shareholders are not legally liable to

pay any calls, either while the company is a going concern or in its winding up (Ford,

1986). Therefore, it is expected that disclosure transparency may differ between no-

liability firms and limited liability firms. Further, no-liability firms tend to be smaller

firms, and because they tend not to have reached the production phase, they are also

less likely to be profitable. As a result, they may be reluctant to provide transparent

information due to: a) the high cost of accumulating detailed information, b) the fact

that they may feel that the disclosure could endanger their competitive position

(Singhvi and Desai, 1971) and c) they are not subject to political costs (Cooke, 1989).

The above leads to the following research question:

Research Question 4: Do no-liability companies in the extractive industries have less transparent derivative disclosures in their financial statements than limited liability firms?

5.2 Value Relevance of Derivative Disclosures

5.2.1 Disclosure Quality and the Market Value of Firms Koonce, McAnally and Mercer (2000) indicate that the share price of firms that

externally hedge is higher than for other firms. Firms are motivated to hedge as

hedging is a useful indicator of managerial quality. DaDalt, Gay and Nam (2001)

indicate that hedging could present shareholders with a more informative picture of a

firm’s true earnings capacity. Therefore, it is expected that more transparent

disclosures will have an impact on the share prices of the firms.


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While the previous research questions investigate the association between firm

characteristics and the transparency of derivative information, the following research

questions examine the perception of market participants on the transparency of

derivative disclosures. This approach examines quality using capital markets data. It is

generally believed that the quality of financial reporting affects the decisions of

capital markets participants (Kothari, 2000; Heflin, Shaw and Wild, 2001). If market

participants value the information as high quality then a positive association between

the information and the share prices is expected. Hence, enhanced disclosure not only

benefits the disclosing firms but also the investors (Gelb and Zarowin, 2002). A large

body of literature assesses the value relevance of accounting data by examining its

association with share prices (Barth, 1994; Barth, Beaver and Landsman, 1996;

Nelson, 1996; Venkatachalam, 1996). Barth, Beaver and Landsman (2001) argue that

accounting information can be value relevant if it reflects the relevance of the


reflected in share prices.

Lang et al. (2003) and Gelb and Zarowin (2002) investigate the association between

accounting quality and share prices. Lang et al. (2003) provide evidence that cross-

listed firms, as compared to non-cross-listed firms, have higher accounting quality as

their accounting data is more highly associated with price. Gelb and Zarowin (2002)

examine the association between the level of corporate disclosure and stock prices.

They find that high disclosure firms have higher earnings response coefficients

(ERCs) than low disclosure firms.


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Based on the above, it is expected that more transparent derivative disclosures will

have a positive impact on the share price of firms in the extractive industries. Hence,

the next research question is:

Research Question 5(a): Are the share prices of firms in the extractive industries associated with more transparent financial statement derivative disclosures?

However, the above research question does not provide information about which

particular derivative disclosures are important in firm valuation. This leads to the

following research question:

Research Question 5(b): Which AASB 1033 disclosure components are value relevant?

The following research questions examine the importance of each disclosure

component required under AASB 1033. This is important because prior studies

provide evidence that items disclosed in the notes to the financial statements are not

value relevant. Nevertheless, results from those studies might not represent other

industries and jurisdictions.

5.2.2 Value Relevance of Disclosure of Hedges of Anticipated Transactions

Firms are required by AASB 1033 to disclose information that is useful for decision-

making. This includes both qualitative and quantitative information. The market may

react differently to accounting information which varies in quality (Imholf, 1992).


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Because of subjectivity, it is very difficult to determine whether qualitative

information is of higher quality than quantitative information, or vice versa. For

example, in some cases financial statement users are more interested in qualitative

information (such as forward looking information) rather than quantitative

information (such as earnings forecasts) since users may have their own ability to

determine the quantitative amounts.

Paragraph 5.8 AASB 1033 requires firms to disclose: a) a description of the

anticipated transactions, including the period of time until they are expected to occur,

b) a description of the hedging instruments and c) the amount of any deferred or

unrecognised gain or loss49 and the expected timing of recognition as revenue or

expense. This information permits the users of an entity’s financial report to

understand the nature and effect of hedges of anticipated future transactions. The

research question is:

Research Question 6: Do the share prices of extractive industries firms reflect the information that firms hedge their exposure to the risks from anticipated transactions?

The issue of whether the disclosed items are considered as important as recognised

items has been examined by previous researchers. Davis-Friday, Folami, Liu and

Mittelstaedt (1999) define recognition as any amount that has been recorded and

included in the total of the income statement or balance sheet. On the other hand,

disclosure is any information (qualitative or quantitative in nature) that is contained in

the notes to the financial statements that is not recognised in the income statement or

49 The unrecognised gain or loss may result from the difference between net fair value and the historical cost of financial instruments. The amount includes all accrued gains and losses on hedge instruments.


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balance sheet. Pfeiffer (1998), Aboody (1996) and Davis-Friday et al. (1999) provide

evidence that market participants value differently the recognised and disclosed items.

Market participants may consider the disclosed information as unimportant and

therefore, they ignore the disclosure (Barnes, 2001).

Pfeiffer (1998) provides evidence that the off-balance sheet information is valued

differently from on-balance sheet items. Similarly, Davis-Friday et al. (1999) indicate

that the market attaches more weight to the recognised items than to the disclosed

items. AASB 1033 requires firms to disclose the amount of deferred or unrecognised

gain or loss in the note on hedge transactions. However, in the event of early

termination of hedging the gain or loss is deferred until the underlying asset has been

terminated. The above raises the following research question:

Research Question 7: Are the share prices of extractive industries firms associated with the disclosure of the unrecognised hedging gain or loss?


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5.2.3 Value Relevance of Fair Value Disclosures

Fair value accounting50 has become the preferred option of accounting for financial

instruments as opposed to historical cost. A move to fair value is believed to be due to

the belief that market-based information is the most relevant financial data for

financial statement users. The disclosure of fair value information is expected to

provide more useful information for users to assess the effects of derivative

transactions (Rasch and Wilson, 1998).

Prior research has investigated the usefulness of fair value information based on the

relevance and reliability of the information recognised and disclosed in the financial

statements required by the accounting standards. Most value relevance studies

examine the value relevance of fair value information (for example Barth, 1994;

Eccher et al., 1996; Nelson, 1996; Venkatachalam, 1999). Barth (1994) and Barth et

al. (1996) provide evidence that the fair value estimates provide significant

explanatory power beyond the historical costs. However, Eccher et al. (1996) reject

the hypotheses on the incremental explanatory power of fair value. Therefore, the next

research question is:

Research Question 8: Are the net fair value disclosures value relevant and do they provide incremental information over book values for firms in the extractive industries?

50 AASB 1033 defines fair value as the amount for which an asset could be exchanged, or liability settled, between knowledgeable, and willing parties in an arm’s length transaction. The term ‘fair value’ is used interchangeably with mark-to-market, market value-based and market value accounting.


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5.3 Summary This chapter has developed the research questions which draw on both the Australian

institutional environment and prior studies in the areas of disclosure quality and value

relevance research. Because this study focuses on a specific issue, derivative

disclosures, the research questions on firm characteristics are developed based on

prior research that has explored the quality of other disclosures in financial

statements. The current study examines the association between the quality of

derivative information disclosed according to AASB 1033 and firm size, type,

profitability, growth opportunities, leverage and auditor. The value relevance research

questions are based on prior research that has explored the value relevance of fair

values as compared to historical accounting for derivative disclosures. The next

chapter details the research method and data collection procedures used to test these

research questions and presents descriptive statistics.

Chapter 6: Research Design, Data Collection and Descriptive Statistics

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CHAPTER 6 RESEARCH DESIGN, DATA COLLECTION AND DESCRIPTIVE STATISTICS

This chapter outlines the data selection process, research methodology undertaken and

model development in the testing of the research questions developed in chapter five.

Data selection procedures and the period of the study are discussed in section 6.1.

Section 6.2 deals with the specification of the variables used in this study and the

model development. Section 6.3 discusses the estimation procedures, while section

6.4 presents the descriptive statistics. Section 6.5 summarises the chapter.

6.1 Data Selection and Test Period The main source of information for this study is the annual reports of all listed

companies in the extractive industries51. These industries play a significant role in the

Australian economy, where they generate exports worth more than $30 billion in 2000

to 2002 (Department of Foreign Affairs and Trade, 2003a; 2003b). They represent

25% of the listed companies on the Australian Stock Exchange (ASX).

Approximately 27% of the companies are no-liability firms.

All extractive industries firms (354 firms) listed on the ASX for the years between

1998 to 2001 were initially selected. Firms were contacted and asked to provide

annual reports for each year. However, in some cases the annual reports were

downloaded from corporate websites or the Annual Report Collection (Connect 4).

51 According to Deegan (2002) the extractive industries refer to firms which engage in the search for natural substances of commercial value such as minerals, oil and natural gas.


94

Eighty nine firms were excluded because they did not respond to the request and their

annual reports were not available from Connect 4. Further, the data size was reduced

to 149 firms by excluding: a) foreign listed firms, b) newly listed/delisted firms, c)

mining servicing firms, d) firms that have been dormant/under receivership etc. and e)

firms with missing data. 12 firms were eliminated for regression analysis purposes

because of the unavailability of share price data. Therefore, the number of firms

available for this study is 13752.

The annual reports of the firms were manually searched to identify whether these

firms are users of derivative instruments. First, the note on the statement of

accounting policies was examined. This statement provides details of the significant

accounting policies adopted for the financial year. Firms provide information about

the objectives of holding or issuing derivative financial instruments or hedging

activities in this note. Further, in the event that firms fail to indicate their hedging

behaviour in the note on accounting policies, the note on financial instruments was

examined. This manual search concentrated on the terms of hedging instruments,

derivative instruments, forward contracts, foreign currency contracts, futures

contracts, swap agreements and rate swaps. The following are two examples of

accounting policy notes, which indicate the user status:

52 Summary of data selection is presented in Table 6.2.


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Westralian Sand Limited Annual Report 1998

Note 1 Statement of Significant Accounting Policies s) Derivative financial instruments (Other than foreign currency and commodity hedging)

The economic entity enters into consumer price index swap contracts and interest rate swap agreements to manage financial risk.

QCT Resources Limited Annual Report 1999 1. Statement of Accounting Policies

Derivatives

Derivatives transactions are entered into solely to hedge foreign exchange risks, within limit set by the Board of Directors.

The note on financial instruments is another source of information. This note provides

detailed information on each class of financial instruments, including derivative

instruments. A firm is identified as a user if there is information disclosed with regard

to hedging instruments, derivative instruments, forward contracts, foreign currency

contracts, futures contracts, swap agreements and interest rate swaps. The following is

the note on the financial instruments of 1998 annual reports of Westralian Sands

Limited, which indicates the firm is a user of derivative instruments. The note

describes information about: a) the anticipated transaction that exposed the company

to the risk, b) hedging instruments and c) the amount of any deferred or unrecognised

gain or loss53.

53 The amount includes all accrued gains or losses on financial instruments designated as hedges of anticipated transactions. The accrued gain or loss may be unrealised (as a results of carrying the hedging instrument at net fair value) but recorded in the statement of financial position, or it may be unrecognised (if the instrument is carried at cost), or it may be realised. However, the accrued gain or


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Westralian Sands Limited Note 26 Financial Instruments

a) Foreign Exchange Hedging of Revenues The sales revenue of the economic entity is predominantly dominated in United States dollars. In order to protect against adverse exchange rate movements, a proportion of future anticipated sales revenue has been sold forward utilising either forward foreign exchange contract or foreign exchange options.

b) Forward Foreign Exchange Contracts

The table below details outstanding forward contracts to sell US Dollars, and buy Australian dollars as at balance date:

Maturity Sell US

Dollars Buy Australian

Dollars 1998 US$M 1998 A$M Not later than 1 year 16.9 22.8 Later than 1 year but not later than 2 years

6 10.5

The mark to market loss relating to these contracts at 31 December 1998 is A$13.8m (1997 Nil) and is included in provision for loss on currency forward contracts and options.

Table 6.1 presents details on the use of derivative instruments among the 137 firms,

classified by size and type of firm. Column 4 Panel A shows that 56 firms in the

sample are listed as Top 500 Australian companies54 and the majority of these are

limited liability companies. In all years, 44 (78%) firms disclose their use of

derivatives. While only two firms specifically disclose that they do not use

loss has not been recognised in the calculation of net profit or loss pending completion of hedging transaction (paragraph 5.8.2 AASB 1033). 54 These firms are among 94 extractive industries firms in the Top 500 Public Companies listed in the Business Review Weekly (BRW) or Annual Report Collection (Connect 4).


97

derivatives, ten (18%) firms make no specific reference to derivative financial

instruments. The majority of those making no disclosures are no-liability firms.

Table 6.1: The Use of Derivative Financial Instruments for Hedging Purposes

Limited Liability Firm No Liability Firm Total Status 1998 1999 2000 2001 1998 1999 2000 2001 1998 1999 2000 2001

Panel A: Firms from the Top 500 Companies Listed in BRW/Connect 4 (n = 56)

User 35 35 35 35 9 9 9 9 44 44 44 44 Unknown 3 3 3 3 7 7 7 7 10 10 10 10 Non-user 1 1 1 1 1 1 1 1 2 2 2 2 Total 39 39 39 39 17 17 17 17 56 56 56 56 Panel B: Firms from outside the Top 500 companies (n = 81)

User 3 5 8 8 18 16 13 13 21 21 21 21 Unknown 4 4 20 31 47 45 30 17 51 49 50 48 Non-user 0 0 4 5 9 11 6 7 9 11 10 12 Total 7 9 32 44 74 72 49 37 81 81 81 81

Column 4 Panel B of Table 6.1 indicates that 81 firms in the data are outside the Top

500 companies, i.e. they are smaller companies. However, only 21 of these firms

disclose that they use derivative instruments, with the majority of these smaller firms

being no-liability firms. It is evident that there is a sharp decline in the total number of

no-liability companies during the four year period. Further, the majority of the non-

top 500 firms make no disclosures about their use of derivative financial instruments.

Given that these companies are small companies, it can be safely assumed that these

60 firms do not use derivatives. In total, the number of firms that use derivative

financial instruments in this study is 65 firms for each of the years. Only 12 firms

from the top 500 are excluded, 2 being non-users and 10 unknown. Table 6.2

summarises the data selection procedure for the final data of 65 firms.


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Table 6.2: Summary of Data Selection Procedure

Selection Criteria No of Firm No of listed firms in the extractive industries 354 - Firms that did not respond and are not on Connect 4 89 - Foreign firms 19 - Newly listed/delisted firms 43 - Mining servicing/investment firms 6 - Dormant / under receivership 2 - Missing information 46 - Missing share price data 12 Usable annual reports 137 - Non users and unknown status 72 Users 65

6.2 Specification of Variables and Model Development

The following sections discuss variables for the regression analysis and models of

association between disclosure quality and firm characteristics, and value relevance of

derivative information. Subsection 6.2.1 describes the dependent and independent

variables for the regression analysis on disclosure quality and firm characteristics.

Dependent and independent variables for the value relevance analysis are discussed in

subsection 6.2.2.


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6.2.1 Disclosure Quality and Firm Characteristics (Firm Characteristics Model)

6.2.1.1 Variables a) Dependent Variable

The dependent variable for the first part of this study is disclosure transparency,

which is termed “disclosure quality”. A number of previous studies rely on corporate

disclosure quality as measured by users such as the Financial Analysts Federation

(Imhoff, 1992; Sengupta, 1998; Riahi-Belkaoui, 2001; Heflin et al., 2001; Shaw,

2002), the Association for Investment Management Research (Lang and Lundholm,

1993; Lang et al., 2003; Lobo and Zhou, 2001; Bushee and Noe, 2000; Price, 1998;

Botosan and Plumlee, 2003) and the Center for International Financial Analysis and

Research (Hope, 2003a and 2003b). However, these studies examine disclosure

quality based on all the information disclosed in the annual reports and other media.

Other studies measure disclosure quality based on a self-constructed disclosure index.

These include Cooke (1989, 1991 and 1992), Malone et al. (1993), Wallace (1988),

Wallace, Naser and Mora (1994), Botosan (1997), Tower, Hancock and Taplin

(1999), Chalmers (2001), Taplin, Tower and Hancock (2002) and Ali et al. (2003). In

these studies researchers employ either a weighted or a unweighted index (Marston

and Shrives, 1991). A weighted index requires a survey to be conducted so that

financial statement users can rate disclosure items listed by the researchers in order of

importance. The unweighted index is less subjective than the weighted index. In this

case, researchers adopt a dichotomous procedure where a score of one is given for

disclosed items and zero otherwise. Therefore, the index assumes that each of the

items of disclosure is equally important (Cooke, 1991).


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The current study develops an unweighted index for derivative disclosures to

represent disclosure quality based on the information in the financial statements and

notes to the financial statements. This thesis acknowledges that annual reports are not

the only source of corporate reporting. However, focusing on this source will not

reduce the quality of information disclosure since it is generally believed that the

annual report is one of the most important sources of corporate information (Botosan,

1997). As well, these derivative disclosures have been audited. The index is

developed based on the AASB 1033 requirements. As AASB 1033 does not specify

rules for the recognition and measurement of financial instruments, its disclosure

requirements are extensive. Therefore, compliance with the accounting standard is

assumed to provide high quality disclosures. Five categories of information are

identified55. These are policy information, hedges of anticipated future transactions,

risk information, net fair value information and commodity contracts regarded as

financial instruments. A score of one is given for each item based on the detailed

information provided, both qualitative and quantitative, while a score of zero is

allocated if firms failed to provide any information required. Table 6.3 documents the

attributes of the disclosure index.56

55 The disclosures of these items are required by AASB 1033. However, there is no theory on the items to be included in the disclosure index, and the number of items in the index have varied from one research study to another (Marston and Shrives, 1991; Wallace and Naser, 1995). 56 A score two is given for paragraphs 5.2(b), 5.6(a), 5.6(b), 5.8(a) and 5.8(c) since these paragraphs require two information.


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Table 6.3: Components of Derivative Disclosure Index Reference Score Policy Information

Accounting policies and method adopted Para 5.2 (a) 1 a) Extent and nature of the underlying financial instruments, b)

including significant terms and conditions that may affect the amount, timing and uncertainty of future cash flows.

Para 5.2 (b) 2

Objectives for holding or issuing derivative financial instruments Para 5.3 1 Component score of policy information 4 Hedges of Anticipated Transactions

a) A description of the anticipated transaction and b) including the period of time until they are expected to occur.

Para 5.8 (a) 2

A description of the hedging instruments. Para 5.8 (b) 1 a) Amount of any deferred or unrecognised gain or loss and b)

the expected timing of recognition as revenue or expense. Para 5.8 (c) 2

Component score of hedges of anticipated transactions 5 Risk Information

Contractual repricing or maturity dates for interest rate risk Para 5.4 (a) 1 Effective interest rates or weighted average Para 5.4 (b) 1 The maximum amount of credit risk exposure at reporting date Para 5.5 (a) 1

Component score of risk information 3 Net Fair Value Information

a) The aggregate net fair value as at the reporting date and b) showing separately the aggregate net fair value of those financial assets or financial liabilities which are not readily traded on organised markets in a standardised form.

Para 5.6 (a) 2

The method or methods adopted in determining net fair value. Para 5.6 (b) 1 Any significant assumptions made in determining net fair value. Para 5.6 (c) 1 The carrying amount and the net fair value of either the

individual asset or appropriate groupings of those individual assets.

Para 5.7 (a) 1

a) The reasons for not reducing the carrying amount and b) including the nature of the evidence that provides the basis for management’s belief that the carrying amount will be recovered.

Para 5.7 (b) 2

Component score of net fair value information 7 Commodity Contracts Information

Contract for commodity gold Para 5.9 (a) 1 Component score of commodity contracts information 1

To develop the index the notes to the financial statements are examined. First, the

note containing the statement of accounting policies is examined57. Basically, firms

disclose the objectives for holding or issuing derivative financial instruments in this

note. In the event that firms fail to indicate their hedging behaviour in this note, the

note on financial instruments is examined. The search concentrates on the terms of 57 This statement provides details of the significant accounting policies adopted for the financial year.


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hedging instruments, derivative instruments, forward contracts, foreign currency

contracts, futures contracts, swap agreements and rate swaps. There are three

possibilities with disclosures. The entities either: a) disclose that they hedge the risk

internally or externally, b) disclose that they do not hedge, or c) disclose nothing

about hedging.

After identifying the hedging behaviour of firms, the next step is to capture

information about hedge disclosures and net fair values of financial assets, financial

liabilities and derivative financial instruments. This information is disclosed in the

note on financial instruments. The followings are example of notes of financial

instruments taken from Consolidated Rutile Limited 1999 Annual Report and

Centennial Coal Company Limited 2001 Annual Report, respectively:

Consolidated Rutile Limited, 1999 Annual Report

Note 27 Financial Instruments

a) Off-balance sheet derivative instruments The parent entity is party to financial instruments with off-balance sheet risk in the normal course of business in order to hedge exposure to fluctuations in foreign exchange rates and interest risk. Hedging of foreign currencies is effected through a combination of forward contracts and options. Exposure to interest rate fluctuations is managed through interest rate swaps and options. i) Foreign exchange hedging of revenues The company’s sales revenue is predominantly denominated in United States dollars. In order to protect against adverse exchange rate movements a proportion of future anticipated sales revenue has been sold forward utilising either forward exchange contracts or foreign exchange options.


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Centennial Coal Company Limited 2001 Annual Report

47 Financial Instruments

g) Hedges of Anticipated Future Transaction The consolidated entity has entered into contracts to supply coal to customers denominated in US Dollars. The consolidated entity has entered into forward foreign exchange contracts and foreign exchange option contracts to hedge the exchange rate risk arising from these anticipated future transactions.

As at the reporting date the aggregate amount of the unrealised losses under forward foreign exchange contracts and foreign exchange option contracts relating to anticipated future transactions is $6,537,000. Such unrealised losses will be realised during the 2002 financial year when the anticipated future transactions take place.

To make each component of the score58 add equally to the total score, the component

score is divided by the number of items in that component. Therefore, each

component contributes a score of one to the total score out of five. The quality of

derivative disclosures is measured by dividing the total score for each firm by the total

possible score for that firm. For example, if a firm provides all information listed in

Table 6.3, the “disclosure quality” of that firm is one (i.e. 5/5 or 1), and thus the firm

is said to provide “high quality” disclosures or transparent derivative instruments

information (Table A 3 Appendix A presents an example of the disclosure quality of

BHP Billiton). However, firms are not penalised if the information is not relevant to

their situation, i.e. the total score and total possible score are both reduced. The whole

annual report is examined to ensure that the items are not relevant to the company.

58 This refers to component of the disclosure index, and is not relate to component analysis.


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b) Independent Variables

i) Size of the Firm

Size of the firm is one of the characteristics that have been found to be consistently

related to disclosure policy. Several studies use the sum of book value of debt and

market value of common equity (as in Knoff, Nam and Thornton, 2002) and net sales

(Stanga, 1976; Cooke, 1989; Wallace et al., 1994) as a proxy for size. However, the

current study defines size as the log of total assets, which represents SIZE. This is

because the measure “total assets” is the least affected by market fluctuations in the

oil and gas industry (Malone, Fries and Jones, 1993). Asset size has been used in

studies by Singhvi and Desai (1971), Firth (1979), Wallace (1988), Cooke (1989),

Imhoff (1992) and Malone et al. (1993).

ii) Performance

Performance of the firms has also been identified as a factor impacting disclosure

quality. High performance is measured by two variables: profitability (PROFIT) and

price-earnings ratio (PE). The former measures current performance while the latter

provides a measure of the market’s perception of a firm’s expected future

performance. Profitability is determined by dividing earnings before tax by total

assets. This is consistent with prior studies such as Ali et al. (2003) and Wallace et al.

(1994). The PE ratio is derived by dividing price by earnings before extraordinary

items per share.


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iii) Type of Firm in the Extractive Industries

Australian company law has an optional provision allowing mining industry firms to

form a company as a no-liability firm (NL). As at 2001, 27% of listed firms in the

extractive industries are no-liability firms. The majority of these firms are small

firms. Table 6.1 indicates that in 1998, out of the total 137 firms, 91 are identified as

no-liability firms, and this number reduces to 54 in 2001 with the majority of these

firms outside the Top 500 companies (i.e. small firms). Out of these, 26 firms and 22

firms use derivatives in 1998 and 2001, respectively. Whether the firm is a no-liability

firm or a limited liability firm is indicated by TYPE. The measure of TYPE is a

dichotomous variable of one to represent no-liability firm, and zero otherwise. Cooke

(1989) and Wallace and Naser (1995) indicate that type of industry may affect the

level of disclosure. However, since this study examines disclosure within one

industry, comparing one type of firm (no-liability firm) with another type of firm

(limited liability firm) in this industry, will provide evidence of whether the type of

firm will impact on the information provided.

iv) Auditor

The auditor plays a major role in clients’ disclosure policies and practices. To

examine this association the variable AUDIT is used to distinguish between the use of

a Big 5 (or Big 6) auditor and a smaller audit firm. About 53 of the total 65 firms in

1998 are audited by Big 5 (Big 6) audit firms, and the number increases to 56 firms in


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2001. As in prior studies a dichotomous variable is used with a score of one given to a

Big 5 (Big 6) firm, and zero otherwise.

v) Control Variables

Three control variables that have been found in prior research to be associated with

the quality or extent of disclosure are also included. Two variables are used to

measure growth opportunities and one variable is used as a proxy for leverage. First,

market-to-book ratio (MTB), which measures the market value of the firm divided by

the book value of tangible assets. This variable provides a measure of the market’s

perceptions of the value of the firm relative to assets-in-place, with a high value

suggesting growth opportunities (Smith and Watts, 1992; Gaver and Gaver, 1993).

Growth firms are expected to disclose more information as these firms have greater

information asymmetry and agency costs (Eng and Mark, 2003). However, Eng and

Mak (2003) find no significant relationship between disclosure and growth

opportunities, which is represented by a factor score of growth variables (market-to-

book value of assets, market-to-book value of equity and price-earnings ratio).

Second, a dichotomous variable indicating whether or not the firm engages in

research and development activities (R&D) is used; i.e. one for firm that is involved

in R&D, zero otherwise. R&D activities are an indication that the firm is likely to

grow in the future (Gaver and Gaver, 1993; Percy, 2000; Clinch, 1991).

To identify whether firms in the extractive industries engage in R&D activities, notes

to the financial statements of each firm are examined. Firms disclose their R&D

activities either in: a) the note on accounting policies, b) the note on operating

income, c) the note on income tax or d) the note on intangibles or non-current assets.


107

An example of a firm that disclosed R&D activities in the note on income tax is taken

from Centennial Coal Company Limited in 2001. This firm is coded as one. Further,

some firms may use R&D syndicates for financing R&D. These firms are also coded

as one. An example of a firm that uses R&D syndicates is Energy Equity Corporation

Limited. Through out 1998 to 2000, there are 21 firms that use derivative instruments

and which are also involved in R&D, and this number reduces to 20 firms in 2001.

The other control variable is leverage, which is measured as debt divided by the book

value of common equity. Firms with high leverage are expected to reduce disclosure

as leverage controls the free cash flow problem, and as in Jensen (1986), the agency

costs of debt are controlled through restrictive debt covenants in debt agreements

rather than increased disclosure of information in financial reports (Eng and Mak,

2003). Several prior studies such as Hossain and Adams (1995) and Ali et al. (2003)

provide evidence that leverage is not significantly related to disclosure. However,

Ahmed and Courtis (1999) indicate that leverage is positive and significantly related

to disclosure levels, especially when it is proxied by the debt-to-equity ratio and the

debt-to-total asset ratio. Specific to the oil and gas industry, Malone et al. (1993)

indicate that firms with high debt-to-equity ratios disclose greater financial

information than firms with low debt-to-equity ratios.

6.2.1.2 Regression Model

Equation 6.1 examines the association between the information quality of disclosures

of derivative instruments and firm characteristics. Firm characteristics are represented

by: firm size (SIZE); profitability (PROFIT); price-earnings ratio (PE); market-to-

book ratio (MTB); type of firm in the extractive industries (TYPE); type of auditor


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(AUDIT), leverage (DTE) and research and development firms (R&D). The function

of TRANSP is to examine the relationship between firm characteristics and disclosure

transparency. The measure of disclosure transparency represents a firm’s actual

disclosure scores as a percentage of that firm’s total possible disclosure scores. The

regression model is expressed as:

TRANSPit= α0+α1SIZEit+α2PROFITit+α3PEit+α4TYPEit+α5AUDITit

+α6MTBit+α7R&Dit +α8DTEit+ εit (6.1)

Where: TRANSP = disclosure transparency = firm’s actual disclosure score/firm’s

total possible disclosure score SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class

of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity i = firm t = year

6.2.2 Value Relevance of Derivative Disclosures (Market Value Model) Value relevance studies examine the relevance and reliability of information

recognised and disclosed in the financial statements required by the accounting

standards. These studies are an empirical operationalisation of the criteria, relevance

and reliability. Information can be value relevant if it reflects the relevance of


reflected in share prices (Barth, Beaver and Landsman, 2001). Financial statements

present the economic events of a business entity, which occur during the reporting


109

period, and hence, the information may be value relevant to investors. Unlike

managers, shareholders and investors have limited access to this information, and

therefore, the disclosed information, such as hedge and net fair value information,

may be of value relevance to them.

To test whether the hedge disclosures and net fair value of financial assets (FA),

financial liabilities (FL) and derivative financial instruments are useful in equity

valuation, this study employs valuation models. There are a few valuation models

being used in capital markets research, including the dividend-discounting model, the

earnings capitalisation model and the residual income model. The balance sheet

model is quite popular in value-relevance studies, i.e. disclosure regulation research

(Kothari, 2001).

Unlike most previous research in value relevance, this study develops the models

based on Ohlson (1995)59. Ohlson’s model provides a direct link between accounting

amounts and firm value which is absent from other models (Barth, 2000). More

importantly, the model specifies how to estimate firm value from accounting amounts

rather than relying on market prices, permitting researchers to specify tests relating to

perceived mispricing of shares and providing a link between financial analysis and

valuation (Barth, 2000).

Ohlson expresses firm value as book value, plus discounted future expected abnormal

earnings. The model is derived based on two primary assumptions: a) the relation

59 Simko (1999) also developed a valuation model based on Feltham and Ohlson (1995).


110

between value and expected future earnings: i.e. ( )

+

+∑∞

=Ε≡

ττ

τ r

tdttP

11 60 and b) the clean

surplus relation (change in book value to equal earnings minus net dividends61). The

model is:

(6A)

where: Pt = the market value (price) of the firm’s equity at date t yt = net book value at date t Rf = the risk-free rate plus 1 Et [.] = the expected value operator conditioned on the date t information xa = abnormal earnings define as xt – (Rf – 1) yt-1

Adding the third assumption related to information dynamic, i.e. the time series

behaviour of abnormal earnings, Ohlson derives:

ttttt vdxkykP 2)()1( αϕ +−+−= (6B)

where: k = (Rf –1)α1= (Rf –1)ω/ (Rf –ω) φ = Rf/(Rf-1) v = information other than abnormal earnings xt = earnings for the period (t-1, t) dt = net dividend paid at date t ω and α are known parameters between zero and one.

Equation (6B) indicates that the valuation model can be viewed as a weighted average

of earnings and book value. The equation provides a better understanding of the

relative valuation implications of book value and net income in valuation (Barth,

2000). Therefore, the framework for examining the cross-sectional relation between 60 Where Pt is the price of the firm’s equity at time t, Et[dt+τ] is the expected dividends paid at time t+τ conditional on time t information and r is the discount rate that is assumed to be constant. 61 tttt dxyy −+= −1 , where y denotes book value of current year (t) and the previous year (t-1).

[ ]attftt xRyP τ

τ

τ+

∞

=

− Ε+= ∑1


111

firm value and hedge disclosures, and net fair value information is based on equation

(6B). Equation (6B) is cited as the theoretical foundation for studies between share

price, book value (and its components) and earnings (and its components) (Easton,

1999 and Ota, 2001), also known as the price model. The linear regression is:

Pit = α0 + α1bit+ α2xit +εit (6C) where, Pit is share price at year end t for firm i, bit is book value of equity at year end

t for firm i and xit is earnings for year t available to firm i’s common shareholders.

To investigate the value relevance of derivative disclosures, models are developed

from Equation 6C. In order to examine how the model behaves within this data, the

following model is estimated.

itititti EBVMV εααα +++= 210 (6D)

where:

MV = market value of firms’ common equity measured three months following the financial year

BV = book value of equity at year end E = earnings for year available to firm’s common shareholders I = firm t = year However, since the model violates the normality and equal variance assumptions, log

transformation is applied on the market value. The new model is specified below.

Pit = α0 + α1BVit+ α2Eit +εit (6.2)

Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BV = book value of equity at year end E = earnings for year available to firm’s common shareholders i = firm t = year


112

The log model satisfies the normality assumption. However, this model suffers from

heteroscedasticity therefore, White’s Heteroscedasticity Corrected Statistics (White,

1980) were employed.

6.2.2.1 Dependent Variable

Market value is represented by the number of outstanding shares multiplied by the

firm’s share price at the last trading day of the three months following the end of the

firm year. This date is chosen to ensure that the information is in the public domain

(Barth et al., 1996; Nelson, 1996). The share prices were obtained from the share

prices database of the University of Queensland Business School and The Australian

newspaper.

6.2.2.2 Independent Variables

To examine the value relevance of derivative disclosures, a number of valuation

models are developed based on Ohlson (1995). Two basic independent variables of

Ohlson (1995) are book value of equity and earnings. As the models were developed

additional variables added. These variables are classified into two groups. The first

group examines the association between disclosure quality of derivative information

and market value. The second group is included to examine the association between

market value and specific items required to be disclosed; for example, a hedge of an

anticipated transaction and net fair value. Table 6.4 summarises the independent

variables employed in the market value models. These variables are discussed below.


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Table 6.4: Summary of Independent Variables Employed in the Market Value Models

Research Question Models Used Specific Variables Used Common Variables Used

Discussed in

RQ 5(a): Are the share prices of firms in the extractive industries associated with high quality financial statement derivative disclosure?

Pit = α0 + α1BVit+ α2Eit + α3 TRANS,it + εit

TRANSP, which represent disclosure quality of derivative information.

Book value of equity (BV) and earnings (E).

Section 6.2.2.2 (a) (i)

RQ 5(b): Which AASB 1033 disclosure information is value relevant?

Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+ε

Component score of net fair value information (CINFV), Component score of hedge information (CIHedge) and Component score of risk information (CIRisk)


Section 6.2.2.2 (a) (i)

RQ 6: Do share prices of extractive industries firms reflect the information that firms hedge their exposure to the risks from anticipated transactions? RQ 7: Are the share prices of extractive industry firms associated with the disclosure of the

Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4OBDIit + α5URGLit+ εit

Component score of hedge information (CIHedge), off-balance sheet derivative financial instruments (OBDI) and unrealised gain or loss financial asset or liability (URGL).


Section 6.2.2.2 (b) (i)


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Research Question Models Used Specific Variables Used Common Variables Used

Discussed in

unrecognised hedging gain or loss?

RQ 8: Are the net fair value disclosures value relevant and do they provide incremental information over book values for firms in the extractive industries?

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + α4TFFIit + α5OBDIit + α6CINFV,it + εit

Net fair value of financial instruments (TFFI), off-balance sheet derivative financial instruments (OBDI) and CINFV.

Book value of non-financial instruments (BVNFI), book value of financial instruments (TBFI) and earnings (E)

Section 6.2.2.2 (b) (ii)

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit + α6OBDIit + α7CINFV,it + εit

Unrealised gain or loss of financial assets (DIFFA) and financial liabilities (DIFFL) off-balance sheet derivative financial instruments (OBDI) and CINFV.

Book value of non-financial instruments (BVNFI), book value of financial instruments (TBFI) and earnings (E)

Section 6.2.2.2 (b) (iii)


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a) Value Relevance of Disclosure Quality

i) Transparency

Transparency has been defined in a previous subsection as a firm’s actual disclosure

score as a percentage of that firm’s total possible disclosure score. The purpose of

including this variable is to provide evidence on whether market participants value

transparency of derivative disclosures as an important factor in firm valuation.

Moreover, incorporating transparency or disclosure quality provides direct evidence

on the relationship between the market value of the firm and the quality of

information rather than relying on proxies (such as earnings) or the assumption of

high quality (such as in Lang, Ready and Yetman, 200362). Two models adapted from

Ohlson (1995) are developed. Equation 6.3 specifies the relationship between market

value and disclosure quality (transparency), book value of equity and earnings.

Pit = α0 + α1BVit+ α2Eit + α3TRANSPit+εit (6.3)

where:

P = natural log of market value of firms’ common equity measured three months following the financial year

BV = book value of equity at year end E = earnings for year available to firm’s common shareholders TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s

total possible disclosure scores. i = firm t = year

62 They conclude that cross-listed firms provide high quality accounting information since they are less aggressive in managing earnings, recognise losses in a timely manner, and whose earnings are highly associated with share prices.


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However, including TRANSP alone in the Ohlson model does not recognise the

importance of each component of the disclosure index in firm valuation. Therefore,

including each component of transparency helps us to understand users’ perceptions

of the worth of qualitative and quantitative information disclosed in financial

statements. This enables us to identify which information is more valuable to market

participants and hence it should help standard-setters in developing new standards.

Policy information is excluded from the model since it is highly correlated with the

other components, giving rise to potential multicollinearity problems. Also excluded

is commodity contract information because the analysis reveals that the majority of

firms are not involved in this type of contract. The model therefore includes

information on hedges of anticipated future transactions, risk information and net fair

value information. These components are important as they may unmask the risks

attached to the instruments and will help investors identify the potential benefits and

costs of their investment. Perhaps the most important information is net fair value as it

may provide more useful information for users to assess the effects of derivative

transactions (Rasch and Wilson, 1998). The model is specified in equation 6.4.

Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4CINFV,it + α5CIRisk, it+εit (6.4)

where:

P = natural log market value of firms’ common equity measured three months following the financial year

BV = book value of equity at year end E = earnings for year available to firm’s common shareholders CIHedge = component score of hedge information CINFV = component score of net fair value CIRisk = component score of risk information i = firm t = year


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b) Value Relevance of Hedge Transactions, Net Fair Value and Unrealised Gain or

Loss on Financial Instruments

i) Hedge Disclosure

To explore research question 6 and 7, the component score of hedge information

(CIHedge) is included as an additional explanatory variable in the Ohlson model

(Ohlson, 1995). The significance of CIHedge will indicate the importance of disclosing

hedge information in the annual report. This is specified below:

Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + εit (6.5) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year, BV = book value of equity at year end, E = earnings for year t available to firm i’s common shareholders CIHedge = component score of hedge information t = time i = firm

Given the significance of hedge information this leads to the need to explore the

importance of information related to the hedge information. Therefore, additional

explanatory variables are included in the model. These are the unrealised gain or loss

on financial instruments (URGL) and the off-balance sheet derivative financial

instruments (OBDI), which are disclosed in notes to the financial statements (see

equation 6.6). These variables are continuous variables.


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Firms that hedge their exposure describe their rationale for hedging and provide a

description of the derivative instruments used for hedging. This is disclosed in the

note on financial instruments. Firms may hedge to mitigate losses from the

anticipated transactions such as future commodity production, future expenditure in

foreign currency, fluctuations in interest rates and currency prices and future sales

revenue. Basically, the anticipated transactions can be grouped into: commodity

production, interest rate fluctuations and currency fluctuations. The instruments used

to hedge the anticipated transactions are normally based on the types of exposure.

These instruments are interest rate contracts (swaps, futures and forward), foreign

exchange contracts (forward and options) and commodity contracts (forward and

options). The following note on financial instruments was taken from Consolidated

Rutile Limited, 1999 annual report.

Note 27 Financial Instruments

a) Off-balance sheet derivative instruments The parent entity is party to financial instruments with off-balance sheet risk in the normal course of business in order to hedge exposure to fluctuations in foreign exchange rates and interest risk. Hedging of foreign currencies is effected through a combination of forward contracts and options. Exposure to interest rate fluctuations is managed through interest rate swaps and options. ii) Foreign exchange hedging of revenues

The company’s sales revenue is predominantly denominated in United States dollars. In order to protect against adverse exchange rate movements a proportion of future anticipated sales revenue has been sold forward utilising either forward exchange contracts or foreign exchange options.


119

A considerable number of firms who hedge their exposure to risks did not disclose the

amounts of deferred or unrecognised gain or loss, even though it is a requirement to

disclose this amount according to AASB 1033. This might be due to the fact that the

firms did not hold derivatives at the balance sheet date.

The relationship between market value and hedge information is specified in equation

6.6. Including OBDI in the equation will provide evidence on the significance of

derivative instruments in firm valuation. Including CIHedge might provide evidence of

whether the qualitative information is considered as important as quantitative

information (book value of equity, earnings, off-balance sheet derivative financial

instruments and unrealised gain or loss of financial assets and liability). This is of

particular interest because paragraph 5.8 AASB 1033 requires firms to disclose both

qualitative and quantitative information. The component score (CIHedge) is included to

represent this requirement.

Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4OBDIit + α5URGL it+ εit (6.6) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year, BV = book value of equity at year end, E = earnings for year t available to firm i’s common shareholders CIHedge = component score of hedge information URGL = unrealised gain or loss of financial assets and financial OBDI = off-balance sheet derivative financial instruments t = time i = firm


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ii) Net Fair Value

Paragraph 7 AASB 1033 defines net fair value as the fair value after deducting

(adding) costs expected to be incurred where the asset (liability) is to be exchanged

(settled). Firms disclose the net fair value in the note on financial instruments.

However, some firms disclose the net fair value in another note. For example,

Auridiam Limited did not disclose the net fair value for investments in the note on

financial instruments. However, the firm discloses the net fair value of investment in

the note on investments.

To explore research question 8, the net fair value information is included in the

Ohlson model. The significance of the net fair value information indicates the

importance of disclosing net fair value information in the corporate annual report.

This model is specified in equation 6.7.

Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + εit (6.7) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BV = book value of equity at year end, E = earnings for year t available to firm i’s common shareholders CINFV = component score of net fair value t = time i = firm Analogously to hedge information, the significance of CINFV indicates the possibility

of exploring the importance of detailed information required by paragraph 5.6 AASB

1033. To investigate the value relevance and explanatory power of net fair value,

equation 6.7 is expanded. The book value of equity (BV) is separated into the book

value of financial instruments (TBFI) and the book value of non-financial instruments


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(BVNFI). Both are recognised in the balance sheet. TBFI is computed as the sum of

financial assets less financial liabilities, current and non-current. BVNFI is measured

as the total book value of equity less the book value of financial instruments. This is

specified in equation 6.8.

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + α4CINFV,it + εit (6.8) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments CINFV = component score of net fair value t = time i = firm

Given the significance of net fair value information, equation 6.8 is expanded to

include detailed information about net fair value as required by paragraph 5.6 AASB

1033. The total fair value of financial instruments (TFFI) and the off-balance sheet

derivative financial instruments (OBDI) are included as required by paragraph 5.6

AASB 1033. OBDI is a continuous variable. TFFI is measured as the sum of the net

fair value of financial instruments less the net fair value of financial liabilities. The

aggregation of assets and liabilities as TBFI and TFFI assumes that the coefficients on

assets and liabilities are identical. This will reduce the power of the tests

(Venkatachalam, 1996). However, given the small data size and the number of

explanatory variables in the equation, it is prudent to use the TBFI and TFFI instead

of further separating them.


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Equation 6.9 is used to estimate the importance and the explanatory power of net fair

value information. A significant value for coefficients α4, α5, α7 and α8 will indicate

the value relevance of net fair value information in this model. A positive coefficient

being significantly different from zero would provide evidence of the incremental

explanatory power of AASB 1033 net fair value conditional on other included

explanatory variables63 (Barth, 1994; Venkatachalam, 1996; Simko, 1999).

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + α4TFFIit + α5OBDIit + α6CINFV,it + εit (6.9) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments TFFI = net fair value of financial instruments OBDI = off-balance sheet derivative financial instruments CINFV = component score of net fair value t = time i = firm Multicollinearity could be a problem when estimating equation 6.9 since TBFI and

TFFI64 are correlated (Barth, 1994). Therefore, TBFI is dropped from the equation so

that the explanatory power of TFFI without such effects can be estimated65. The

following model is used to estimate the explanatory power of net fair value and off-

balance sheet derivative financial instruments incremental to the book value of non-

financial instruments and earnings:

63 An alternative to this approach is discussed in subsection 6.2.3. 64 TFFI equals to TBFI plus the unrealised gain or loss (URGL). According to Barth (1994) equation 6.9 is econometrically equivalent to Pit = α0 + α1BVNFIit+ α2Eit + γ3TBFIit + α4URGLit + α5OBDIit + α6CINFV,it + α7FVTFFIit + α8FVOBDI it + εit , where γ3 = α3 + α4. In the following subsection, the estimation based on this equation is employed. This procedure may overcome the multicollinearity between TFFI and TBFI. 65 Table 8.4 Chapter 8 reports that the pairwise correlation between TBFI and TFFI is 0.9985. This indicates that the variables are highly correlated.


123

Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it + εit (6.10) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TFFI = net fair value of financial instruments OBDI = off-balance sheet derivative financial instruments CINFV = component score of net fair value t = time i = firm iii) Unrealised Gain or Loss on Financial Instruments

As discussed in the previous section, TBFI and TFFI might be correlated since TFFI

is equal to TBFI plus the unrealised gain or loss (URGL). Therefore, an alternative

model is developed in this section which focused on the URGL on financial

instruments, which is a continuous variable. To obtained this information, the note on

financial instruments is examined. The following note, which taken from the 2001

annual report of Centennial Coal Company Limited is a good example.

47 Financial Instruments

g) Hedges of Anticipated Future Transaction The consolidated entity has entered into contracts to supply coal to customers denominated in US Dollars. The consolidated entity has entered into forward foreign exchange contracts and foreign exchange option contracts to hedge the exchange rate risk arising from these anticipated future transactions. As at the reporting date the aggregate amount of the unrealised losses under forward foreign exchange contracts and foreign exchange option contracts relating to anticipated future transactions is $6,537,000. Such unrealised losses will be realised during the 2002 financial year when the anticipated future transactions take place.


124

Fair value accounting is not mandatory for Australian firms. However, the accounting

standard setting body recognises the relevance of fair values to financial statement

users. The first steps have been taken by the AASB to require firms to disclose the net

fair value of financial instruments. Paragraph 5.6 AASB 1033 requires firms to disclose

the aggregate amount of the net fair value, the method(s) adopted in determining the net

fair value and the assumptions made in determining the net fair value. As long as the

financial assets or financial liabilities, including the underlying assets or liabilities, have

not been disposed of or settled, the difference between net fair value and the carrying

value need not be recognised in the balance sheet. Surprisingly there were some firms

that recognised the unrealised gain or loss as an asset or liability. Central Norseman

Gold Company and Austral Coal Limited defer hedging gains and losses as assets and

liabilities. This action may relate to the characteristics of the firms, such as the size of

the firms.

To investigate the value relevance and the explanatory power of unrealised gain or

loss, the unrealised gain or loss of financial instruments (URGL), total book value of

financial instruments (TBFI), book value of non-financial instruments (BVNFI) and

earnings (E) are included in the model. Following Simko (1999), the URGL is

separated into broad class of financial instruments: URGL of financial assets

(DIFFA), URGL of financial liabilities (DIFFL) and off-balance sheet derivative

financial instruments (OBDI). This is specified in equation 6.11. A significant value

of α4, α5 and α6 indicate the value relevance of the unrealised gain or loss and a

coefficient significantly different from zero would provide evidence of incremental


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explanatory power of URGL conditional on other included explanatory variables

(Barth, 1994; Venkatachalam, 1996; Simko, 1999).

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit + α6OBDIit

+ α7CINFV,it + εit (6.11) Analogous to equation 6.10, TBFI is excluded from the model to examine the

explanatory power of the unrealised gain or loss on financial instruments beyond the

book value of non-financial instruments and earnings66. Any positive significance of

α3, α4 and α5 will indicate the explanatory power of URGL beyond other variables.

This is specified in equation 6.12.

Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit

+ α6CINFV,it + εit (6.12) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments OBDI = off-balance sheet derivative financial instruments DIFFA = difference between net fair value of financial assets and book

value of financial assets. DIFFL = difference between net fair value of financial liabilities and book

value of financial liabilities. CINFV = component score of net fair value t = time i = firm

66 Table 8.5 Chapter 8 reports that TBFI is highly correlated with BVNFI.


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6.2.3 Incremental Explanatory Power of the Net Fair Value and the Unrealised Gain or Loss on Financial Instruments Beyond the Book Value of Financial and Non-Financial Instruments and Earnings Valued at Historical Cost

An alternative approach to the above discussion on the incremental explanatory power

of variables, the adjusted R squared (R2) of equations 6.9 and 6.11 are compared with

the adjusted R2 of equations without the net fair value and the unrealised gain or loss

on financial instruments and the component score net fair value information. This

approach has been used in Collins, Maydew and Weiss (1997), Graham and King

(2000) and Li-Chin, Chao-Shin and Pyung-Sik (2001). The procedure permits

assessing whether the net fair value and the unrealised gain or loss of financial assets,

financial liabilities, or off-balance sheet derivative financial instruments are value

relevant and provide explanatory power in explaining firm share price beyond the

book value of financial and non-financial instruments and earnings valued at historical

cost.

6.2.3.1 Incremental Explanatory Power of Net Fair Value To examine the explanatory power of the net fair value, beyond the book value and

earnings, the adjusted R2 of the equation which includes all variables as well as the

net fair value information is compared with the equation 6.14 which excludes the net

fair value; i.e. the remaining variables are book value variables. The equation that

examines the value relevance of the net fair value information is specified in equation

6.9.

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + α4TFFIit + α5OBDIit + α6CINFV,it + εit (6.9)


127

Examining the relative and the incremental explanatory power of net fair value

beyond the book value of financial and non-financial instruments and earnings valued

at historical cost requires two additional equations. Equation 6.13 expresses price as a

function of net fair value alone, and equation 6.14 expresses price as a function of

book value and earnings.

Pit = α0 + α1TFFIit + α2OBDIit + α3CINFV,it + ε it (6.13)

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + εit (6.14) Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments OBDI = off-balance sheet derivative financial instruments TFFI = net fair value of financial instruments CINFV = component score of net fair value t = time i = firm Following Graham and King (2000), the incremental explanatory power is defined as

the difference between the adjusted R2 of equation 6.9 over equations 6.13 and 6.14.

The explanatory power is defined as below:


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AdjR2

h,nfv represents the adjusted R2 produced by equations 6.9.

The total explanatory power of book value of financial and non-financial instruments and earnings valued at historical cost and net fair value

AdjR2h represents the adjusted

R2 produced by the equation 6.14.

The explanatory power of book value of financial and non-financial instruments and earnings valued at historical cost

AdjR2nfv represents the

adjusted R2 produced by the equation 6.13.

The explanatory power of net fair value

AdjR2nfv/h = AdjR2

h,nfv less AdjR2

h

The incremental explanatory power of net fair value beyond the book value of financial and non-financial instruments and earnings valued at historical cost

AdjR2 h/nfv = AdjR2h,nfv less

AdjR2nfv

The incremental explanatory power of book value of financial and non-financial instruments and earnings valued at historical cost

6.2.3.2 Incremental Explanatory Power of the Unrealised Gain or Loss on Financial Instruments

Analogous to subsection 6.2.3.1, equations 6.11 and 6.15 are included to examine the

incremental explanatory power of the unrealised gain or loss on financial instruments

beyond the book value of financial and non-financial instruments and earnings valued

at historical cost. The equations are specified below:

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit + α6OBDIit +

α7CINFV,it + εit (6.11)

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + εit (6.14) Pit = α0 + α1DIFFAit + α2DIFFLit + α3OBDIit + α4CINFV,it + εit (6.15)


129




value of financial liabilities. CINFV = component score of net fair value t = time i = firm As above, the incremental explanatory power is defined as below: AdjR2

h,urgl represents the adjusted R2 produced by equations 6.11.

The total explanatory power of book value of financial and non-financial instruments and earnings valued at historical cost and unrealised gain or loss on financial instruments.

AdjR2h represents the adjusted

R2 produced by the equation 6.14.

The explanatory power of book value of financial and non-financial instruments and earnings valued at historical cost

AdjR2urgl represents the

adjusted R2 produced by the equation 6.15.

The explanatory power of unrealised gain or loss on financial instruments

AdjR2urgl/h = AdjR2

h,urgl less AdjR2

h The incremental explanatory power of the unrealised gain or loss on financial instruments beyond the book value of financial and non-financial instruments and earnings valued at historical cost

AdjR2 h/urgl = AdjR2h,urgl less

AdjR2urgl

The incremental explanatory power of the book value of financial and non-financial instruments and earnings valued at historical cost


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6.3 Estimation Procedures

The above models are estimated using Eviews 4.0, PcGive 1.0 and SPSS 11.5

statistical packages. The models are estimated based on pooled data. This study

employs multiple regression analysis to estimate the association between disclosure

quality of derivative information and firm characteristics (firm characteristic model)

and the value relevance of derivative information (market value models). The multiple

regression technique is performed, which focuses on the significance of the

independent variables in explaining disclosure quality and market value.

The ranked regression procedure is also performed on the firm characteristics model

as an alternative approach to the other techniques. This procedure is also performed in

Lang and Lundholm (1993), Wallace et al. (1994), Owusu-Ansah (1998) and Ali et al.

(2003). The rank transformation is a simple procedure where the continuous variables

are replaced with their rank. The smallest observation is ranked as 1 and continued to

rank n for the largest. In the case of ties the average ranks are assigned.

The analyses on market value models is performed on the undeflated variables since

Barth and Kallapur (1996) indicate that deflation has unpredictable effects of

coefficient bias, heteroscedasticity and estimation efficiency. Heteroscedasticity

occurred in all value relevance estimations and therefore, the White (1980)

heteroscedasticity-consistent standard errors in addition to the heteroscedasticity and

autocorrelation consistent (HAC) standard error67 (in case both heteroscedasticity and

67 Also known as Newey-West standard errors.


131

autocorrelation occur) are employed. These are consistent with Barth (1994), Barth et

al. (1996), Eccher et al. (1996) and Nelson (1996).

Nevertheless, prior studies also indicate that heteroscedasticity can be reduced by: a)

dividing all variables with the number of outstanding shares and b) including a scale

proxy, such as the number of outstanding shares, as an independent variable (Barth

and Kallapur, 1996). However, none of these were able to overcome or reduce the

heteroscedasticity. Therefore, the results of the estimation were based on the White’s

corrected regression and the Newey-West standard errors.

In addition to the above, two regression analyses are performed to examine the

incremental explanatory power of net fair value and unrealised gain or loss on

financial instruments beyond the book value of financial and non-financial

instruments and earnings valued at historical cost. These procedures are commonly

used in value relevance studies (for example, Collins, Maydew and Weiss, 1997;

Graham and King, 2000; Li-Chin, Chao-Shin and Pyung-Sik, 2001). The focus of this

analysis is on the adjusted R2 of each equation. However, the approach used by Barth

(1994), Eccher et al. (1996), Venkatachalam (1996) and Simko (1999) in interpreting

the incremental explanatory power of variables are also employed. This approach

examines whether the coefficient of the independent variables are significantly

positive and significantly different from zero.

Regression analysis is also performed on yearly data for all models (firm

characteristics model and market value models) so any potential for the effect of time

can be identified. In addition to that, the procedure reduced autocorrelation that

occurred in the above models. Results for year-by-year and average four year are


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reported in Appendix B. Diagnostic tests are also performed on each model to ensure

valid conclusions are made based on the multiple regression results. These tests are

normality tests, autocorrelation tests, heteroscedasticity tests and multicollinearity

tests. Results on these tests are presented alongside the results in Chapters 7 and 8.

6.4 Descriptive Statistics

6.4.1 Firm Characteristics Model

Table 6.5 reports the descriptive statistics of dependent and independent variables of

equation 6.1. For the dependent variable, the average transparency is 88.71% for the

pooled data. Examining each year reveals that the average transparency increased

from 86.29% in 1998 to 90.23% in 2001. This indicates that the overall level of

derivative disclosures among firms in the extractive industries has increased for each

year and moving towards greater compliance with the AASB 1033 disclosure

requirements. The level of variation among firms across the period of study is

reducing, as indicated by the standard deviation, reducing from 0.1137 in 1998 to

0.0772 in 2001.

Because of the variability in the level of total assets between firms, the size variable is

transformed into its natural log in order to normalise the distribution.68 Table 6.5

shows that there is little variability in the means for size, profitability, leverage (debt-

to-equity ratio) and research and development over the period of study. The means of

the price-earnings ratio and market-to-book ratio are more variable, with positive

means in two years and negative in two years. The proportion of limited liability firms

68 The largest firm is BHP Billiton Ltd. with total assets amounting $37,082million, and the smallest firm is Kalrez Energy Ltd. with total assets amounting $0.97million.


133

increases over the period from 58% to 66% while in all years more than 80% of firms

use a Big five or Big six auditor.


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Table 6.5: Descriptive Statistics and Correlation Matrix: Firm Characteristics Model

Variable 1998 (n=65) 1999 (n=65) 2000 (n=65) 2001 (n=65) Pooled (n=260)

TRANSP 0.8629(0.1137) 0.8905(0.09854) 0.8928(0.08691) 0.9023(0.0772) 0.8871(0.0956)

SIZE 18.3682(2.0473) 18.4466(1.9903) 18.5134(1.9649) 18.5639(1.9698) 18.4726 (1.9830)

PROFIT -0.0616(0.2921) -0.0392(0.2002) -0.0965(0.6666) -0.0325(0.2857) -0.0574 (0.4020)

PE -6.8185(104.9639) 48.0050(259.2588) 6.2190(65.5440) -0.0247(36.8096) 11.8452 (145.5570)

TYPE 0.4154(0.4966) 0.3846(0.4903) 0.3385(0.4769) 0.3385(0.4769) 0.3692 (0.4835)

AUDIT 0.8154(0.3910) 0.8308(0.3779) 0.8615(0.3481) 0.8615(0.3481) 0.8423 (0.3652)

MTB 0.0537(16.2549) -0.1815(19.8379) 4.0848(7.9907) -10.0741(96.6262) -1.5293 (50.1274)

R&D 0.3231(0.4966) 0.3231(0.4713) 0.3231(0.4713) 0.2923(0.4584) 0.3154 (0.4656)

DTE 0.3064(0.5106) 0.3156(0.5481) 0.2819(0.4263) 0.2931(96.6262) 0.2992 (0.4854)

Note: Means (Standard deviations)

Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity


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6.4.2 Market Value Models

6.4.2.1 Value Relevance of Disclosure Quality

Table 6.6 reports descriptive statistics of the dependent and independent variables for

Equations 6.3 and 6.4. The mean score for the log of market value is 17.9448 with a

standard deviation of 1.8908. The average level of transparency and standard

deviation of the 25369 firm-year observations is 0.8867 and 0.0944, respectively,

which is approximately about the same as that of the whole data (260 firm-year)

observations. However, the average score of three components of transparency is

almost the same with the highest mean score of 0.1982 for net fair value and the

lowest of 0.1752 for hedge of anticipated transaction score.

Table 6.6: Descriptive Statistics: Value Relevance of Disclosure Quality (n=253)

Mean Standard Deviation

Median Minimum Maximum

LMV 17.9448 1.8908 17.5642 13.0165 23.0104 BV 2.46E+08 5.26E+08 42211000 3347407 2.91E+09 E 21075002 1.08E+08 1258389 -2.82E+08 9.67E+08 TRANSP 0.8867 0.0944 0.9167 0.5786 1.0000 CINFV 0.1982 0.0447 0.1875 0.1000 0.3333 CIHedge 0.1752 0.0817 0.2000 3.33E-14 0.2500 CIRisk 0.1969 0.0740 0.2000 2.00E-14 0.3333

Variable definitions: LMV = natural log market value of firm’s common equity measured three

months following the financial year t for firm i, BV = book value of equity at year end t for firm i, E = earnings for year t available to firm i’s common shareholders, TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible

disclosure scores CINFV = component score of net fair value, CIHedge = component score of hedge information, CIRisk = component score of risk information.

69 Seven firms were excluded because of the huge differences in book value of equity and earnings between these data and the rest of the data.


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6.4.2.2 Value Relevance of Hedge Transaction, Net Fair Value and the Unrealised Gain or Loss on Financial Instruments

Table 6.7 reports descriptive statistics on the dependent and independent variables of

equation 6.5 to equation 6.8. The average market value of the models is 17.9448 with

the standard deviation of 1.8908. On average, firms in the extractive industries

possess more financial liabilities than financial assets where the total book value of

financial instruments is equal to – $136,000,000. On average, the book value of

financial assets is $1,783,592 more than the net fair value of financial assets (DIFFA)

This reflect the fact that extractive firms incurred unrealised losses during the period.

Nevertheless, financial liabilities exhibit an unrealised gain (DIFFL) by $2,045,112.

The average value of off-balance sheet derivative financial instruments (OBDI) is –

$7,756,216 indicating that firms hold more derivatives classified as liabilities than as

assets.

6.5 Summary This chapter has discussed the research design and data collection procedures. Data

from 137 listed firms in the extractive industries were gathered from 1998 to 2001

annual reports. Models are developed to examine the association between the

disclosure quality of derivative information and firm characteristics (firm

characteristics model) and the association between market value and derivative

information (market value models). Descriptive statistics are presented as well as

details on firm attributes. Chapter 7 and chapter 8 present the results of the firm

characteristics model and market value models respectively.


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Table 6.7: Descriptive Statistics: Value Relevance of Hedge Transaction, Net Fair Value and Unrealised Gain or Loss on Financial Instruments. (n=253)

Mean Standard

Deviation Median Minimum Maximum

LMV 17.9448 1.8908 17.5642 13.0165 23.0104 BV 2.46E+08 5.26E+08 42211000 3347407 2.91E+09 BVNFI 3.82E+08 9.09E+08 49358000 -25295000 5.37E+09 E 21075002 1.08E+08 1258389 -2.82E+08 9.67E+08 URGL -7626599 1.17E+08 0.0000 -9.14E+08 7.15E+08 CINFV 0.1982 0.0447 0.1982 0.1000 0.3333 CIHedge 0.1752 0.0817 0.2000 3.33E-14 0.2500 OBDI -7756216 1.13E+08 0.0000 -9.11E+08 6.69E+08 TBFI -1.36E+08 4.14E+08 -2627925 -2.56E+09 3.08E+08 TFFI -1.36E+08 4.16E+08 -2627925 -2.56E+09 3.08E+08 DIFFA -1783592 18739352 0.0000 -2.12E+08 -1.53E+08 DIFFL -2045112 17539292 0.0000 -1.53E+08 1.07E+08


months following the financial year t for firm i, BV = book value of equity at year end t for firm i, E = earnings for year t available to firm i’s common shareholders, BVNFI = book value of non financial instruments CINFV = component score of net fair value, CIHedge = component score of hedge information, TBFI = total book value of financial instruments TFFI = net fair value of financial instruments OBDI = off-balance sheet derivative financial instruments URGL = unrealised gain or loss of financial asset and financial liability DIFFA = difference between net fair value of financial assets and book


value of financial liabilities. t = time i = firm

Chapter 7: Results: Disclosure Quality and Firm Characteristics

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CHAPTER 7 RESULTS: DISCLOSURE QUALITY AND FIRM CHARACTERISTICS

Chapter five developed research questions and chapter six presented research models

that relate disclosure quality with firm characteristics. In this chapter, the results of the

multiple regression analyses that relate disclosure quality and firm characteristics are

presented. Overall, the results support prior studies that size of the firm, price-

earnings ratio and debt-to-equity ratio do play an important role in determining the

quality of information disclosed by the firms. However, in certain cases market-to-

book ratio and profitability of the firms also may lead firms to provide more

transparent information.

Section 7.1 explains the diagnostic tests performed. Section 7.2 presents the

descriptive results. Section 7.3 discusses validity of the disclosure index. Multiple

regression results are discussed in section 7.4. Section 7.5 discusses and analyses the

findings and section 7.6 summarises and concludes the chapter.

7.1 Diagnostic Tests

Several diagnostic tests, such as normality tests, autocorrelation tests,

heteroscedasticity tests and multicollinearity tests, are performed to ensure valid

conclusions are drawn based on the multiple regression results. If the tests are not

satisfied then corrective procedures are performed.


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7.1.1 Normality Test The normality test using Jarque-Bera statistics and graphical analysis are performed

on the residuals of all the models. The Jarque-Bera statistics measure the difference of

the skewness and kurtosis of the series as opposed to those from a normal distribution.

The reported probability is the probability that the Jarque-Bera statistics exceeds the

observed value under the null hypothesis of the normal distribution. A probability

with a small value indicates the rejection of the null hypothesis. Skewness is a

measure of asymmetry of the distribution of the series around its mean. If the series is

normally distributed the skewness is zero. A positive skewness means the distribution

has a long right tail and a negative skewness indicates that the distribution has a long

left tail. Kurtosis measures the flatness or peakedness of the distribution of the series.

The kurtosis for a series with a normal distribution is 3. If the kurtosis exceeds (lower)

than 3, it indicates that the distribution is peaked (flat) relative to the normal. The

skewness and kurtosis presented by Eviews 4.0 is –0.6450 and 3.4169 respectively.

The Jarque-Bera statistics is significant at p < 0.01.

7.1.2 Autocorrelation Test Classical linear regression assumes that the disturbances (errors) are not correlated

with each other and have the same variance (Kennedy, 1998, p. 43). Disturbances are

autocorrelated when the covariances and correlations between different disturbances

are not zero. The Durbin-Watson statistic (d) is used to test for autocorrelation. As a

guideline, the Durbin-Watson statistics has provided a lower limit dL and an upper

limit dU for a decision regarding the presence of positive and negative correlation

(Gujarati, 1999). If a d value falls in between dU (the highest value of d which is


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below 2) and 4-dU, the null hypothesis of no positive or negative autocorrelation can

be accepted70. Kennedy (1998) indicates that the closer d is to 2.0, the more

confidence there can be of no autocorrelation in the disturbances. The Durbin-Watson

statistic (d) on the main model indicates that autocorrelation is not a concern since d is

very close to 2 (d = 1.9801). As the Durbin-Watson statistic has a few disadvantages

(Thomas, 1997, p. 304) the Breusch-Godfrey serial correlation LM test is also

performed. The test indicates that there is no serial correlation in the residuals.

7.1.3 Heteroscedasticity Test Classical linear regression model (CLRM) assumes that the disturbances are

spherical; where they have the same variance. However, if this is not the case the

disturbances are said to exhibit heteroscedasticity, or unequal variances (Kennedy,

1998). Hence, this can lead to misleading conclusions (Gujarati, 1999, p. 349). Two

tests are performed to test for unequal variances. These are the residual plots produced

by the SPSS 11.5 and the White heteroscedasticity test (White, 1980) reported by

Eviews 4.0 statistical packages. Both tests indicate that there is unequal variance in

the model. The White’s test indicates that the null hypothesis that the errors exhibit

homoskedasticity is rejected at p < 0.05. Therefore, the White’s Heteroscedasticity

Corrected Regression71 is employed.

70 Refer to Gujarati (1999, p. 389) for a detailed discussion. 71 Throughout this thesis the White’s heteroscedasticity corrected regression is used interchangeably with the White’s heteroscedasticity corrected standard errors.


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7.1.4 Multicollinearity Test Multicollinearity occurs when there is a linear relationship between independent

variables. Kennedy (1998, p 187) suggests the use of a correlation matrix and

condition index to detect multicollinearity. Table 7.1 presents a correlation matrix

between the independent variables. Kennedy suggests that a high value (about 0.8 or

0.9) for correlation coefficients indicates high correlation between the variables. Table

7.1 indicates that while the size variable is correlated with a number of the other

variables, only two coefficients exceed 0.6. This suggests that multicollinearity is

unlikely to be a problem. To confirm this finding the variance inflation factors (VIF)

are examined using SPSS 11.5. The highest VIF is 3.275 for size followed by 1.746

for type of the firm. VIF being less than 10 confirms that there is no need to be

concerned about the correlation between the independent variables. In addition to the

above tests, the matrix plots are also examined. The matrix plots produced by Eviews

4.0 indicate that multicollinearity is not a major concern.


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Table 7.1: Correlation Coefficients between Variables

TRANSP SIZE PROFIT PE TYPE AUDIT MTB R&D DTE TRANSP 1.0000 SIZE 0.4696*** 1.0000 PROFIT 0.2178*** 0.2820*** 1.0000 PE 0.1309** 0.1398** 0.0280 1.0000 TYPE -0.2718*** -0.6193*** -0.2022*** -0.0712 1.0000 AUDIT 0.1421** 0.3810*** 0.2094*** 0.1073* -0.3906*** 1.0000 MTB -0.0415 -0.0062 -0.0116 -0.0027 0.0609 -0.0487 1.0000 R&D 0.2999*** 0.6307*** 0.1158* 0.1037* -0.3306*** 0.2937*** 0.0688 1.0000 DTE 0.3315*** 0.5275*** 0.0619 0.1570** -0.2505*** 0.1043 0.0017 0.2854*** 1.0000

***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10 respectively. Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity


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7.2 Descriptive Results

7.2.1 Firms’ Disclosure Scores

Table 7.2 reports the numbers of firms in the extractive industries that disclose all

information required by the accounting standard, AASB 1033. Column 3 panel A

reports that the number of firms reporting all required information in the Top 500

decreased from 11 firms in 1998 to 8 firms in 2001. Panel B indicates that in 1998 no

non-Top 500 firms using derivatives provided high quality derivative information.

However, the number of non-Top 500 firms with high quality disclosures increased to

one firm in 1999 and 2000, and two firms in 2001.

Table 7.2: Number of Firms that Report all Information Required by AASB 1033 (100% disclosure)

Year Full Sample User sample*

Panel A: Top 500 Companies Listed in BRW/Connect 4 (n=56) (n=44) 1998 12 11 1999 15 14 2000 10 9 2001 9 8

Panel B: Non-Top 500 Companies (n=81) (n=21) 1998 1 0 1999 7 1 2000 7 1 2001 5 2

Total 137 65 * This refers to the firms that use derivatives for hedging purposes.


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Table 7.3 reports the number of Australian firms in the extractives industries

classified according to the quality of their derivative disclosures. The number of firms

in the user-sample providing high quality information is indicated in column 7, Panel

A. In 1998 there are 11 firms disclosing 100% of information about financial

instruments. The number increases to 15 in 1999, but decreased to 10 in 2000. The

majority of firms providing high quality derivative information are limited liability

firms. However, the number of limited liability firms producing high quality

information decreased from 10 in 1998 to 8 in 2001. However, there was an increase

in the number of firms providing 90% to 99% of this information. The number

increased from 20 (1998) to 31 (2001) for the full sample. The majority of these firms

are limited liability firms (Column 6, Panel B).

Table 7.3: Disclosure Quality of Firms in the Australian Extractive Industries

Year < 30% 30%-49% 50%-69% 70%-89% 90%-99% 100% Panel A: User sample (n=65) 1998 0 0 7 27 20 11 1999 0 0 4 25 21 15 2000 0 0 3 25 27 10 2001 0 0 1 23 31 10 Panel B: Limited liability firms in user sample 1998 (n=38) 0 0 1 13 14 10 1999 (n=40) 0 0 1 15 13 11 2000 (n=43) 0 0 2 14 19 8 2001 (n=43) 0 0 0 13 22 8 Panel C: No-liability firms in user sample 1998 (n=27) 0 0 6 14 6 1 1999 (n=25) 0 0 3 10 8 4 2000 (n=22) 0 0 1 11 8 2 2001 (n=22) 0 0 1 10 9 2


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7.2.2 Disclosure Components

As discussed in the previous section each component of the disclosure index plays an

important role in determining the quality of derivative disclosures. Table 7.4 reports

descriptive statistics for each disclosure component for the pooled sample (65 times 4

years). Panel A reports the statistics for the user sample. The mean for each disclosure

component (Panel A) indicates that user firms disclose almost all information with

regard to policy information (99.62%). However, they do withhold some information

in relation to hedges of anticipated transactions (76.72%), risk information (81.09%),

net fair value information (81.30%) and commodity contracts information (36.54%).

Information about commodity contracts is relevant to only a limited number of

extractive firms using commodity instruments, such as gold forward contracts, to

hedge their risk.

Table 7.4: Descriptive Statistics of Disclosure Components (Pooled Sample)

Mean Standard Deviation

Median Minimum Maximum

Panel A: User Sample (n=260) Policy Information 0.9962 0.0620 1.0000 0.0000 1.0000 Hedges of Anticipated Transactions 0.7672 0.3490 1.0000 0.0000 1.0000 Risk Information 0.8109 0.2840 1.0000 0.0000 1.0000 Net Fair Value Information 0.8130 0.1404 0.7500 0.5000 1.0000 Commodity Contracts Information 0.3654 0.4825 0.0000 0.0000 1.0000 Panel B: Limited liability firms in User Sample (n=168) Policy Information 1.0000 0.0000 1.0000 1.0000 1.0000 Hedges of Anticipated Transactions 0.8508 0.2539 1.0000 0.0000 1.0000 Risk Information 0.8571 0.2542 1.0000 0.0000 1.0000 Net Fair Value Information 0.8187 0.1388 0.7500 0.5000 1.0000 Commodity Contracts Information 0.4405 0.4979 0.0000 0.0000 1.0000 Panel C: No-liability firms in User Sample (n=92) Policy Information 0.9891 0.1043 1.0000 0.0000 1.0000 Hedges of Anticipated Transactions 0.6145 0.4380 0.8000 0.0000 1.0000 Risk Information 0.7264 0.3160 0.6667 0.0000 1.0000 Net Fair Value Information 0.8028 0.1435 0.7500 0.5000 1.0000 Commodity Contracts Information 0.2283 0.4220 0.0000 0.0000 1.0000


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A comparison of Panel B and Panel C indicates that no-liability firms make fewer

disclosures than limited liability firms especially of information about hedges of

anticipated transactions and risk information. Panel C indicates that the mean for

hedges of anticipated transactions is 0.6145 compared to 0.8508 for limited liability

firms (Panel B). The mean for risk information for no-liability firms is 0.7264 as

compared to 0.8571 for limited liability firms.

Further investigation of each component reveals that some firms failed to disclose

detailed information about the expected timing of recognition of any deferred or

unrecognised gain or loss as a revenue or expense, the aggregate net fair value and

carrying amount and the net fair value of either the individual asset or appropriate

grouping of those individual assets. It is also recorded that some firms did not disclose

their reasons for not reducing the carrying amount to net fair value. As a consequence

they did not provide any information about their evidence for management’s belief

that the carrying amount will be recovered.

Table 7.5 reports the trend in disclosing derivative information among user firms over

the period of the study. Panel A indicates that policy information as required by

paragraph 5.2 (a), (b) and paragraph 5.3 AASB 1033 is fully disclosed in all years

except 1998. Further, there is a steady increase over the four year period in the

disclosure transparency of hedges of anticipated transactions and risk information.

Panel B reports that the mean of the disclosure score for hedges of anticipated

transactions and risk information for limited liability firms increased from 85.31%

(1998) to 89.92% (2001) and 84.21% (1998) to 87.60% (2001) respectively. A similar

trend was documented for no-liability firms.


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Table 7.5: Mean Disclosure Components of User Firms for the Period 1998 to 2001

1998 1999 2000 2001 Panel A: User Sample (n=65)

Policy Information 0.9846 1.0000 1.0000 1.0000 Hedges of Anticipated Transactions 0.6851 0.7272 0.7979 0.8585 Risk Information 0.7513 0.8077 0.8385 0.8462 Net Fair Value Information 0.8198 0.8152 0.8051 0.8121 Commodity Contracts Information 0.3692 0.3692 0.3538 0.3692 Panel B: Limited liability firms

(n=38)

(n=40)

(n=43)

(n=43)

Policy Information 1.0000 1.0000 1.0000 1.0000 Hedges of Anticipated Transactions 0.8531 0.8783 0.8512 0.8992 Risk Information 0.8421 0.8667 0.8605 0.8760 Net Fair Value Information 0.8515 0.8255 0.8109 0.8090 Commodity Contracts Information 0.5000 0.4750 0.4186 0.4186 Panel C: No-liability firms

(n=27)

(n=25)

(n=22)

(n=22)

Policy Information 0.9630 1.0000 1.0000 1.0000 Hedges of Anticipated Transactions 0.4444 0.4853 0.6939 0.7788 Risk Information 0.6235 0.7133 0.7955 0.7879 Net Fair Value Information 0.7751 0.7986 0.7938 0.8182 Commodity Contracts Information 0.1852 0.2000 0.2273 0.2727

Panel C reports the mean disclosure components for no-liability firms. No-liability

firms tend to increase their disclosure for hedges of anticipated transactions and for

risk information. The mean scores for hedges of anticipated transactions increased

from 44.44% (1998) to 77.88% (2001). The mean scores for risk information

increased from 62.35% (1998) to 79.55% (2000), but slightly decreased to 78.79%

(2001). The results indicate that both types of firms realise the importance of both

components for decision-making. Of concern, is the evidence shown in Panel B of the

decrease of the mean score for net fair value information for limited liability firms


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from 85.15% (1998) to 80.90% (2001). However, there is no consistent pattern in the

trend for no-liability firms.

7.3 Validity of the Disclosure Quality Score (Disclosure Index) Research on disclosure quality based on a disclosure index has been criticised because

of validity and reliability issues. This is because the usefulness of the index depends

on the items included in the index (Marston and Shrives, 1991) and the fact that the

selection of the items is very subjective (Botosan, 1997). Marston and Shrives (1991)

considered an index to be valid if the index has any meaning as a measure of

information disclosure. They report that most researchers adapt and change the

existing indices to meet their own objectives and therefore the index is valid in the

particular research environment of interest only.

Botosan (1997) had used several procedures to examine reliability and validity of her

disclosure index. She compared the correlation between the disclosure index and firm

characteristics (i.e. firm size, exchange status, audit firm size and leverage) identified

to be associated with the firms disclosure level in prior research. If her index

successfully measures disclosure levels it should be correlated with these

characteristics. She also examines: a) the correlation between her disclosure index and

the annual report of disclosure scores assigned by the Association of Investment and

Management Research (AIMR) and b) the correlation between components of the

disclosure index, the number of Wall Street Journal (WSJ) articles written about the

firm during 1990 and the number of financial analysts following the firm during the

year. In addition to the above, she also calculated the Cronbach’s coefficient alpha to


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assess the degree to which the correlation among the categories of the disclosure

index is attenuated due to random error.

Botosan concluded that the validity of her disclosure index was supported as there is a

correlation between: a) the disclosure index and size, leverage and exchange listing

status, b) the disclosure index and the score assigned by the AIMR, c) the components

of the disclosure index, number of analysts following the firm and the number of WSJ

articles. Additionally, the Cronbach’s coefficient alpha is satisfied.

To validate the disclosure quality, this thesis examines: a) the correlation between

each component, b) the correlation between disclosure quality and firm characteristics

and c) the disclosure score of firms in the extractive industries which received either

gold, silver or bronze awards from the Australasian Reporting Awards Inc. (ARA).

The ARA is an independent not-for-profit organisation comprised of the professional

bodies and individuals with the objectives:

i) to promote excellence in reporting through the publication of informative and factual reports,

ii) to encourage effective communication of financial and

business information,

iii) to create public awareness of valid and objective measures of performance and to promote a better understanding of the results achieved, and

iv) to create public awareness of the purposes of enterprises,

how they function and their achievements.


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The ARA published criteria reflect the general reporting principles of the Global

Reporting Initiatives (GRI)72. The ARA general criteria apply to all reporting entities.

The criteria components are: a) overview/objectives/highlight, b) review of operations

or activities and c) details and analyses of performance and financial affairs, which

include financial statements and related notes and statistical summaries (ARA, 2003).

Table 7.6 presents the correlation coefficients among the variables. Panel A presents

the correlations between disclosure components index. Panel A Table 7.6 indicates

that hedges of anticipated transactions are positively and significantly correlated with

the other variables at p < 0.01 and p < 0.05, while the risk component is positively and

significantly related with the net fair value component at p < 0.01. Panel B presents

the correlation coefficients between firm characteristics and disclosure quality. Panel

B indicates that size, audit and leverage (DTE) are positively correlated with

disclosure quality at p < 0.01, p < 0.05 and p < 0.01 respectively. This is consistent

with the findings of Botosan (1997) and Ahmed and Courtis (1999).

72 Nine elements for best practice reporting are outlined by the GRI are a CEO statement, concise presentation of key indicators, profile of the entity, policies, organisation and management systems, stakeholder relationships, management performance, operational performance, product performance and sustainability overview.


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Table 7.6: Correlation Coefficients between Variables

Panel A: Correlation Coefficients between Disclosure Components Index TRANSP POLICY HEDGE RISK NFV COMMODITY

TRANSP 1.000 POLICY 0.143** 1.000 HEDGE 0.467*** 0.137** 1.000 RISK 0.819*** -0.041 0.199*** 1.000 NFV 0.542*** -0.083 0.235*** 0.276*** 1.000 COMMODITY 0.252*** 0.047 0.350*** 0.084 0.062 1.000 Panel B: Correlation Coefficients between Variables

TRANSP SIZE AUDIT DTE TRANSP 1.0000 SIZE 0.4696*** 1.0000 AUDIT 0.1421** 0.3810*** 1.0000 DTE 0.3315*** 0.5275*** 0.1043 1.0000

***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10 respectively.

Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible

disclosure scores POLICY = policy information component HEDGE = hedge of anticipated transaction component RISK = risk information component NFV = net fair value component COMMODITY = commodity component SIZE = log of total assets AUDIT = 1 for Big-5/6 auditor, 0 otherwise DTE = total liabilities divided by book value of common equity

In 2002 nine firms in the extractive industries were awarded by the ARA for their

high quality 2001 annual reports (Annual Reports Partner, 2002). Three were awarded

with the gold award, one with silver and five were awarded with bronze award. Table

7.7 presents the disclosure quality score for these companies for 1998 to 2001. As

there is no record available for previous years, the comparison is based on the

recipients for 2002. The results indicate that the disclosure score of these companies

are consistent with the award given, where these companies provided quality


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disclosure derivative information with the range between 0.905 and 1.000. The lowest

disclosure index was awarded for BHP Billiton in 1998.

Table 7.7: Disclosure Quality of Derivative Information of Extractive Firms

Company ARA Award Disclosure Score

1998 1999 2000 2001 Aurora Energy Pty Ltd Gold Award 1.000 1.000 1.000 1.000

MIM Holdings Gold Award 1.000 1.000 0.950 0.950

Rio Tinto Australia Gold Award n.a. n.a. n.a. n.a. Goldfields Ltd Silver Award 1.000 1.000 1.000 1.000

BHP Billiton Limited/Plc Bronze Award 0.905 0.943 0.971 0.971

Boral Limited Bronze Award n.a. n.a. n.a. n.a. Normandy Mining Ltd Bronze Award 0.943 0.943 0.943 0.943

Santos Limited Bronze Award 0.964 0.964 0.938 0.938

Woodside Petroleum Ltd Bronze Award 1.000 1.000 0.964 0.964

Table 7.7 indicates that the score for three firms decreased from previous years. With

this trend are MIM Holdings, Santos Ltd. and Woodside Petroleum Ltd. The

disclosure score for MIM Holdings and Woodside Petroleum decreased from 1.000

for both firms in 1999 to 0.950 and 0.964, respectively in 2001. Santos Limited

disclosure score decreased from 0.964 to 0.938. There is no disclosure score available

for two firms, Rio Tinto Australia and Boral Limited, as they were excluded from the

data sample.


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7.4 Multiple Regression Results

7.4.1 Standard Regression Procedures

Table 7.8 presents the results of the regression analysis of the association between

disclosure transparency and firm characteristics73. As predicted, firm size is

significantly positively related to disclosure quality (p < 0.001). This indicates that

large firms tend to provide more transparent information as compared to small firms.

This is consistent with the work undertaken by Singhvi and Desai (1971), Firth

(1979), Wallace et al. (1994), Wallace and Naser (1995), Riahi-Belkaoui (2001), Ali

et al. (2003) and Cooke (1989, 1991). Also significant are two of the control

variables; debt-to-equity ratio (p = 0.0212) and market-to-book ratio (p = 0.0021)74.

However, market-to-book ratio is negatively related to the disclosure quality of

derivative information.

The coefficients estimated for profitability and price-earnings ratio are positively and

significant at p < 0.05. However, the coefficient estimates for research and

development, firm type and auditor are not significant. While the measure of auditor

is not only insignificant but negatively associated with transparency, this may be due

to lack of variability in this variable. The insignificance of auditor is consistent with

Ali et al. (2003), Firth (1979), Malone at al. (1993) and Wallace et al. (1994).

73 Since heteroscedasticity is present, the results presented are based on the White’s heteroscedasticity-corrected standard errors. 74 The results are consistent for the estimation without the outliers. Results are presented in Table E 1, Appendix E.


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Table 7.8 :Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics (n=260)

TRANSPit=α0+α1SIZEit+α2PROFITit+α3PEit+α4TYPEit+α5AUDITit+α6MTBit

+α7R&Dit +α8DTEit+ εit

Variable Predicted Sign Coefficient Std. Error t-Statistics Prob. Constant ? 0.5602 0.0773 7.2431 0.0000*** SIZE + 0.0178 0.0043 4.1864 0.0000*** PROFIT + 0.0267 0.0129 2.0742 0.0391** PE + 3.93E-05 1.91E-05 2.0587 0.0406** TYPE - 0.0016 0.0146 0.1080 0.9141 Audit +/- -0.0131 0.0188 -0.6956 0.4873 MTB + -8.25E-05 2.65E-05 -3.1070 0.0021*** R&D + 0.0071 0.0136 0.5194 0.6039 DTE +/- 0.0232 0.0100 2.3196 0.0212**

Adjusted R2 = 0.2237 Durbin-Watson Statistics = 1.9801 F statistics = 10.3266 p-value = 0.0000 *** and ** indicate significance at p < 0.01 and p < 0.05 respectively. Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity i = firm t = year


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7.4.2 Sensitivity Analyses

7.4.2.1 Ranked Regression

In addition to the above standard regression, the ranked regression procedure, as in

Lang and Lundholm (1993), Wallace, Naser and Mora (1994), Wallace and Naser

(1995), Owusu-Ansah (1998) and Ali, Ahmed and Henry (2003), is performed. The

procedure is an alternative approach to other robust techniques and a powerful method

for data with monotonic and non-linear relations (Iman and Conover, 1979; Lang and

Lundholm, 1993; Wallace et al., 1994). The approach has also been suggested by

Bollen and Jackman (1990) to mitigate the effect of influential observations. Kane and

Meade (1997) indicate that this technique is considered robust to mitigate problems

associated with skewed distributions and negative values.

The rank transformation is a simple procedure where the continuous variables are

replaced with their rank. It also treats all observations (i.e. influential or not) equally

(Owusu-Ansah, 1998). The smallest observation is ranked as 1 and continued to rank

n for the largest. In the case of ties the average ranks are assigned.

Table 7.9 shows that leverage, size and price-earnings ratios are positively related to

transparency and are highly significant at p < 0.00175. Results that differ to those

presented earlier in Table 7.8 relate to market-to-book ratio and profitability, which

are no longer significant. However the explanatory power of this model increases

from 22.37% to 32.98%. This is contrary to the findings of Owusu-Ansah (1998) and 75 The results are based on the White’s Heteroscedasticity-Consistent Standard Errors, and are consistent with the results of estimation with the outliers excluded. Results with the outliers excluded are presented in Table E 3 Appendix E. Six observations were eliminated because of a large discrepancy between these observations and the rest of the sample.


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Wallace et al. (1994), where both studies report the weaker explanatory power for the

rank regression procedure.

Table 7.9: Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics: Ranked Transformation (n=260)

RTRANSPit=α0+α1RSIZEit+α2RPROFITit+α3RPEit+α4TYPEit+α5AUDITit

+α6RMTBit+α7R&Dit +α8RDTEit+ εit

Variable Predicted Sign Coefficient Std. Error t-Statistics Prob

Constant ? 28.3921 19.7543 1.4373 0.1519 RSIZE + 0.3394 0.1007 3.3720 0.0009*** RPROFIT + 0.0457 0.0672 0.6805 0.4968 RPE + 0.2168 0.0625 3.4682 0.0006*** TYPE - 16.2860 11.5022 1.4159 0.1580 Audit +/- 12.4352 12.5933 -0.9874 0.3244 RMTB + -0.0584 0.0480 -1.2170 0.2248 R&D + -9.1751 10.5397 -0.8705 0.3848 RDTE +/- 0.2953 0.0727 4.0610 0.0001***

Adjusted R2 = 0.3298 Durbin-Watson Statistics = 2.0311 F statistics = 16.9348 p-value = 0.0000 *** indicates significance at p < 0.01.

Variable Definitions: RTRANSP = rank of disclosure (transparency) RSIZE = rank of total assets (in thousands) RPROFIT = rank of profitability RPE = rank of price/earnings ratio TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise RMTB = rank of market-to-book ratio R&D = 1 for R&D firm, 0 otherwise. RDTE = rank of total liabilities divided by book value of common equity i = firm t = year


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7.4.2.2 Profit Vs Loss Making Firms

Disclosure quality might be influenced by the profitability of the firm. To measure

whether firms behave differently, a dichotomous variable of one for firms that report a

profit and zero otherwise (DINCOME) is included in equation 6.1. Table 7.10 presents

the results for the pooled estimation of equation 6.1. The results indicate that size and

market-to-book ratio are significantly associated with transparency at p < 0.01. Also

significant are leverage (p < 0.05), price-earnings ratio (p < 0.05) and DINCOME (p <

0.10).

Further, estimation of profit-making and loss-making firms separately may provide

vital information. Table 7.11 reports the results for the separate estimation for profit-

making and loss-making firms. Panel A (B) presents76 the results for the firms with

profits (losses). Panel A indicates that size is positive and significantly related with

transparency at p < 0.001. Also significant at p < 0.10 are profitability and leverage.

Panel B indicates that market-to-book ratio and research and development are

significant at p < 0.10.

76 As both models were subject to heteroscedasticity, the estimations were based on White’s Heteroscedasticity Corrected Regression.


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Table 7.10: Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics. (n=260)

TRANSPit=α0+α1SIZEit+α2PROFITit+α3PEit+α4MTBit+α5R&Dit+α6TYPEit

+α7AUDITit+α8DTEit +α9DINCOMEit+εit


Constant ? 0.5900 0.0801 7.3648 0.0000***

SIZE + 0.0153 0.0047 3.2790 0.0012***

PROFIT + 0.0173 0.0120 1.4406 0.1509

PE + 3.36E-05 1.49E-05 2.2562 0.0249**

MTB + -8.04E-05 2.80E-05 -2.8692 0.0045***

R&D + 0.0087 0.0138 0.6312 0.5285

TYPE - 0.0034 0.0145 0.2345 0.8148

Audit +/- -0.0131 0.0186 -0.7038 0.4822

DTE +/- 0.0273 0.0111 2.466 0.0143**

DINCOME ? 0.0232 0.0135 1.7117 0.0882*

Adjusted R2 = 0.2237 Durbin-Watson Statistics = 1.9801 F statistics = 10.3266 p-value = 0.0000 *** and ** indicate significance at p < 0.01 and p < 0.05 respectively.

Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity DINCOME = 1 for positive earning, 0 otherwise i = firm t = year


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Table 7.11: Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics.

TRANSPit=α0+α1SIZEit+α2PROFITit+α3PEit+α4MTBit+α5R&Dit+α6TYPEit+α7AUDITit +α8DTEit +εit

Variable Predicted Sign Coefficient Std. Error t-Statistics Prob Panel A: Profitable Firms (n=145) Constant ? 0.5848 0.0862 6.7838 0.0000*** SIZE + 0.0189 0.0045 4.225 0.0000*** PROFIT + -0.1055 0.0604 -1.7486 0.0826* PE + -1.30E-06 2.80E-05 -0.0463 0.9631 MTB + -3.51E-05 2.83E-05 -1.2387 0.2176 R&D + -0.0196 0.0151 -1.3021 0.1951 TYPE - 0.0131 0.0210 0.6235 0.5340 Audit +/- -0.0311 0.0203 -1.5363 0.1268 DTE +/- 0.0239 0.0129 1.8521 0.0662* Adj. R2= 0.1684 DW Stat. 1.7984 F Stat. = 4.6452 Prob. = 0.0000 Panel B: Loss-Making Firms (n=115) Constant ? 0.6235 0.1772 3.5189 0.0006*** SIZE + 0.0128 0.0106 1.2103 0.2288 PROFIT + 0.0201 0.0131 1.5323 0.1284 PE + 2.36E-05 2.36E-05 1.0026 0.3184 MTB + -0.0005 0.0003 -1.6869 0.0946* R&D + 0.0527 0.0281 1.8769 0.0633* TYPE - 0.0035 0.0219 0.1616 0.8719 Audit +/- -0.0075 0.0260 -0.2904 0.7721 DTE +/- 0.0235 0.0264 0.8896 0.3757 Adj. R2= 0.1636 DW Stat. 1.7343 F Stat.= 3.7880 Prob. = 0.0006

***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10 respectively. A regression analysis without the outliers also was run, however this does not change the conclusion on SIZE.

Variable definitions: SIZE = natural log of total assets (in thousand). PROFIT = profitability. PE = price-to-earnings ratio. MTB = market-to-book ratio. R&D = 1 for R&D firm, 0 otherwise. TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big5/6 auditor, 0 otherwise. DTE = total liabilities divided by book value of common equity i = firm t = year


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7.5 Discussion and Analysis

7.5.1 Disclosure Quality

Chalmers (2001) provides evidence on the increase in the number of firms disclosing

information about derivatives between 1992 and 1998. She indicates that both the ED

65 Disclosure and Presentation of Financial Instruments and the probability of the

development of a standard, i.e. AASB 1033, influenced firms to enhance their

reporting on derivatives. This thesis provides similar evidence in the extractive

industries. However, the number of firms disclosing all information as required by

paragraph 5.2 to 5.9 has reduced from 11 in 1998 to 10 in 2001 (panel A Table 7.3),

but this was followed by an increase in the number of firms providing 90% to 99%

information (20 in 1998 to 31 in 2001). The majority of these firms are Top 500 firms.

Non-Top 500 firms tend to be small firms, which may not be exposed to risk, or may

experience small exposure to risk, therefore these firms may use in-house techniques

to reduce or overcome risk.

The reduction in the number of firms that report high quality information can be

explained by the fact that firms have simplified their disclosures, such as providing

aggregate or net amount of net fair values instead of previously expanded information.

In addition to that, some firms tended to ignore “other” requirements that relate to the

various sub-sections. For example, paragraph 5.7 requires firms, where one or more

financial assets are recognised at an amount in excess of their net fair value, to

disclose: a) the carrying amount and the net fair value of either an individual or group

of assets, b) the reasons for not reducing the carrying amount and c) the nature or

basis for the belief that the carrying amount will be recovered. The majority of the


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firms failed to disclose the basis for their belief that the carrying amount of financial

assets will be recovered and therefore, their disclosure quality is less than firms that

disclose all the requirements.

However, the most important finding is that even though the disclosure is mandatory,

the majority of firms provide less than full information (50%-99%). Therefore, it is

necessary for the standard setters or regulators to use their enforcement powers to

ensure better compliance (Kothari, 2000).

This study found among limited liability and no-liability firms that the level of

disclosure varies significantly. No-liability firms tend to disclose less information,

especially on hedges of anticipated transactions and risk information (Table 7.4 and

Table 7.5). The rationale for this could be that no-liability firms incur higher relative

costs of accumulating detailed information about hedges of anticipated transactions,

risk information, net fair value and commodity contracts information. Alternatively,

increased disclosure could endanger their competitive position.

While fair value is relevant for financial statement users to assess the effect of

derivative transactions (Rasch and Wilson, 1998), the disclosure of this component is

less transparent than the policy information component (Table 7.4). These firms may

be unwilling to move to fair value accounting, as reported in Delloitte Touche

Tohmatsu (2000), or disclosing net fair value may require them to incur additional

costs of accumulating net fair value information.


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Firms continue to use their discretion in the disclosure of certain information, in

particular, net fair value information, even though this is required by AASB 1033.

Therefore, as in Chalmers and Godfrey (2000), this lack of disclosure may hinder the

understandability, comparability and consistency, of the disclosures and hence the

quality of derivative disclosures among firms in the extractive industries

The idea of requiring firms to disclose risk information and net fair value information

is to aid financial statement users in understanding the effects of interest rate risk and

credit risk on firms’ cash flows. Failure to disclose such information may cause

financial statement users to underestimate the risk to which the entity is exposed and

thus the potential gain or loss. Detailed information on hedges of anticipated

transactions would enable users of financial statements to understand the nature and

effect of a hedge on future transactions.

7.5.2 Comparison with Prior Studies

Prior studies have provided evidence on the association between disclosure quality of

other information in the annual report and firm characteristics. Based on these studies,

the firm characteristics model was developed to provide answers to four research

questions developed in chapter five. Table 7.12 presents a summary of the findings of

prior studies and the current study.


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Table 7.12: Results on the Association between Disclosure Quality and Firm Characteristics.

Prior Studies Current Study

Firm

Characteristics Significant Not Significant

Size Singhvi and Desai (1971), Firth (1979), Imhoff (1992), Wallace et al. (1994), Wallace and Naser (1995), Riahi-Belkaoui (2001), Ali et al. (2003), Ahmed and Nicholls (1994) and Cooke (1989, 1991)

Malone et al. (1993) Pooled data: Positive and significant at p < 0.001

Ranked regression: Positive and significant at p < 0.001

Performance

Wallace and Naser (1995) and Ali et al. (2003)

nil

Pooled data: Profitability is positive and significant at p < 0.05. Price-earnings ratio is positive and significant at p < 0.05. Ranked regression: Ranked PE is positive and significant at p < 0.001

Type of firm in the industries

n.a.

n.a.

Not significant in any of the analyses

Auditor

Singhvi and Desai (1971), Ahmed and Nicholls (1994) and Wallace and Naser (1995)

Firth (1979), Malone at al. (1993), Wallace et al. (1994) and Ali et al. (2003).

Not significant in any of the analyses

Market-to-book ratio

n.a.

Eng and Mak (2003)

Pooled: Negative and significant at p < 0.001 Ranked regression: Not significant


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Prior Studies Current Study

Firm Characteristics

Significant Not Significant R&D

n.a.

n.a.

Not significant in pooled and ranked regression

Debt-to-equity ratio

Low DTE- Imhoff (1992) High DTE – Malone et al. (1993), Ahmed and Courtis (1999)

Hossain and Adams (1995) and Ali et al. (2003)

Pooled: Positive and significant at p < 0.05 Ranked regression: Positive and significant at p < 0.001


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Table 7.12 indicates that size, performance (represented by profitability and PE ratio)

and leverage are positively and significantly related to disclosure quality of derivative

instruments. Market-to-book ratio is negatively and significantly related to disclosure

quality. Large firms are expected to provide transparent information to reduce

political costs (Cooke, 1989). As the majority of large firms in the extractive

industries use derivatives, and are exposed to various risks, providing transparent

information (by complying with the AASB 1033 requirement) assists investors to

better understand the risks attached to the instruments. Furthermore, large firms may

incur a lower cost of accumulating information and therefore, are able to provide

detailed information about derivative instruments.

Similar to Wallace and Naser (1995) and Ali et al. (2003), the current study provides

evidence that profitability and the PE ratio are positively and significantly related to

high quality derivative information. By providing detailed derivative information,

high performance firms indicate the positive effects of derivative instruments on

improving their net income. As high quality information will lead to higher share

prices (Miller and Bahnson, 2002), this will boost management compensation

(Wallace et al., 1995) such as a higher salary or maximisation of their bonus.

Furthermore, as high profitability firms are subject to political and monitoring costs,

providing detailed information will limit these costs. These firms also have higher

disclosure levels since they have the resources to pay the cost of increased disclosure.

Two of the control variables used in this study are significantly related to the

disclosure quality of derivative information. These are market-to-book and debt-to-

equity ratios. However, market-to-book ratio is negatively related to disclosure


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quality. The results indicate that high growth firms tend to withhold some information

about derivatives. This will reduce the information about the risk attached to the

instruments, enabling them to remain competitive. This study found that debt-to-

equity ratio is positive and significantly related to disclosure quality. This result is

consistent with Malone et al. (1993).

However, only size and profitability are significant in the average of four years’ data

analysis (Table B 1 Appendix B). The significant results for the pooled data might be

influenced by a particular year. The year-by-year analysis presented in Table B 1

Appendix B indicates that size is significant in 2000 and 2001, and profitability is

only significant in 2000. That may be due to the reaction over the re-issuance of

AASB 1033 in 199977. As larger firms and firms with higher profitability may be

subject to political costs and monitoring costs, they may provide more transparent

information, especially immediately after the issuance of accounting pronouncements.

Since none of these variables are significant in 1998 and 1999, this suggests that the

results in Table 7.8 might be influenced by a particular year (Lang and Lundholm,

1993). Similar results for the estimation without the influential observations (outliers)

on each procedure are documented. These are presented in Table E 1 Appendix E.

7.6 Summary The quality of derivative disclosures among firms in the extractive industries has

increased since the accounting standard AASB 1033 Presentation and Disclosure of

Financial Instruments was applicable. However, firms still use discretion in

77 The results for year-by-year and average four year analyses are presented in Appendix B.


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disclosing derivative information, especially in relation to net fair value. What is

concerning is that the majority of firms provide less than full information (50-99%).

Kothari (2000) indicates that regulators need to use their enforcement power to ensure

better compliance. Overall, the multivariate analyses indicate that larger firms tend to

provide more transparent derivative information within the extractive industries.

These findings hold for both the ranked regression technique and the average of four

years’ data (Appendix B). Also significantly related to derivative disclosure quality

are price-earnings and debt-to-equity ratios (leverage). Similar results are obtained for

each procedure using estimation without the influential observations (outliers –

Appendix E). Chapter 8 provides evidence of the association between the quality of

derivative disclosures and share prices and the value relevance of hedge transactions

and net fair value information in particular, the unrealised gain or loss of financial

instruments (URGL) and off-balance sheet derivative financial instruments (OBDI).

Chapter 8: Results: Value Relevance of Derivative Disclosures

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CHAPTER 8 RESULTS: VALUE RELEVANCE OF DERIVATIVE DISCLOSURES

This chapter presents the results on the association between the market value of firms

and derivative information, such as hedge transactions and net fair value. The results

indicate that in addition to the significance of the book value of equity, market

participants regard: a) the disclosure quality of derivative information as value

relevant, b) hedge information and risk information components are value relevant, c)

qualitative information is as important as quantitative information, d) subject to the

model specification, the net fair value of financial instruments is value relevant and e)

the incremental explanatory power of net fair value and the unrealised gain or loss of

financial instruments beyond book value of financial and non-financial instruments

and earnings valued at historical cost is very low.

The remaining sections of this chapter are organised as follows. Section 8.1 discusses

the diagnostic tests undertaken. Multiple regression results on pooled data are

discussed in section 8.2. Section 8.3 presents the results on the incremental

explanatory power of the net fair value and unrealised gain or loss of financial

instruments. Section 8.4 discusses the results and section 8.5 summarises the chapter.


169

8.1 Diagnostic Tests

Several diagnostic tests, as explained in chapter 7, are performed on each model to

ensure the validity of the results. These tests for are normality, autocorrelation,

heteroscedasticity and multicollinearity.

8.1.1 Normality Tests

The normality tests using Jarque-Bera statistics and graphical analysis, as discussed in

chapter 7 (section 7.1.1), are performed on the residuals of all models. Table 8.1

below presents statistics on skewness, kurtosis and Jarque-Bera for all models. The

table indicates that six of the models (equations 6.3, 6.6, 6.9, 6.10, 6.11 and 6.12)

experience negative skewness and equation 6.4 experiences positive skewness. Table

8.1 indicates that the kurtosis test statistics for all models is less than 3, with the

highest kurtosis being 2.9669 for equation 6.11 and the lowest being 2.4557 for

equation 6.12. The probability of the Jarque-Bera statistics indicates that the series is

normally distributed.

Table 8.1:Normality Test of Value Relevance Models

Equation Skewness Kurtosis Jarque-Bera Stat. Prob 6.3 -0.1001 2.7932 0.8734 0.6462 6.4 0.0218 2.7566 0.6445 0.7245 6.6 -0. 0274 2.5640 2.0356 0.3614 6.9 -0.0495 2.7409 0.8109 0.6667 6.10 -0.0997 2.7137 1.2835 0.5264 6.11 -0.0588 2.9669 0.1574 0.9243 6.12 -0.0872 2.4557 3.4430 0.1788


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8.1.2 Autocorrelation Tests

Autocorrelation is unlikely to be a problem for all the equations. If there is no serial

correlation, the Durbin-Watson statistic (d) will be around 2. A d value below 2

indicates a positive correlation and a value between 2 and 4 indicates a negative

correlation. The closer a d value is to 2 provides more evidence of the absence of

autocorrelation78. The Durbin-Watson statistics for equations 6.3 to 6.12 are within

the range of 1.8514 (equation 6.4) and 2.1015 (equation 6.10). However, due to the

limitations of Durbin-Watson statistics, the Breusch-Godfrey serial correlation LM

tests were also performed. The test results indicate that autocorrelation is not a major

concern for all equations as the probability estimate is not significant.

8.1.3 Heteroscedasticity Tests

White’s heteroscedasticity test (White, 1980), reported by Eviews 4.0 statistical

package, indicates that heteroscedasticity does exists in the value relevance models.

The null hypothesis of homoscedasticity is rejected at p < 0.001 for equations 6.3 to

6.11. Therefore, White’s heteroscedasticity-corrected regression was performed on

those equations. While other procedures fail to overcome heteroscedasticity, White’s

heteroscedasticity-corrected regression produces adjusted standard errors of the

estimated regression coefficients to allow for correct statistical inferences to be

drawn.

78 Detailed discussion is in subsection 7.1.2.


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8.1.4 Multicollinearity Test

Multicollinearity occurs when there is a linear relationship between the independent

variables. Table 8.2 presents the correlation matrix between the independent variables,

wherein a pairwise correlation matrix is shown for the first two equations for the

value relevance models (i.e. equations 6.3 and 6.4). The correlation matrix indicates

that multicollinearity is unlikely to be a problem, with the highest correlation between

independent variables being 0.6269 for the variables book value of equity and

earnings, well below the suggested level of a high correlation (about 0.8 or 0.9). The

pair plot of each independent variable also indicates that the variables are not

correlated.

Table 8.2: Correlation Coefficients between Variables Correlation Matrix Value relevance of Disclosure Quality (n=253)

LMV BVE Earnings Transp CINFV CIHedge CIRisk LMV 1.0000 BVE 0.7011*** 1.0000 E 0.4631*** 0.6269*** 1.0000 Transp 0.4528*** 0.2653*** 0.1469** 1.0000 CINFV -0.1165* -0.0480 0.0505 0.1136** 1.0000 CIHedge 0.4116*** 0.2410*** 0.1443** 0.4022*** -0.3210*** 1.0000 CIRisk 0.1800*** 0.1342** 0.1208* 0.6033*** 0.3095*** -0.1451** 1.0000



months following the financial year t for firm i, BVE = book value of equity at year end t for firm i, E = earnings for year t available to firm i’s common shareholders, TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s

total possible disclosure scores CINFV = Component score of net fair value, CIHedge = Component score of hedge information, CIRisk = Component score of risk information.


172

Multicollinearity does exist for the rest of the value relevance models (equation 6.6 to

equation 6.12). The presence of multicollinearity in these models is revealed in Table

8.3 through to Table 8.5. Table 8.3 presents a correlation matrix for equation 6.6.

Multicollinearity may cause a problem for this model with the highest correlation of

0.9822, between URGL and OBDI.

Several remedial techniques have been suggested for situations such as this, including

dropping the variables, combining the cross-sectional and time series data (pooling

the data) and the use of transformed variables (Gujarati, 2003, p.364). However, the

first and third procedures suggested above are not appropriate as dropping the highly

correlated variable may diminish the objective of this study to examine the value

relevance of each independent variable over the other. Therefore, this study is based

on pooled data incorporating the second suggested technique. Nevertheless, some

researchers such as Ahmad (2000), Bernard (1987) and Board, Rees and Sutcliffe

(1992) acknowledge that multicollinearity does exist ‘naturally’ in capital markets

research, which use accounting numbers as explanatory variables, as the numbers are

‘naturally’ interrelated. In the face of the above arguments the estimation was

conducted with the existence of multicollinearity. In keeping with prior research the

models are tested excluding the highly correlated variables and the results compared

to the models with multicollinearity issues.


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Table 8.3: Correlation Coefficients between Variables: Value Relevance of Hedge Disclosures

LMV BVE E CIHedge OBDI URGL

LMV 1.0000 BVE 0.7011*** 1.0000 E 0.4631*** 0.6269*** 1.0000 CIHedge 0.4116*** 0.2410*** 0.1443** 1.0000 OBDI -0.0383 0.0358 0.0986 -0.0548 1.0000 URGL -0.0437 0.0279 0.0646 -0.0585 0.9822*** 1.0000

***, ** and * indicates significance at p < 0.01, p < 0.05 and p < 0.10, respectively.

Variable definitions: P = natural log market value of firms’ common equity measured three months

following the financial year, BE = book value of equity at year end, E = earnings for year t available to firm i’s common shareholders CIHedge = component score of hedge information URGL = unrealised gain or loss of financial assets and financial liabilities OBDI = off-balance sheet derivative financial instruments t = time i = firm

Table 8.4 presents a correlation matrix for the value relevance model of net fair value.

Table 8.4 indicates that multicollinearity is likely to be a problem for the equation 6.9,

with strong correlations between TBFI and TFFI (0.9985). This is followed by the

correlation between BVNFI and TBFI (0.9576).


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Table 8.4: Correlation Coefficients between Variables: Value Relevance of Net Fair Value

LMV BVNFI E CINFV OBDI TBFI TFFI LMV 1.0000 BVNFI 0.6641*** 1.0000 E 0.4631*** 0.5419*** 1.0000 CINFV -0.1165* -0.0303 0.0505 1.0000 OBDI -0.0383 -0.0128 0.0986 -0.0792 1.0000 TBFI -0.5669*** -0.9576*** -0.3929*** 0.0056 0.0736 1.0000 TFFI -0.5665*** -0.9560*** -0.3995*** 0.0010 0.0780 0.9985*** 1.0000

***, ** and * indicates significance at p < 0.01, p < 0.05 and p < 0.10, respectively. Variable definitions: P = natural log market value of firms’ common equity measured three months following the

financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments TFFI = net fair value of financial instruments OBDI = off-balance sheet derivative financial instruments CINFV = component score of net fair value t = time i = firm

The correlation between the independent variables in the value relevance model of the

unrealised gain or loss of financial instruments is presented in Table 8.5. Table 8.5

indicates that multicollinearity also occurs in equations 6.11, with the highest

correlation of 0.9576 between BVNFI and TBFI.


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Table 8.5: Correlation Coefficients between Variables: Value Relevance of Unrealised Gain or Loss of Financial Instruments

LMV BVNFI E CINFV OBDI TBFI DIFFA DIFFL

LMV 1.0000 BVNFI 0.6641*** 1.0000 E 0.4631*** 0.5419*** 1.0000 CINFV -0.1165* -0.0303 0.0505 1.0000 OBDI -0.0383 -0.0128 0.0986 -0.0792 1.0000 TBFI -0.5669*** -0.9576*** -0.3929*** 0.0056 0.0736 1.0000 DIFFA -0.2553*** -0.3130*** -0.4285*** -0.0356 -0.0811 0.1891*** 1.0000 DIFFL -0.2251*** -0.2752*** -0.2635*** 0.0702 -0.1999*** 0.1350** 0.2287*** 1.0000

***, ** and * indicates significance at p < 0.01, p < 0.05 and p < 0.10, respectively. Variable definitions: P = natural log market value of firms’ common equity measured three months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments OBDI = off-balance sheet derivative financial instruments DIFFA = difference between net fair value of financial assets and book value of financial assets. DIFFL = difference between net fair value of financial liabilities and book value of financial liabilities. CINFV = component score of net fair value t = time i = firm


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8.2 Multiple Regression Results

8.2.1 Disclosure Quality of Derivative Information and the Market Value of Firms

Research questions 5(a) and 5(b) explore the association between the disclosure

quality of derivative information and firms’ share prices. To explore this research

question, two models are developed as specified in equations 6.3 and 6.4. Since both

models are subject to heteroscedasticity, White’s heteroscedasticity-correction is

employed. The results of the regression estimates are reported in Table 8.6. Panel A

shows the results for the first model which estimates the association between the total

disclosure score and firm market value. Book value of equity and TRANSP or

disclosure quality are positive and significantly related to market value at p < 0.01.

These results are based on the estimation without the influential variables because of a

large difference between the book value of equity and earnings between these firms

and the rest of the companies.79

The second model identifies the components of the disclosure index that are the most

valuable to users. Results are reported in Panel B of Table 8.6, which shows that the

book value of equity, the component score of hedge information and the component

score of risk information are positive and significant at p < 0.01. The adjusted R2 of

this model is 56.56%, which is higher than the adjusted R2 of the model presented in

panel A.

79 Estimation on the full sample and winsorizing at 5% provides similar results, where the book value of earnings and transparency are positive and significantly related with market value at p < 0.001. Table F 1 of Appendix F presents the results for the full sample.


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Table 8.6: The Association between Information Quality of Derivative Disclosures and the Market Value of Firms (n=253)1

Variables Coefficient Std Error T-statistics Prob

Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 (equation 6.3) BV 2.14E-09 2.97E-10 7.1849 0.0000*** E 8.45E-10 1.02E-09 0.8301 0.4073 TRANSP 5.7676 0.8191 7.0415 0.0000*** Constant 12.2883 0.7176 17.1231 0.0000 Adj R2 = 0.5643 DW Statistics = 1.8915 F-statistics = 109.7815 Prob. = 0.0000 Panel B : Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+εit (equation 6.4)

BV 2.12E-09 2.71E-10 7.7973 0.0000*** E 6.95E-10 9.33E-10 0.7443 0.4574 CINFV -2.0917 1.8918 -1.1057 0.2699 CIHedge 6.2335 1.0363 6.0151 0.0000*** CIRisk 3.7802 1.1417 3.3111 0.0011*** Constant 15.9886 0.4593 34.8115 0.0000 Adj R2 = 0.5656 DW Statistics = 1.8514 F-statistics = 66.6310 Prob. = 0.0000 *** indicates significance at p < 0.01 1 Results are based on White’s heteroscedasticity-corrected regression Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BV = book value of equity at year end E = earnings for year available to firm’s common shareholders TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s

total possible disclosure scores CIHedge = component score of hedge information CINFV = component score of net fair value CIRisk = component score of risk information i = firm t = year

Industry specific factors might influence the results on earnings in both models. Firms

in the extractive industries, especially exploration companies, may incur substantial

losses (Henderson and Peirson, 2000, p. 682) and therefore, market participants may

ignore current earnings in firm valuation. This is because the current level of earnings


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is unlikely to be a useful indication of future performance. Therefore, in this case

investors tend to rely on book values rather than earnings (Collins, Pincus and Xie,

1999). Moreover, within the extractive industries, negative earnings may be more

related to small firms. These firms potentially face a greater likelihood of

encountering financial distress or failure than do larger firms in the industries (Colins,

Maydew and Weiss, 1997).

To investigate this, the sample was ranked into small and large firms based on the top

and bottom 25% of total assets80. The sample leaves out the middle size firms. The

models were re-estimated on 128 firms/years, after eliminating two outliers, one for

each group of the sample (small and large firms). This is because the book value of

equity of these firms is larger than the rest of the sample. The results of equation 6.3

are similar to those in Panel A, Table 8.6 (see Table F 2 in Appendix F). However, the

component score of the net fair value information is no longer significant (Panel B

Table F 2 in Appendix F). In addition to the above procedure, separate analysis on

each group of firms (firms in the top and bottom 25% based on total assets) indicates

that earnings is significant for the bottom 25% firms, but not for the top 25% firms

(Table F 3 in Appendix F). However, for the large firms, the book value of equity and

the component score of risk information are positive and significant at p < 0.001, and

the component score of hedge information and net fair value are negative and


80 The range of total assets for the small firms is between A$10,694.72 to A$172,162.00, and A$23,888,934.00 to A$8,335,501,500.00 for the large firms.


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8.2.2 Value Relevance of Hedge Disclosures Research questions 6 and 7 extend research questions 5(a) and (b), by focusing to the

specific information required by AASB 1033. These research questions raise the issue

of the importance of qualitative/quantitative information and recognised/unrecognised

information. Prior studies provide evidence that the disclosed items, especially the

unrealised gain or loss and off-balance sheet derivative financial instruments (e.g.

Davis-Friday et al., 1999 and Pfeiffer, 1998) are not considered as important in firm

valuation. Therefore, to explore these research questions the component score of

hedge information is included in the Ohlson model. This is specified in equation 6.5.

Table 8.7 reports the multiple regression results on the association between market

value and the disclosure of hedge information and unrealised gain or loss of financial

instruments as required, based on the main model of the value relevance of hedge

disclosure. The results on the multiple regression analysis indicate that the book value

of equity and hedge information is significant at p < 0.01 (Panel A).

Given the significance of hedge information this provides an opportunity to explore

whether detailed information disclosed in annual reports is valued as important. This

is specified in equation 6.6. Results are presented in Panel B Table 8.7. As in Panel A

Table 8.7, the book value of equity and hedge information are significant at p < 0.01.

However, Panel B indicates that none of the disclosed items (unrealised gain or loss of

financial instruments and off-balance sheet derivative financial instruments) is

significant. The adjusted R2 for Panel B is 54.87%.


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Table 8.7: The Association between Hedge Disclosure and Market Value of the Firms (n=253)1

Variables Coefficient Std Error T-statistics Prob Panel A : Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + εit (equation 6.5) BV 2.2E-09 2.83E-10 7.7802 0.0000*** E 7.34E-10 9.79E-10 0.7498 0.4541 CIHedge 5.9655 0.9118 6.5424 0.0000*** Constant 16.3436 0.1686 96.9593 0.0000 Adj R2 = 0.5551 Durbin Watson = 1.8949 F-statistics = 103.5550 Prob 0.0000 Panel B: Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4OBDIit + α5URGL it+ εit (equation 6.6) BV 2.19E-09 2.79E-10 7.8558 0.0000*** E 8.72E-10 1.02E-09 0.8557 0.3930 CIHedge 5.8895 0.9191 6.4078 0.0000*** OBDI -1.38E-09 4.01E-09 -0.3431 0.7319 URGL 5.14E-10 3.95E-09 0.1301 0.8966 Constant 16.3491 0.1693 96.5921 0.0000 Adj R2 = 0.5487 Durbin Watson = 1.8876 F-statistics = 62.2852 Prob 0.0000

*** indicates significance at p < 0.01 1 Results are based on White’s Heterosdecasticity Corrected Regression Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year, BV = book value of equity at year end, E = earnings for year t available to firm i’s common shareholders CIHedge = component score of hedge information URGL = unrealised gain or loss of financial assets and financial liabilities OBDI = off-balance sheet derivative financial instruments t = time i = firm

8.2.3 Value Relevance of Net Fair Value Disclosures

Research question 8 raises the issue of the importance of net fair value information in

firm valuation. Given that net fair value is relevant for decision-making, the models

were developed in such a way as to provide evidence on the association between the

market value of the firm and net fair value information, and to provide evidence on

the incremental explanatory power of net fair value beyond that of book value. To


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explore this issue, the component score of net fair value information is included in the

Ohlson model as in equation 6.7. The results indicate that the book value of equity is

positive and significant at p < 0.001 and the net fair value information is negative p <

0.05. The adjusted R2 for this model is 49.39%.

As net fair value information is significant, the next step is to estimate the model that

separates the book value of financial instruments from the book value of non-financial

instruments. This is specified in equation 6.8. The results of these estimations are

presented in Panel A Table 8.8. Panel A indicates that both the book value of non-

financial instruments and the book value of financial instruments are positive and

significant at p < 0.001. However, the net fair value information is negative and

marginally significant at p < 0.10. The adjusted R2 for this model is 49.64%, which is

not overly different from the adjusted R2 of equation 6.7. This suggests that the

estimation of net fair value information is unaffected by the separation of financial

and non-financial instruments. However, separating financial and non-financial

instruments will provide a better understanding of the value relevance of one class

over the other.


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Table 8.8: The Association between Net Fair Value and Market Value (n=253) (White’s Heteroscedasticity Corrected Regression)


Panel A: Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + α4CINFV,it + εit (equation 6.8) BVNFI 2.94E-09 4.70E-10 6.2526 0.0000*** E 2.22E-10 1.01E-09 0.2199 0.8262 TBFI 3.61E-09 9.53E-10 3.7885 0.0002*** CINFV -3.3280 1.7949 -1.8542 0.0649* Constant 16.8700 0.2284 73.8541 0.0000 Adj R2 = 0.4964 Durbin Watson = 2.0048 F-statistics = 63.1103 Prob = 0.0000

Panel B: Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + α4TFFIit + α5OBDIit + α6CINFV,it + εit (equation 6.9) BVNFI 3.06E-09 4.82E-10 6.3477 0.0000*** E 2.29-10 1.01E-09 0.2267 0.8208 TBFI 4.85E-09 4.47E-09 1.0859 0.2786 TFFI -9.44E-10 4.45E-09 -0.2124 0.8320 OBDI -1.49E-09 9.69E-10 -1.5412 0.1246 CINFV -3.6069 1.7956 -2.0087 0.0457* Constant 18.0064 0.3775 47.7049 0.0000*** Adj R2 = 0.5004 Durbin Watson = 2.0004 F-statistics = 43.0727 Prob = 0.0000 Panel C : Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it +εit (equation 6.10)BVNFI 2.90E-09 5.32E-10 5.4539 0.0000*** E 6.23E-10 9.44E-10 0.6597 0.5101 TFFI 3.59E-09 1.08E-09 3.3055 0.0011*** OBDI -1.54E-09 9.45E-10 -1.6290 0.1046 CINFV -3.5531 1.7927 -1.9820 0.0486* Constant 18.0032 0.3774 47.7061 0.0000 Adj R2 = 0.4995 Durbin Watson = 2.0149 F-statistics = 51.2980 Prob 0.0000

***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments TFFI = net fair value of financial instruments OBDI = off-balance sheet derivative financial instruments CINFV = component score of net fair value t = time i = firm


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Panel B Table 8.8 provides evidence on the association between net fair value and the

market value of the firm based on the expanded Ohlson model. Panel B indicates that

the book value of non-financial instruments is positive and significantly related to

market value at p < 0.001. However, including net fair value of financial instruments

and OBDI leaded book value of financial instruments to be no longer significant. Also

significant is the component score of net fair value.

Since there is collinearity between TBFI and TFFI, the book value of financial

instruments was excluded from the model. Panel C indicates that dropping TBFI

results in TFFI being positive and significant at p < 0.001. CINFV is also significant at

p < 0.0581.82 The positive value of the net fair value of financial instruments indicates

that the incremental explanatory power of net fair value of financial instruments is

beyond that of the other independent variables included in the model (Barth, 1994;

Venkatachalam, 1996; Simko, 1999).

8.2.4 Value Relevance of the Unrealised Gain or Loss of Financial Instruments

Research question 8 raises the issue of the value relevance of the unrealised gain or

loss, which is measured as the difference between the net fair value and the carrying

value of financial instruments. Table 8.6 presents the multiple regression results on

the association between the unrealised gain or loss on financial assets, financial

liabilities, derivative instruments and the market value of the firm. Panel A Table 8.9

81 In re-estimating the equation by replacing BVNFI with TBFI, the results indicate that earnings and CINFV are significant at p < 0.01. However, the adjusted R2 is 39.68%, which is lower than the adjusted R2 presented in Panel C Table 8.8. Results presented in Panel C provide better results compared to the re-estimation results. 82 Results for year-by-year analysis presented in Table C 3 Appendix C indicate that OBDI is significant in 1999 to 2001. Also significant is TFFI in 1998 and 1999. However, CINFV is not significant in any year.


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indicates that BVNFI and TBFI are positively significantly related to market value at p

< 0.001. The primary interest of this study is the unrealised gain or loss on financial

assets (DIFFA), financial liabilities (DIFFL) and off-balance sheet derivative

financial instruments (OBDI). Panel A indicates that none of these variables are

significant. Nevertheless, CINFV is significant at p < 0.10. The results indicate that the

unrealised gain or loss on financial instruments is not regarded as value relevant.

Table 8.9: The Association between the Market Value of Firms and the Difference Between Net Fair Value and Book Value of Financial Instruments

(n=253)1

Variables Coefficient Std Error T-statistics Prob Panel A: Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit +

α6OBDIit + α7CINFV,it + εit (equation 6.11) BVNFI 3.53E-09 6.52E-10 5.4103 0.0000*** E 2.14E-10 1.05E-09 0.2031 0.8392 TBFI 4.79E-09 1.28E-09 3.7287 0.0002*** DIFFA 5.54E-09 5.53E-09 1.0018 0.3174 DIFFL 8.69E-09 5.75E-09 1.5107 0.1322 CINFV -3.4493 1.7926 -1.9242 0.0555* OBDI -1.35E-09 1.02E-09 -1.3273 0.1856

Constant 17.9450 0.3816 47.0317 0.0000 Adj R2 = 0.5051 Durbin Watson = 1.9996 F-statistics = 37.7401 Prob = 0.0000 Panel B: Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit +

α6CINFV,it + εit (equation 6.12)

BVNFI 1.17E-09 1.77E-10 6.6101 0.0000*** E 2.74E-09 1.04E-09 2.6360 0.0089** DIFFA -1.47E-09 3.71E-09 -0.3959 0.6925 DIFFL -3.31E-09 5.11E-09 -0.6478 0.5177 CINFV -4.6758 1.8685 -2.5025 0.0130** OBDI -1.04E-09 1.03E-09 -1.0098 0.3136

Constant 18.3498 0.3852 47.6335 0.0000 Adj R2 = 0.4716 Durbin Watson = 1.9483 F-statistics = 36.5872 Prob = 0.0000

***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively. 1 Results are based on White’s heteroscedasticity-corrected regression. Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = Total book value of financial instruments OBDI = off-balance sheet derivative financial instruments DIFFA = difference between net fair value of financial assets and book


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Excluding the book value of financial instruments (TBFI) from equation 6.11 (as

specified in equation 6.12) resulted in earnings being significant at p < 0.01 and CINFV

significant at p < 0.05 (Panel B Table 8.9)83. These results indicate that the unrealised

gain or loss on financial assets and financial liabilities is not value relevant and does

not provide incremental explanatory power beyond other including variables (Barth,

1994; Venkatachalam, 1996; Simko, 1999).

83 In re-estimating the equation in Panel B by replacing BVNFI with TBFI, the results indicate that earnings, TBFI and CINFV are significant at p < 0.01. Also significant at p < 0.10 is DIFFL. However, the adjusted R2 is, 40.44%, which is lower than the adjusted R2 presented in Panel B Table 8.9.


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8.3 Incremental Explanatory Power of Net Fair Value and the Unrealised Gain or Loss of Financial Instruments

To examine the incremental explanatory power of net fair value and the unrealised

gain or loss of financial instruments, the adjusted R2 is examined. Table 8.10 and

Table 8.11 present the results on the adjusted R2 of equations 6.9, 6.11 and 6.13 to

6.15. The explanatory power of the book value of non-financial instruments and

earnings and the explanatory power of net fair value and the unrealised gain or loss on

financial instruments are reported in columns 3 and 4 respectively. The incremental

explanatory power of the book value of non-financial instruments and earnings valued

at historical cost beyond the net fair value and the unrealised gain or loss on financial

instruments are reported in column 5. The incremental explanatory power of net fair

value and the unrealised gain or loss on financial instruments beyond the book value

of non-financial instruments and earnings valued at historical cost are reported in

column 6.

Table 8.10 presents the adjusted R2s of equations 6.9, 6.13 and 6.14 for each year and

for the pooled data. Table 8.10 indicates that the incremental explanatory power of net

fair value above the book value of non-financial instruments and earnings (AdjR2nfv/h)

for the pooled data is very low; i.e. 0.80%, compared to the incremental explanatory

power of book value of non-financial instruments and earnings valued at historical

cost beyond the net fair value, (AdjR2h/nfv) which is 17.41%. Table 8.10 provides

evidence that the incremental explanatory power of the net fair value beyond the book

value of non-financial instruments and earnings at historical costs has increased.

However, the incremental explanatory power of the book value of non-financial

instruments and earnings valued at historical cost beyond the net fair value has


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reduced. Table 8.10 indicates that in 1998 the AdjR2nfv/h is 1.98% and this increases to

8.34% in 2001, and the AdjR2h/nfv reduces from 27.97% in 1998 to 8.61% in 2001.

Table 8.10: The Incremental Explanatory Power of Net Fair Value beyond the Book Value of Financial and Non-financial Instruments and Earnings Valued at

the Historical Cost

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4TFFIit + α5OBDIit + α6CINFV,it +εit - AdjR2

h,nfv Pit = α0 + α1TFFIit + α2OBDIit + α3CINFV,it +εit - AdjR2

nfv Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + εit - AdjR2

h AdjR2

h,nfv AdjR2h AdjR2

nfv AdjR2nfv/h AdjR2

h/nfv 1998 (n=62) 0.5041 0.4843 0.2244 0.0198 0.2797 1999 (n=63) 0.5616 0.5342 0.3563 0.0274 0.2053 2000 (n=64) 0.4810 0.4174 0.3477 0.0636 0.1333 2001 (n=64) 0.5109 0.4275 0.4248 0.0834 0.0861 Pooled data 0.5004 0.4924 0.3263 0.0080 0.1741


months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments OBDI = off-balance sheet derivative financial instruments TFFI = net fair value of financial instruments CINFV = component score of net fair value t = time i = firm AdjR2

h,nfv = the total explanatory power of book value of non-financial instruments and earnings at historical cost and net fair value

AdjR2h = the explanatory power of book value of non-financial instruments and earnings valued at

historical costs AdjR2

nfv = the explanatory power of net fair value AdjR2

nfv/h = the incremental explanatory power of net fair value AdjR2

h/nfv = the incremental explanatory power of book value of non-financial instruments and earnings valued at historical costs


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The incremental explanatory power of the unrealised gain or loss of financial

instruments beyond the book value of non-financial instruments and earnings valued

at historical cost (AdjR2urgl/h) is also very low compared to the incremental

explanatory power of the book value of non-financial instruments and earnings valued

at historical cost. Table 8.11 indicates that the AdjR2urgl/h for the pooled data is 1.26%

compared to 40.19% for AdjR2h/urgl. Consistent with the results for net fair value, the

incremental explanatory power of URGL beyond the book value of non-financial

instruments and earnings valued at historical cost increases from 8.03% (1998) to

11.84% (2001). The incremental explanatory power of the book value of non-financial

instruments and earnings valued at historical cost beyond the net fair value decreases

from 32.86% (1998) to 12.82% (2001).

Table 8.11: The Incremental Explanatory Power of Unrealised Gain or Loss of Financial Instruments Beyond the Book Value of Financial and Non-financial

Instruments and Earnings Valued at the Historical Cost (n=253)

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit + α6OBDIit + α7CINFV,it

+ εit - AdjR2h,urgl

Pit = α0 + α1DIFFAit + α2DIFFLit + α3OBDIit + α4CINFV,it + εit - AdjR2

urgl Pit = α0 + α1BVNFIit+ α2Eit + α3TBFIit + εit - AdjR2

h AdjR2

h,urgl AdjR2h AdjR2

urgl AdjR2urgl/h AdjR2

h/urgl 1998 0.5646 0.4843 0.2360 0.0803 0.3286 1999 0.6753 0.5342 0.3684 0.1411 0.3069 2000 0.5091 0.4174 0.3611 0.0917 0.1480 2001 0.5459 0.4275 0.4177 0.1184 0.1282 Pooled data 0.5050 0.4924 0.1031 0.0126 0.4019


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value of financial liabilities. CINFV = component score of net fair value t = time i = firm AdjR2

h,urgl = the total explanatory power of book value of non-financial instruments and earnings valued at historical cost and unrealised gain or loss of financial instruments

AdjR2h = the explanatory power of book value of non-financial instruments and earnings valued at

historical cost AdjR2

urgl = the explanatory power of unrealised gain or loss of financial instruments AdjR2

urgl/h = the incremental explanatory power of unrealised gain or loss of financial instruments AdjR2

h/urgl = the incremental explanatory power of book value of non-financial instruments and earnings valued at historical costs

8.4 Discussion of the Results

The objective of this chapter is to provide empirical evidence on the significance of

the disclosure quality of derivative information on firm valuation. There is no prior

evidence that directly examines the relationship between the disclosure quality of

derivative information and market value. Perhaps the most closely related paper is

Gelb and Zarowin (2002). However, they compare the association between current

stock returns and future earnings changes of high disclosure firms and low disclosure

firms. Instead of looking at the effect of high quality information on share prices, they

argue that enhanced disclosure should lead to improved predictions of future earnings.

The current study provides answers for five research questions discussed in chapter

five. A summary of the research questions and the results for each are presented in

Table 8.12.


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Table 8.12: Summary of Results of Value Relevance Models Research Questions Equations Findings Reference

RQ 5(a): Are the share prices of firms in the extractive industries associated with high quality financial statement derivative disclosure?

• Pit = α0 + α1BVit+ α2Eit + α3 TRANSP,it + εit (Equation 6.3)

Book value of equity and Transparency are significant at p < 0.001.

Panel A Table 8.6

RQ 5(b): Which AASB 1033 disclosure information is value relevant?

• Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+ε (Equation 6.4)

Book value of equity, component score of hedge information and component score of risk information are significant at p < 0.01.

Panel B Table 8.6

RQ 6: Do share prices of extractive industries firms reflect the information that firms hedge their exposure to the risks from anticipated transactions? RQ 7: Are the share prices of extractive industry firms associated with the disclosure of the unrecognised hedging gain or loss?

• Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4OBDIit + α5URGL it+ εit (Equation 6.6)

Book value of equity and component score of hedge information are significant at p < 0.001

Panel B

• Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it + εit (Equation 6.10)

Book value of non-financial instruments and TFFI are significant at p < 0.001. Component score of net fair value is significant at p < 0.05 in panel C.

Panel C Table 8.8.

RQ 8: Are the net fair value disclosures value relevant and do they provide incremental information over book values for firms in the extractive industries?

• Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit + α6CINFV,it + εit (Equation 6.12)

BVNFI and Earnings are significant at p < 0.01. CINFV is significant at p < 0.05.

Panel B Table 8.9


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8.4.1 Disclosure Quality of Derivative Information and the Market Value of Firms

Research question 5(a) examines whether high quality derivative information is

associated with firms’ share prices. Subsection 8.3.1 presents the results for this

research question. Incorporating transparency in the Ohlson model provides evidence

on the significance of book value and transparency at p < 0.001 for both the pooled

data and year-by-year analysis (as in Appendix C). This indicates that market

participants regard the disclosure quality of derivatives as an important factor in

determining market value. This finding supports the general belief that high quality

disclosures (or transparency) benefits the share market (as found in Miller, 2001;

Miller and Bahnson, 2002). Investors are more confident with high quality

information and therefore, they will be satisfied with lower returns as the risk is

reduced, which leads to higher security prices (Miller and Bahnson, 2002). Most

importantly, this study contributes to the literature by providing evidence of the

importance of transparent derivative information as it has been considered as being

value relevant by market participants.

The second model identifies which components of the disclosure index are more

valuable to users (research question 5(b)). Results for the pooled data indicate that

market participants regard the book value of equity, hedge information and risk

information as important for market valuation. This result is important to accounting

standard setters because it indicates that market participants are very selective in

determining the most important derivative information in firm valuation. Most

importantly, in this industry market participants regard net fair value as unimportant


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for firm valuation. However, only results for the first model are supported by the

results conducted using refined data presented in Table D 1 Appendix D.

8.4.2 Value Relevance of Hedge Disclosures Research questions 6 and 7 raise the issue of the importance of qualitative/quantitative

information and recognised/unrecognised information. To explore these research

questions a model specified in equation 6.6 is developed. Table 8.7 reports the

multiple regression results on the association between market value and disclosure of

hedge information and the unrealised gain or loss of financial instruments. The key

results are summarised in Table 8.12. Results indicate that the book value of equity

and hedge information are the only variables that are positively and significantly

related to market value. Both are significant at p < 0.001. The insignificance of the

unrealised gain or loss on financial instruments and off-balance sheet derivative

financial instruments indicates that none of these items are value relevant. This

finding is consistent with the prior research of Pfeiffer (1998), Aboody (1996) and

Davis-Friday et al. (1999). Hence, this indicates that market participants might ignore

the disclosed information as they consider it unimportant (Barnes, 2001).

8.4.3 Value Relevance and the Incremental Explanatory Power of Net Fair Value Disclosure and the Unrealised Gain or Loss of Financial Instruments

Research question 8 raises the issue of the value-relevance and the incremental

explanatory power of the net fair value and unrealised gain or loss on financial

instruments, measured as the difference between the net fair value and the carrying


193

value of financial instruments. The summary of the results for the value relevance of

the net fair value and the unrealised gain or loss on financial instruments are presented

in Table 8.12. Two models are developed based on Barth (1994) and Simko (1999).

The first model, based on Barth (1994), incorporates the net fair value of financial

instruments in the Ohlson model. However, the second model incorporates the

unrealised gain or loss, which is the difference between the net fair value and the book

value of the financial instruments84.

The results for the first model (equation 6.10) indicate that the book value of non-

financial instruments and net fair value of financial instruments are significant at p <

0.01. Also significant at p < 0.05 is CINFV. However, OBDI is not significant. Results

for the second model (equation 6.12), which based on Simko (1999), provide evidence

that BVNFI and E are significant. CINFV also recorded as significant at p < 0.05.

However, none of the unrealised gain or loss on financial assets (DIFFA) and

financial liabilities (DIFFL) and OBDI are significant.

The inconsistency and insignificance of the net fair value and the unrealised gain or

loss of financial instruments indicates that market participants, especially those related

to firms in the extractive industries, may not be ready or are unwilling to move

towards a net fair value regime. Users may be sceptical of the usefulness of net fair

value in assessing the effects of derivatives. This finding is contrary to the argument

made by Rasch and Wilson (1998). Further, fair value may be less reliable than

historical cost as managers use their discretion in determining fair value (Ahmad,

2000).

84 Simko (1999) used the difference between fair value and book value (URGL) to represent the fair value variable in his model.


194

This result is supported by the low incremental explanatory power of net fair value

and the unrealised gain or loss of financial instruments beyond the book value of non-

financial instruments, book value of financial instruments and earnings valued at

historical costs. The incremental explanatory power of the net fair value and the

unrealised gain or loss on financial instruments beyond the book value of financial

and non-financial instruments and earnings valued at historical cost for the pooled

data is 0.80% and 1.26%, respectively. This is less than the incremental explanatory

power of the book value of financial and non-financial instruments and earnings

valued at historical cost beyond the net fair value and the unrealised gain or loss of

financial instruments which is valued at 17.41% and 40.19%, respectively. Perhaps

the low incremental explanatory power of net fair value is due to the fact that firms

tend to provide net fair values as the carrying value or book value of the instruments.

Therefore, the disclosures of net fair value and unrealised gain or loss on financial

instruments do not provide additional information beyond book value for decision

making. However, the incremental explanatory power of net fair value and the

unrealised gain or loss of financial instruments (book value of financial and non-

financial instruments and earnings valued at historical costs) has increased

(decreased) from 1998 to 2001. This indicates that market participants in the

extractive industries appreciate the importance of net fair value and therefore the

value relevance of net fair value is expected to improve in the future.


195

8.5 Summary Chapter 8 examines the disclosure quality of derivative information based on a capital

markets study. Derivative information is considered as high quality if there is a

significant association between the information and market value. Overall, the

multiple regression results indicate that market participants regard derivative

information as value relevant. This study also found that hedge information and risk

information are regarded as value relevant. Examining the broad class of financial

instruments provides evidence that the net fair value of financial instruments is value

relevant. However, the unrealised gain or loss on financial assets (DIFFA) and

financial liabilities (DIFFL) and off-balance sheet derivative financial instruments are

not significant. However, these items are significant in some years in the year-by-year

analysis as in Table C 2 to Table C 4 in Appendix C. These results are supported by

the low incremental explanatory power of net fair value beyond the book value of

financial and non-financial instruments and earnings valued at historical cost. This

indicates that users are still sceptical about the usefulness of net fair value, contrary to

the results in Rasch and Wilson (1998). The results presented in this chapter indicate

that both firms in the extractive industries and market participants may not be ready

for the impending adoption of IAS 39.

Chapter 9: Summary and Conclusions

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CHAPTER 9 SUMMARY AND CONCLUSIONS

9.1 Summary

The objective of this study is, first, to examine the relationship between the

transparency or disclosure quality of derivative information and firm characteristics.

Second, this study investigates the value relevance of derivative disclosures in

particular hedge information, net fair value information and risk information. This

study has developed two main models to provide answers for nine research questions.

The first model, the firm characteristics model, was developed to investigate the

relationship between the disclosure quality of derivative information and

characteristics of firms in the extractive industries. The second set of models, the

market value models, provide evidence on the value relevance of the disclosure

quality of derivative information and the value relevance of hedges of anticipated

transactions, net fair value and risk information.

9.1.1 Firm Characteristics Model

The unweighted disclosure index is developed to measure disclosure quality.

Following prior research that has explored the quality of other disclosures in financial

statements, four research questions that examine the association between firm

characteristics and disclosure quality of derivative information are developed. Firm

characteristics are represented by size, high performance firms, type of firm in the

extractive industries and type of auditor. Three control variables, market-to-book


197

ratio, research and development activity and debt-to-equity ratio are also included in

the model. Size (represented by the log of total assets), high performance firms

(represented by price-earnings ratio and profitability), research and development (1

for R&D firms, 0 otherwise) and market-to-book ratio are expected to be positively

related to disclosure quality.

The estimation of the model based on pooled data assumes that each firm-year can be

treated as an independent observation. However, as in Lang and Lundholm (1993),

the degree of freedom in calculating the significance levels is overstated if the

independent variables fail to remove autocorrelation in the dependent variables.

Therefore, similar to Lang and Lundholm (1993) the regression analysis is repeated

for each year and an average of four years’ data (Appendix B). In addition to the

above, the ranked regression procedure also is performed. In this technique the

continuous variables are replaced with their rank. The smallest observation is ranked

as 1 and the largest observation is ranked as n. In the case of ties, the average ranks

are assigned.

The results support the following conclusions:

i. The disclosure quality of derivative information among firms in the extractive

industries has increased since the accounting standard, AASB 1033

Presentation and Disclosure of Financial Instruments, was applicable85.

Nevertheless, the number of firms that provide high quality derivative 85 Table 7.3 in chapter 7 provides evidence that the number of firms with a disclosure index between 0.50 and 0.89 has reduced from 34 (1998) to 24 (2001), but the number of firms with a disclosure index between 0.90 and 1.00 has increased from 31 (1998) to 41 (2001).


198

information (the disclosure index is 1.00) has dropped marginally. The

majority of firms still use discretion in the disclosure of derivative information

especially with regard to information on hedges of anticipated transactions,

risk information and net fair value information. Of concern is that the majority

of firms provide less than complete information (50-99%). Kothari (2000)

suggests that regulators need to use their enforcement power to ensure

compliance.

ii. Large firms tend to provide high quality accounting information. The results

of this study provide evidence that support the general belief of the association

between larger firms and disclosure quality. The results of the pooled

regression indicate that larger firms in the extractive industries provide higher

quality derivative information then small firms. This finding holds for both the

ranked regression and the average of four years’ data (Appendix B).

iii. The debt-to-equity ratio, measuring leverage, also appears to be associated

with high quality derivative disclosures. The results indicate that high leverage

firms in the extractive industries are more likely to provide detailed

information on derivative instruments. This result is consistent with Ahmed

and Courtis (1999) and particularly with Malone et al. (1993). Malone et al.

(1993) indicate that firms with high leverage (high debt-to-equity ratios) in the

oil and gas industry disclose more extensive financial information than firms

with lower debt-to-equity ratios.


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iv. To a lesser extent, high performance firms and firms with low growth

opportunities (low market-to-book ratio) also provide higher quality derivative

disclosures. Firms with high profitability and price-earnings ratios are more

likely to provide higher quality derivative information, perhaps because they

may be exposed to higher political costs. The negative association between

market-to-book ratio (measuring growth) and disclosure quality indicates that

firms with growth opportunities in the extractive industries tend to disclose

lower quality derivative information. These firms tend to be more secretive as

providing more information could endanger their competitive advantage.

Further, disclosing more information may cause them to incur higher costs of

accumulating information. The significance of performance and growth

opportunities are not consistent in other regression techniques (ranked

regression, year-by-year and average of four years’ data (Appendix B)).

9.1.2 Market Value Model

The second part of this study examines the association between disclosure quality and

market value, and the association between market value and specific disclosures,

namely hedges of anticipated transactions, net fair value information and risk

information. The first market value model examines the association between

transparency or disclosure quality of derivative information and the market value of

firms in the extractive industries. This study develops the models based on Ohlson

(1995). The Ohlson model is modified to include transparency (as measured by the

disclosure index). This is represented by equation 6.3. However, including the

measure of transparency alone may override the importance of each component of the


200

disclosure index. Therefore, the model is expanded to include three components of the

disclosure index. These are hedges of anticipated transactions, net fair value

information and risk information. The resultant model is specified in equation 6.4.

The second market value model examines whether: a) qualitative information (hedges

of anticipated transactions) and quantitative information are valued differently by

market participants, and b) net fair value and the unrealised gain or loss on financial

instruments are value relevant and provide incremental explanatory power above

historical cost. To examine the first issue, both qualitative and quantitative

information required by paragraph 5.8 of AASB 1033 are included in the expanded

Ohlson model. The qualitative information is represented by the component score of

hedge information and the quantitative information is represented by the unrealised

gain or loss on financial instruments (URGL) and off-balance sheet derivative

financial instruments (OBDI). The resultant model was specified in equation 6.6.

Prior studies on the value relevance of the fair value of financial instruments have

provided inconclusive results. To examine the value relevance of net fair value and

the unrealised gain or loss on financial instruments, both historical cost and net fair

value information were included in the expanded Ohlson model. Further, the book

value and the net fair value of non-financial instruments are separated from the book

value and the net fair value of financial instruments. Also included in the model are

the off-balance sheet derivative financial instruments (OBDI). This was specified in

equations 6.9 and 6.10. To examine the value relevance and incremental explanatory

power of the unrealised gain or loss on financial instruments (URGL), the URGL was

separated into the URGL of financial assets (DIFFA) and the URGL of financial

liabilities (DIFFL). This was specified in equations 6.11 and 6.12.


201

The results of these estimations support the following conclusions:

i. High quality disclosure (transparency) benefits the share market (equation 6.3). In

particular, Australian investors value derivative disclosures as an important factor

in determining the market value of firms in the extractive industries. The market

participants consider net fair value information as less value relevant than hedge

information and risk information (equation 6.4).

ii. The hedge information required by paragraph 5.8 is as important as recognised

items (equation 6.6). The results reveal that hedge information is negatively

related to market value, indicating that the more hedge information disclosed in

the notes to the financial statements the less relevant it is for market valuation.

The unrealised gain or loss on financial instruments and off-balance sheet

financial instruments are not significant for the pooled data, however both are

significant in one of the years in the year-by-year analysis (Table C 2 Appendix

C). This indicates that market participants depend more on the recognised items

than on disclosed items for decisions on market valuation.

iii. To a limited extent, subject to the constraint of the model specification and sample

data, the net fair value of financial instruments, the unrealised gain or loss on

financial liabilities and off-balance sheet derivative financial instruments are value

relevant (Appendix C).


202

iv. The incremental explanatory power of net fair value and unrealised gain or loss on

financial instruments beyond the book value of financial and non-financial

instruments and earnings at historical cost is very low. However, the incremental

explanatory power increased from 1998 to 2001, as compared to the incremental

explanatory power of the book value of financial and non-financial instruments

and earnings at historical cost beyond net fair value and unrealised gain or loss on

financial instruments (Tables 8.10 and 8.11 in Chapter 8).

9.2 Contributions of the Study The evidence presented in this study contributes to the literature in several ways. i. There is limited research that relates disclosure quality of derivative information

with firm characteristics. Berkman et al. (2002), Nguyen and Faff (2002), Nance

et al. (1993), Smith and Stulz (1985) and Géczy, Minton and Schrand (1997)

examine the relation between the use of derivatives and firm characteristics.

Hancock (1994), Berkman et al. (1997) and Chalmers (2001) examine the

reporting practices and the quality of voluntary derivative disclosure prior to

AASB 1033. However, no prior studies have identified the characteristics

associated with the disclosure of derivative information. Evidence from this study

has contributed to the literature in this area. The results indicate a positive

association between disclosure quality and the measures of size, leverage, price-

earnings ratio, profitability and market-to-book ratio and these results are

consistent with research on other disclosures.


203

ii. The findings of this study also contribute to the value relevance literature. The

existing value relevance studies focus more on the effect of derivative accounting

numbers on the value of banks and financial institutions. Results from the finance

industry may not be relevant to other industries and therefore, this study extends

the existing findings to firms in the extractive industries.

iii. Findings from this study are relevant to standard setters and regulators for future

directions in developing accounting standards related to net fair value. In

particular this research is of relevance when the AASB is re-examining the

standard and considering the adoption of the International Accounting Standards.

Results from this study provide important information on this complex area for

both Australian and international standard setters.

9.3 Limitations

The research evidence presented in this thesis is subject to several limitations.

i. The findings could be biased as the sample is based on those companies

included in the Connect 4 Annual Report Collection Database or those

responding to a request for annual reports.

ii. The sample of firms using derivatives is relatively small and this may have

limited the power of statistical tests. Lack of variability in independent variables

such as type of auditor may also have contributed to the insignificant findings.


204

iii. The study looks specifically at derivative disclosures by firms in the extractive

industries which limits the generalisability of the results of this study to a broad

class of information and cross-section of firms. While there are limitations to the

evidence presented, it does support the findings of previous research indicating a

direct association between disclosure quality and firm characteristics.

iv. The disclosure index, which represents disclosure quality or transparency, is

developed based on the information disclosed in the annual reports. However,

the annual reports are not the only source of corporate reporting. Investors and

creditors do consider other media, such as quarterly reports and public releases

or discussions, in making decisions (Healy and Palepu, 2001). However, the

annual report is important as the information used in the disclosure index has

been audited. Therefore, these derivative disclosures have a measure of

credibility not afforded to other forms of organisational communication (Neu,

Warsame and Pedwell, 1998).

v. The disclosure index has been criticised because its usefulness depends on the

items selected for inclusion. Further, assigning a zero score to a company that

does not possess the instruments could give a wrong indication about corporate

disclosure practice. This is because the index fails to differentiate between firms

which do not hold the instruments with firms that hold the instruments but fail to

disclose the information. To overcome this, the index score is converted by

dividing the actual score by the maximum score possible for that particular

company (Marston and Shrives, 1991).


205

9.4 Directions for Future Research

i. This study should be extended to other industries to provide regulators with a

clear picture of how Australian firms react to the AASB 1033 disclosure

requirements and how these requirements help investors in decisions-making.

This will assist them to overcome issues related to measurement and

recognition of derivative instruments. Further, results presented by prior non-

Australian studies may not be applicable to Australian firms as the industries

operate in a different institutional environment.

ii. Future research may extend the current study using different research

methods. The capital markets research approach may not provide the actual

reactions of the people involved with these particular issues. Interviewing

managers, investors and auditors may help us understand the level of

acceptance of net fair value among those people. Further, this will help us

understand the importance of disclosure transparency among these stakeholder

groups.

Appendix A

206

APPENDIX A: AUSTRALIAN FIRMS IN THE EXTRACTIVE INDUSTRIES LISTED ON THE ASX IN 1998 TO 2001

Appendix A

207

Table A 1: Listed Australian Firms in the Extractive Industries

No Company name ASX Code Industry classification

1 ACCLAIM EXPLORATION NL AEX GOLD PRODUCER 2 ADELAIDE BRIGHTON ABC CEMENT 3 ADELAIDE RESOURCES LTD AND GOLD EXPLORER 4 ADEX HOLDINGS LTD AXH GOLD EXPLORER 5 ADMIRALTY RESOURCES NL ADR GOLD PRODUCER 6 AFMINEX LIMITED AFM MINERAL EXPLORER 7 AGD MINING LIMITED AGZ GOLD EXPLORER 8 AKD LIMITED AKD DIAMONDS 9 ALCASTON MINING NL AMG GOLD EXPLORER 10 ALKANE EXPLORATION LTD ALK MINING EXPLORER 11 ALLEGIANCE MINING NL AGM MINING EXPLORER 12 ALLIANCE ENERGY LTD AGS GOLD EXPLORER 13 ALLIED MINING & PROCESSING LTD AMS GOLD EXPLORER 14 ALLSTATE EXPLORATIONS NL ALX GOLD EXPLORER 15 AMADEUS PETROLEUM NL AMU OIL/GAS PRODUCER 16 AMALG RESOURCES NL ARC GOLD, COOPER 17 AMITY OIL LTD AYO OIL/GAS PRODUCER 18 AMMTEC LTD AEC MINING SERVICES 19 ANACONDA NICKEL LTD ANL BASE METALS 20 ANGLO AUSTRALIAN RESOURCES NL AAR GOLD EXPLORER 21 ANTAEUS ENERGY LTD AEL COAL 22 ANVIL MINING NL AVL MINING EXPLORER 23 ANZOIL NL AZL OIL/GAS EXPLORER 24 APOLLO GROUP LTD APX GOLD EXPLORER 25 AQUARIUS PLATINUM LTD AQP MINING EXPLORER 26 AQUILA RESOURCES LTD AQA GOLD EXPLORER 27 ARC ENERGY NL ARQ OIL/GAS EXPLORER 28 ARGOSY MINERALS INC AGY MINING EXPLORER 29 ARROW ENERGY NL AOE OIL/GAS EXPLORER 30 ASHBURTON MINERALS LTD ATN MINING EXPLORER 31 ASTRO MINING NL ARO DIAMONDS 32 AUDAX RESOURCES LTD ADX GOLD EXPLORER 33 AUIRON ENERGY LTD AUY COAL 34 AURIDIAM LTD ADM DIAMONDS 35 AURORA GOLD LTD AUG GOLD, OTHER MINING 36 AUSDRILL LTD ASL MINING SERVICES 37 AUSTINDO RESOURCES CORP NL ARX MINING EXPLORER 38 AUSTMINEX NL ATX BASE METALS 39 AUSTPAC RESOURCES NL APG MINERAL SANDS 40 AUSTRAL COAL LTD AUO COAL

Appendix A

208


41 AUSTRALASIAN GOLD MINES NL ATE MINING EXPLORER 42 AUSTRALIAN MAGNESIUM CORP LTD ANM BASE METALS 43 AUSTRALIAN MINING INVESTMENTS LTD AUM COAL 44 AUSTRALIAN OIL & GAS CORP LTD AOG MINING SERVICES 45 AUSTRALIAN OVERSEAS RES. LTD AOV BASE METALS 46 AUSTRALIAN UNITED GOLD NL AUL GOLD EXPLORER 47 AUSTRALIAN WORLDWIDE EXP. LTD AWE OIL/GAS EXPLORER 48 AZTEC RESOURCES LTD AZR URANIUM 49 BALLARAT GOLDFIELDS NL BGF GOLD EXPLORER 50 BARRA RESOURCES LTD BAR GOLD EXPLORER 51 BASIN MINERALS LTD BMS MINERAL SANDS 52 BASS STRAIT OIL TRUST BSO OIL/GAS INVESTOR 53 BEACH PETROLEUM NL BPT OIL/GAS PRODUCER 54 BEACONSFIELD GOLD NL BCD GOLD EXPLORER 55 BEMAX RESOURCES NL BMX MINING EXPLORER 56 BENDIGO MINING NL BDG GOLD EXPLORER 57 BHP BILLITON LTD BHP OIL, STEEL, MINING 58 BLACK RANGE MINERALS LTD BLR BASE METALS 59 BLIGH OIL & MINERALS NL BLO OIL/GAS PRODUCER 60 BOLNISI GOLD NL BSG GOLD PRODUCER 61 BOULDER STEEL LTD BGD GOLD EXPLORER 62 BRANDRILL LTD BDL MINING SERVICES 63 BUKA MINERALS LTD BKA BASE METALS 64 BULLION MINERALS LTD BLN MINING EXPLORER 65 BURDEKIN PACIFIC LTD BKS GOLD EXPLORER 66 CABLE & TELECOMS LTD CTZ DIAMONDS 67 CALTEX AUSTRALIA LTD CTX OIL/GAS INVESTOR 68 CAPRAL ALUMINIUM LTD CAA DIVERSIFIED RESOURCES69 CARBON MINERALS NL CRM GOLD EXPLORER 70 CARDIA TECHNOLOGIES LTD CNN GOLD EXPLORER 71 CARNARVON PETROLEUM NL CVN OIL/GAS PRODUCER 72 CARNEGIE CORP LTD CNM MINING EXPLORER 73 CARPATHIAN RESOURCES LTD CPN OIL/GAS EXPLORER 74 CARPENTER PACIFIC RES NL CPC GOLD EXPLORER 75 CCI HOLDINGS LTD CHL MINING SERVICES 76 CENTAMIN EGYPT LTD CNT GOLD EXPLORER 77 CENTAUR MINING & EXPL LTD CTR GOLD EXPLORER 78 CENTENNIAL COAL CO LTD CEY COAL 79 CENTRAL KALGOORLIE GOLD MINES LTD CKG GOLD EXPLORER 80 CENTRAL NORSEMAN GOLD CORP LTD CNG GOLD PRODUCER 81 CENTRAL PACIFIC MINERALS NL CPM OIL/GAS PRODUCER 82 CENTRAL WEST GOLD NL CWG GOLD EXPLORER 83 CHARTES TOWERS GOLD MINES LTD CTO GOLD EXPLORER 84 CIM RESOURCES LTD CIM COAL 85 CITYVIEW CORP LTD CVI OIL/GAS EXPLORER

Appendix A

209


86 CLUFF RESOURCES PACIFIC NL CFR MINING EXPLORER 87 COAL & ALLIED INDUSTRIES LTD CAN COAL 88 COBRA RESOURCES LTD CBO GOLD EXPLORER 89 COMET RESOURCES LTD CRL MINING EXPLORER 90 COMPASS RESOURCES NL CMR MINING EXPLORER 91 CONQUEST MINING LTD CQT DIAMONDS 92 CONSOLIDATED BROKEN HILL LTD CBH MINING PRODUCER 93 CONSOLIDATED MINERALS LTD CSM MINING PRODUCER 94 CONSOLIDATED RUTILE LTD CRT MINERAL SANDS 95 CONTINENTAL GOLDFIELDS LTD CVC GOLD EXPLORER 96 COPLEX RESOURCES NL CXR OIL/GAS EXPLORER 97 CROESUS MINING NL CRS GOLD PRODUCER 98 CUE ENERGY RESOURCES LTD CUE OIL/GAS EXPLORER 99 CULLEN RESOURCES LTD CUL GOLD EXPLORER 100 CUMNOCK COAL LTD CMK COAL 101 DALRYMPLE RESOURCES NL DRE GOLD EXPLORER 102 DANAE RESOURCES NL DNS MINING EXPLORER 103 DEEPGREEN MINERALS CORP LTD DGM COAL 104 DELTA GOLD LTD DGD GOLD PRODUCER 105 DIAMOND ROSE NL DRN DIAMONDS 106 DIAMONDS VENTURES NL DDV DIAMONDS 107 DIORO EXPLORATION NL DIO MINING INVESTMENT 108 DOMINION MINING LTD DOM GOLD PRODUCER 109 DRAGON MINING NL DRA GOLD EXPLORER 110 DRILLSEARCH ENERGY LTD DLS OIL/GAS PRODUCER 111 DURBAN ROODEPOORT DEEP LTD DRD GOLD PRODUCER 112 DWYKA DIAMONDS LTD DWY DIAMONDS 113 EAGLE BAY RESOURCES NL EBR OIL/GAS EXPLORER 114 EAST COAST MINERALS NL ECM MINING INVESTMENT 115 EASTERN CORPORATION LTD ECU GOLD EXPLORER 116 EASTERN INTERNATIONAL LTD ESG OIL/GAS EXPLORER 117 EMPEROR MINES LTD EMP GOLD PRODUCER 118 EMPIRE OIL & GAS NL EGO OIL/GAS EXPLORER 119 ENERGY EQUITY CORP LTD EEC OIL/GAS EXPLORER 120 ENERGY RESOURCES OF AUSTRALIA LTD ERA URANIUM 121 EQITX LTD EQX MINING EXPLORER 122 EQUATORIAL MINING LTD EQM MINING EXPLORER 123 EQUIGOLD NL EQI GOLD EXPLORER 124 EQUINOX RESOURCES LTD EQR GOLD EXPLORER 125 EQUS LTD EQS MINING EXPLORER 126 EROMANGA HYDROCARBONS NL ERH OIL/GAS EXPLORER 127 ESMERALDA EXPLORATION LTD ESE GOLD EXPLORER 128 ESPERANCE MINERALS NL ESM MINING PRODUCER 129 ESSENTIAL PETROLEUM RES LTD EPR OIL/GAS EXPLORER 130 EXCO RESOURCES NL EXS MINING EXPLORER

Appendix A

210


131 FIRST AUSTRALIAN RES LTD FAR OIL/GAS EXPLORER 132 FRASER RANGE HOLDING LTD FGR BASE METALS 133 GALLERY GOLD LTD GGN GOLD EXPLORER 134 GATEWAY MINING NL GML MINING EXPLORER 135 GENERAL GOLD RESOURCES NL GGR MINERAL EXPLORER 136 GEORGRAPHE RESOURCES LTD GHR GOLD EXPLORER 137 GIANTS REEF MINING LTD GTM GOLD, OTHER MINING 138 GINDALBIE GOLD NL GBG GOLD EXPLORER 139 GIRALIA RESOURCES NL GIR GOLD EXPLORER 140 GLENGARRY RESOURCES LTD GGY GOLD EXPLORER 141 GME RESOURCES LTD GME GOLD EXPLORER 142 GOLD MINES OF SARDINIA LTD GMS GOLD EXPLORER 143 GOLD PARTNERS LTD GLP GOLD EXPLORER 144 GOLDEN CROSS RESOURCES LTD GCR GOLD EXPLORER 145 GOLDEN DEEPS NL GED GOLD EXPLORER 146 GOLDEN STATE RESOURCES LTD GDN GOLD EXPLORER 147 GOLDEN VALLEY MINES LTD GVM GOLD PRODUCER 148 GOLDFIELDS LTD GLD GOLD PRODUCER 149 GOLDSEARCH LTD GSE GOLD EXPLORER 150 GOLDSTREAM MINING NL GDM GOLD EXPLORER 151 GONDWANA RESOURCES LTD GDA GOLD EXPLORER 152 GRD NL GRD GOLD, INVESTMENT 153 GREATER PACIFIC GOLD LTD GPN GOLD EXPLORER 154 GREENSTONE RESOURCES NL GNR MINING EXPLORER 155 GREENVALE MINING NL GRV BASE METALS 156 GRENFELL RESOURCES LTD GRN MINERAL SANDS 157 GTN RESOURCES LTD GTN MINING PRODUCER 158 GULLEWA LTD GUL GOLD EXPLORER 159 GUTNICK RESOURCES NL GKR GOLD EXPLORER 160 GYMPIE GOLD LTD GYM GOLD PRODUCER 161 HADDINGTON INTERNATIONAL RES LTD HDN MINING EXPLORER 162 HALLMARK CONSOLIDATED LTD HLM GOLD EXPLORER 163 HAMPTON HILL MINING NL HHM GOLD EXPLORER 164 HAOMA MINING NL HAO GOLD PRODUCER 165 HARDMAN RESOURCES NL HDR OIL/GAS EXPLORER 166 HELIX RESOURCES LTD HLX MINING EXPLORER 167 HERALD RESOURCES LTD HER GOLD EXPLORER 168 HERON RESOURCES LTD HRR BASE METALS 169 HILL 50 GOLD NL HFY GOLD PRODUCER 170 HILLCREST RESOURCES LTD HLL GOLD EXPLORER 171 HILLGROVE GOLD NL HGO MINING PRODUCER 172 HOMESTAKE MINING CO HSM GOLD PRODUCER 173 HUDSON RESOURCES LTD HRS MINING PRODUCER 174 ICON ENERGY LTD ICN OIL/GAS EXPLORER 175 IGM GROUP LTD IGM GOLD EXPLORER

Appendix A

211


176 ILUKA RESOURCES LTD ILU MINERAL SANDS 177 IMPERIAL ONE LTD IMP GOLD EXPLORER 178 INDCOR LTD ICO MINING EXPLORER 179 INTERMIN RESOURCES LTD IRC GOLD EXPLORER 180 ITEMUS INC ITM GOLD EXPLORER 181 JERVOIS MINING NL JRV OIL/GAS EXPLORER 182 JOHNSON’S WELL MINING NL JWM GOLD EXPLORER 183 JUBILEE MINES NL JBM MINING EXPLORER 184 JULIA CORPORATION LTD JLA GOLD EXPLORER 185 KAGARA ZINC LTD KZL BASE METALS 186 KALREZ ENERGY LTD KRZ OIL/GAS PRODUCER 187 KANOWNA CONSOL GOLD MINES LTD KCG GOLD EXPLORER 188 KANOWNA LIGHTS LTD KLS GOLD EXPLORER 189 KIDSTON GOLD MINES LTD KGM GOLD PRODUCER 190 KIMBERLY DIAMOND CO LTD KIM DIAMONDS 191 KIMBERLY OIL NL KBO OIL/GAS PRODUCER 192 KINGS MINERALS NL KMN GOLD EXPLORER 193 KINGSGATE CONS NL KCN GOLD EXPLORER 194 LAFAYETTE MINING LTD LAF GOLD EXPLORER 195 LAKE RESOURCES NL LKE MINING EXPLORER 196 LAKES OIL NL LKO OIL/GAS EXPLORER 197 LAVERTON GOLD NL LVG GOLD EXPLORER 198 LEGEND MINING LTD LEG MINING EXPLORER 199 LEYSHON RESOURCES LTD LRL GOLD PRODUCER 200 LIMAX MINING LTD CMX GOLD PRODUCER 201 LION SELECTION GROUP LTD LSG GOLD EXPLORER 202 LIONORE AUSTRALIA (NICKEL) LTD LON BASE METALS 203 LONGREACH OIL LTD LGO OIL/GAS EXPLORER 204 LYNAS CORP LTD LYC GOLD PRODUCER 205 MACMIN LTD MMN MINING EXPLORER 206 MAGELLAN PETROLEUM AUSTRALIA LTD MAG OIL/GAS PRODUCER 207 MAGNUM GOLD NL MGU GOLD EXPLORER 208 MAJESTIC RESOURCES NL MJN MINING EXPLORER 209 MARLBOROUGH RESOURCES NL MBG GOLD EXPLORER 210 MARYMIA EXPLORATION NL MEN GOLD EXPLORER 211 MATRIX METALS LTD MRX BASE METALS 212 MENZIES GOLD LTD MZG GOLD EXPLORER 213 METEX RESOURCES LTD MEE GOLD EXPLORER 214 METHANOL AUSTRALIA LTD MEO OIL/GAS PRODUCER 215 MICHELAGO LTD MIC GOLD EXPLORER 216 MIM HOLDINGS LTD MIM DIVERSIFIED MINING 217 MINCOR RESOURCES NL MCR BASE METALS 218 MINERAL COMMODITIES LTD MRC MINING EXPLORER 219 MINERAL DEPOSITS LTD MDL MINERAL SANDS 220 MINERALS CORP LTD MSC BASE METALS

Appendix A

212


221 MINOTAUR RESOURCES LTD MNR MINING EXPLORER 222 MOLOPO AUSTRALIA NL MPO OIL/GAS EXPLORER 223 MONTO MINERALS NL MOO MINERAL SANDS 224 MOSAIC OIL NL MOS OIL/GAS EXPLORER 225 MOUNT BURGESS MINING NL MTB GOLD EXPLORER 226 MOUNT CONQUEROR MINERALS NL MCO GOLD EXPLORER 227 MT GRACE RESOURCES NL MGD GOLD EXPLORER 228 MURCHISON UNITED NL MUR BASE METALS 229 NAMAKWA DIAMOND CO LTD NDC DIAMONDS 230 NEW HOLLAND MINING LTD NHM GOLD EXPLORER 231 NEWCREST MINING LTD NCM GOLD PRODUCER 232 NEWLAND RESOURCES LTD NRL GOLD EXPLORER 233 NEXUS ENERGY LTD NXS OIL/GAS EXPLORER 234 NIDO PETROLEUM LTD NDO OIL/GAS EXPLORER 235 NORMANDY MINING LTD NDY GOLD PRODUCER 236 NORMANDY NFM LTD NFM GOLD PRODUCER 237 NORMANDY YANDAL OPERATIONS LTD NYY GOLD PRODUCER 238 NORTHERN GOLD NL NNG GOLD EXPLORER 239 NORWEST ENERGY NL NEW OIL/GAS EXPLORER 240 NOVUS PETROLEUM LTD NVS OIL/GAS PRODUCER 241 NUGOLD HILL MINES LTD NGH GOLD EXPLORER 242 NULLARBOR HOLDINGS LTD NLB GOLD EXPLORER 243 OIL COMPANY OF AUSTRALIA LTD OCA OIL/GAS PRODUCER 244 OIL SEARCH LTD OSH OIL/GAS PRODUCER 245 OMEGA OIL NL OMO OIL/GAS EXPLORER 246 OROPA LTD ORP GOLD EXPLORER 247 OXIANA RESOURCES NL OXR MINING EXPLORER 248 PACIFIC MAGNESIUM CORP LTD PMH GOLD EXPLORER 249 PACIFIC MINING LTD PFM MINERAL SANDS 250 PACRIM ENERGY LTD PRE OIL/GAS EXPLORER 251 PALADIN RESOURCES LTD PDN DIVERSIFIED RESOURCES252 PAN AUSTRALIAN RESOURCES NL PNA GOLD EXPLORER 253 PAN PACIFIC PETROLEUM NL PPP OIL/GAS PRODUCER 254 PANCONTINENTAL OIL & GAS NL PCL OIL/GAS EXPLORER 255 PASMINCO LTD PAS BASE METALS 256 PELSART RESOURCES NL PRN GOLD EXPLORER 257 PERILYA LTD PEM GOLD EXPLORER 258 PERSEVERANCE CORP LTD PSV GOLD PRODUCER 259 PETROZ NL PTZ OIL/GAS PRODUCER 260 PETSEC ENERGY LTD PSA OIL/GAS PRODUCER 261 PILBARA MINES LTD PIL MINING EXPLORER 262 PIMA MINING NL PAL MINING EXPLORER 263 PLATSEARCH NL PTS MINING EXPLORER 264 PLENTY RIVER CORP LTD PRM BASE METALS 265 PORTMAN LTD PMM DIVERSIFIED MINING

Appendix A

213


266 POWERISE TECHNOLOGY LTD PTC GOLD EXPLORER 267 PRECIOUS METALS AUSTRALIA LTD PMA MINING EXPLORER 268 PRESTON RESOURCES LTD PSR MINING EXPLORER 269 QUEENSLAND GAS COMPANY LTD QGC OIL/GAS EXPLORER 270 QUEENSLAND OPALS NL QOP MINING EXPLORER 271 RAMBORA TECHNOLOGIES LTD RBT GOLD EXPLORER 272 RAND MINING NL RND GOLD EXPLORER 273 RANGE RESOURCES LTD RRS GOLD PRODUCER 274 RANGER MINERALS LTD RGS GOLD PRODUCER 275 RED BACK MINING NL RBK GOLD EXPLORER 276 REEFTON MINING NL RTM GOLD EXPLORER 277 RELODE LTD RLD MINING EXPLORER 278 RENEWABLE INVESTMENTS LTD RIL GOLD EXPLORER 279 RESOLUTE MINING LTD RSG GOLD PRODUCER 280 RIMFIRE PACIFIC MINING NL RIM MINING EXPLORER 281 ROC OIL COMPANY LTD ROC OIL/GAS PRODUCER 282 ROMA PETROLEUM NL RPM OIL/GAS EXPLORER 283 RUSINA MINING LTD RML MINING INVESTMENT 284 SANTOS LTD STO OIL/GAS PRODUCER 285 SABRE RESOURCES LTD SBR GOLD EXPLORER 286 SALLY MALAY MINING LTD SMY BASE METALS 287 SAMSON EXPLORATION NL SSN GOLD EXPLORER 288 SAPPHIRE MINES NL SHM MINING PRODUCER 289 SARACEN MINERAL HOLDINGS LTD SAR EQUITY INVESTOR 290 SCANTECH LTD SCD MINING SERVICES 291 SDS CORP LTD SDS MINING SERVICES 292 SEDIMENTARY HOLDINGS LTD SED GOLD PRODUCER 293 SELWYN MINES LTD SLN MINING PRODUCER 294 SIPA RESOURCES INTERNATIONAL NL SRI GOLD EXPLORER 295 SIROCCO RESOURCES NL SRO GOLD EXPLORER 296 SMARTRANS HOLDINGS LTD SMA GOLD EXPLORER 297 SMC GOLD LIMITED SMO GOLD PRODUCER 298 SONS OF GWALIA LTD SGW GOLD PRODUCER 299 SOUTHERN CROSS EXPLORATION NL SCX OIL/GAS EXPLORER 300 SOUTHERN PACIFIC PETROLEUM NL SPP OIL/GAS EXPLORER 301 SOUTHERN TITANIUM NL STN GOLD PRODUCER 302 SPINIFEX GOLD LTD SPX GOLD EXPLORER 303 ST BARBARA MINES LTD SBM GOLD PRODUCER 304 ST FRANCIS GROUP LTD SFG MINING EXPLORER 305 STAR MINING CORP NL SCN GOLD EXPLORER 306 STRATA MINING CORP NL STT GOLD EXPLORER 307 STRATEGIC MINERALS CORP NL SMC GOLD EXPLORER 308 STUART PETROLEUM NL STU OIL/GAS EXPLORER 309 SUB-SAHARA RESOURCES NL SBS GOLD EXPLORER 310 SUMMIT RESOURCES LTD SMM MINING EXPLORER

Appendix A

214


311 SUN RESOURCES NL SUR OIL/GAS EXPLORER 312 SURFBOARD LTD SBD MINING PRODUCER 313 SYDNEY GAS CO NL SGC OIL/GAS EXPLORER 314 SYLVANIA RESOURCES LTD SLV MINING EXPLORER 315 SYNERGY METALS LTD SML MINING EXPLORER 316 TAIPAN RESOURCES NL TAI GOLD EXPLORER 317 TAKORADI LTD TKG GOLD EXPLORER 318 TALON RESOURCES NL TLN GOLD, OTHER MINING 319 TANAMI GOLD NL TAM GOLD EXPLORER 320 TAP OIL LTD TAP OIL/GAS PRODUCER 321 TASMANIA MINES LTD TMM MINERAL SANDS 322 TAWANA RESOURCES NL TAW DIAMONDS 323 TECTRONIC RESOURCES NL TTR GOLD PRODUCER 324 THUNDERLARRA EXPLORATION LTD THX MINING EXPLORER 325 TICOR LTD TOR MINERAL SANDS 326 TIGER RESOURCES NL TGS GOLD EXPLORER 327 TITAN RESOURCES NL TIR MINING PRODUCER 328 TOX FREE SOLUTIONS LTD TOX MINING SERVICES 329 TRIAKO RESOURCES LTD TKR MINING PRODUCER 330 TRIBUNE RESOURCES NL TBR MINING EXPLORER 331 TROY RESOURCES NL TRY GOLD EXPLORER 332 VICTORIA PETROLEUM NL VPE OIL/GAS EXPLORER 333 WEDGETAIL EXPLORATION NL WTE MINING PRODUCER 334 WERRIE GOLD LTD WER GOLD EXPLORER 335 WEST AUSTRALIAN METALS NL WME GOLD EXPLORER 336 WEST MUSHGRAVE MINING LTD WMM BASE METALS 337 WEST OIL NL WON OIL/GAS EXPLORER 338 WESTERN AREAS NL WSA MINING EXPLORER 339 WESTERN AUSTRALIAN DIAMOND TRUST WAD DIAMONDS 340 WESTERN METALS LTD WMT BASE METALS 341 WESTGOLD RESOURCES NL WGR GOLD EXPLORER 342 WHITTLE TECHNOLOGY LTD WTL MINING SERVICES 343 WOODSIDE PETROLEUM LTD WPL OIL/GAS PRODUCER 344 XENOLITH GOLD LTD XEN GOLD PRODUCER 345 YAMARNA GOLDFIELDS LTD YAM GOLD EXPLORER 346 YARDARINO LTD YDR MINERAL SANDS

Appendix A

215

Table A 2: List of Data Firms in the Study


1 ADELAIDE RESOURCES LTD AND GOLD EXPLORER 2 AMADEUS PETROLEUM NL AMU OIL/GAS PRODUCER 3 AMALG RESOURCES NL ARC GOLD, COOPER 4 ANACONDA NICKEL LTD ANL BASE METALS 5 ANZOIL NL AZL OIL/GAS EXPLORER 6 ARC ENERGY NL ARQ OIL/GAS EXPLORER 7 AURORA GOLD LTD AUG GOLD, OTHER MINING 8 AUSTRAL COAL LTD AUO COAL 9 BEACH PETROLEUM NL BPT OIL/GAS PRODUCER 10 BHP BILLITON LTD BHP OIL, STEEL, MINING 11 BLIGH OIL & MINERALS NL BLO OIL/GAS PRODUCER 12 BOLNISI GOLD NL BSG GOLD PRODUCER 13 CALTEX AUSTRALIA LTD CTX OIL/GAS INVESTOR 14 CAPRAL ALUMINIUM LTD CAA DIVERSIFIED RESOURCES 15 CENTENNIAL COAL CO LTD CEY COAL 16 COAL & ALLIED INDUSTRIES LTD CAN COAL 17 CONSOLIDATED RUTILE LTD CRT MINERAL SANDS 18 CUMNOCK COAL LTD CMK COAL 19 DELTA GOLD LTD DGD GOLD PRODUCER 20 EMPEROR MINES LTD EMP GOLD PRODUCER 21 ENERGY EQUITY CORP LTD EEC OIL/GAS EXPLORER

22 ENERGY RESOURCES OF AUSTRALIA LTD ERA URANIUM

23 EQUATORIAL MINING LTD EQM MINING EXPLORER 24 EQUIGOLD NL EQI GOLD EXPLORER 25 GIANTS REEF MINING LTD GTM GOLD, OTHER MINING 26 GINDALBIE GOLD NL GBG GOLD EXPLORER 27 GOLD MINES OF SARDINIA LTD GMS GOLD EXPLORER 28 GOLDFIELDS LTD GLD GOLD PRODUCER 29 GYMPIE GOLD LTD GYM GOLD PRODUCER 30 HAOMA MINING NL HAO GOLD PRODUCER 31 HILL 50 GOLD NL HFY GOLD PRODUCER 32 ILUKA RESOURCES LTD ILU MINERAL SANDS 33 JUBILEE MINES NL JBM MINING EXPLORER 34 KALREZ ENERGY LTD KRZ OIL/GAS PRODUCER 35 KIDSTON GOLD MINES LTD KGM GOLD PRODUCER 36 KINGSGATE CONS NL KCN GOLD EXPLORER 37 LEYSHON RESOURCES LTD LRL GOLD PRODUCER 38 MAJESTIC RESOURCES NL MJN MINING EXPLORER 39 MARLBOROUGH RESOURCES NL MBG GOLD EXPLORER 40 MIM HOLDINGS LTD MIM DIVERSIFIED MINING

Appendix A

216


41 MINERAL DEPOSITS LTD MDL MINERAL SANDS 42 MOSAIC OIL NL MOS OIL/GAS EXPLORER 43 MURCHISON UNITED NL MUR BASE METALS 44 NEWCREST MINING LTD NCM GOLD PRODUCER 45 NORMANDY MINING LTD NDY GOLD PRODUCER 46 NORMANDY NFM LTD NFM GOLD PRODUCER 47 NORTHERN GOLD NL NNG GOLD EXPLORER 48 OIL COMPANY OF AUSTRALIA LTD OCA OIL/GAS PRODUCER 49 PERILYA LTD PEM GOLD EXPLORER 50 PERSEVERANCE CORP LTD PSV GOLD PRODUCER 51 PORTMAN LTD PMM DIVERSIFIED MINING 52 RANGER MINERALS LTD RGS GOLD PRODUCER 53 SANTOS LTD STO OIL/GAS PRODUCER 54 SIPA RESOURCES INTERNATIONAL NL SRI GOLD EXPLORER 55 SIROCCO RESOURCES NL SRO GOLD EXPLORER 56 SONS OF GWALIA LTD SGW GOLD PRODUCER 57 ST BARBARA MINES LTD SBM GOLD PRODUCER 58 SURFBOARD LTD SBD MINING PRODUCER 59 TAP OIL LTD TAP OIL/GAS PRODUCER 60 TECTRONIC RESOURCES NL TTR GOLD PRODUCER 61 TICOR LTD TOR MINERAL SANDS 62 TITAN RESOURCES NL TIR MINING PRODUCER 63 TRIAKO RESOURCES LTD TKR MINING PRODUCER 64 WESTERN METALS LTD WMT BASE METALS 65 WOODSIDE PETROLEUM LTD WPL OIL/GAS PRODUCER

Appendix A

217

Table A 3: Components of Derivative Disclosure Index for BHP Billiton (2001) Possible

score BHP Score

Total Score

Policy Information Accounting policies and method adopted 1 1 a) Extent and nature of the underlying financial instruments, b)

including significant terms and conditions that may affect the amount, timing and uncertainty of future cash flows.

2 2

Objectives for holding or issuing derivative financial instruments 1 1 Component score of policy information 4 4 1 Hedges of Anticipated Transactions

a) A description of the anticipated transaction, b) including the period of time until they are expected to occur.

1 1

1 1

A description of the hedging instruments. 1 1 a) Amount of any deferred or unrecognised gain or loss and b) the expected timing of recognition as revenue or expense.

1 1

1 1

Component score of hedges of anticipated transactions 5 5 1 Risk Information

Contractual repricing or maturity dates for interest rate risk 1 1 Effective interest rates or weighted average 1 1 The maximum amount of credit risk exposure at reporting date 1 1

Component score of risk information 3 3 1 Net Fair Value Information

a) The aggregate net fair value as at the reporting date, b) showing separately the aggregate net fair value of those financial

assets or financial liabilities which are not readily traded on organised markets in standardised form.

1 1

1 1

The method or methods adopted in determining net fair value. 1 1 Any significant assumptions made in determining net fair value. 1 1 The carrying amount and the net fair value of either the individual

asset or appropriate groupings of those individual assets. 1 1

a) The reasons for not reducing the carrying amount, b) including the nature of the evidence that provides the basis for

management’s belief that the carrying amount will be recovered.

1 1

1 0

Component score of net fair value information 7 6 0.857143 Commodity Contracts Information

Contract for commodity gold 1 1 Component score of commodity contracts information 1 1 1

Transparency/Disclosure Quality = BHP’s Total Score/Total Possible Score = 4.857143/5 = 0.9714

Appendix B

218

APPENDIX B: FIRM CHARACTERISTICS MODEL YEAR-BY-YEAR AND AVERAGE FOUR YEARS ANALYSES

Appendix B

219

1. Year-by-Year and Average Four Years

1.1 Pooled Data

Time might influence the behaviour of all the variables and therefore might affect the

above results. The preceding analysis assumes that each firm-year can be treated as an

independent observation. However, the degree of freedom in calculating the

significance levels is overstated if the independent variables fail to remove

autocorrelation in the dependent variable (Lang and Lundholm, 1993). Therefore, the

regression analysis was repeated for each year and using four years average data.86

Table B 1 reports the regression results of the relationship between disclosure quality

and firm characteristics for 1998 to 2001 and for average data. Table B 1 indicates

that size is only significant in 2000 and 2001 at p = 0.05 and p < 0.001, respectively.

Size is also significant at p = 0.05 when the average data is used. Also significant is

profitability at p = 0.05 in 2000 and at p = 0.10 for average data. However, none of

the variables are significant in 1998 and 199987,88.

86 A similar approach was used in Lang and Lundholm (1993). 87 Except for 1998, there is no heteroscedasticity present in year-by-year and average regression analysis. The results for 1998 were based on White’s Heteroscedasticity Corrected Regression. 88 Results for the estimation without outliers are presented in Table E 2 Appendix E.

Appendix B

220

Table B 1: The Association between Firms Characteristics and Disclosure Quality on a Year-by-Year Basis and an Average of Four Years Data (n=65)



Variable Sign 1998 1999 2000 2001 Average Constant ? 0.5296(2.3080) 0.6187(2.9074) 0.5751(3.6711) 0.5054(3.2678) 0.5758(3.9533)

SIZE + 0.0163(1.2444) 0.0147(1.2726) 0.0184(2.1056)** 0.0233(2.7021)*** 0.0171(2.1197)**

PROFIT + 0.0295(0.4844) -0.0101(-0.1623) 0.0356(2.2524)** 0.0002(0.0055) 0.0831(1.9538)*

PE + 6.29E-05(1.0282) 1.10E-05 (0.2331) 0.0002(0.8821) 9.54E-06(0.0366) 2.13E-05(0.1720)

TYPE - -0.0141(-0.3410) 0.0090(0.2649) 0.0231(0.9020) 0.0018(0.0768) 0.0135(0.5870)

Audit +/- 0.0275(0.67351) -0.0308(-0.9013) -0.0363(-1.1111) -0.0346(-1.2031) -0.0156(-0.5170)

MTB + -0.0004(-1.0282) -5.26E-05(-0.0881) -0.0013(-0.9775) -4.17E-05(-0.4437) 5.68E-05(0.1619)

R&D + 0.0258(0.7399) 0.0309(0.8974) 0.0051(0.1738) -0.0318(-1.2162) -0.0010(-0.0378)

DTE +/- 0.0354(1.3490) 0.0380(1.5108) 0.0202(0.6243) 0.0089(0.3629) 0.0275(1.0640)

R2 0.3637 0.2392 0.2936 0.2482 0.3372

Adjusted R2 0.2728 0.1306 0.1927 0.1408 0.2425

F statistics 4.0010 2.2014 2.9096 2.3109 3.5608

p-value 0.0008 0.0409 0.0087 0.0323 0.0021

Durbin-Watson Stat.

1.9916 2.1151 1.960 1.6886 1.9048

Note: The italic number (in bracket) represents the t-value. ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10 respectively.

Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity i = firm t = year

Appendix B

221

1.2 Rank Transformation Procedure

Table B 2 shows the results of ranked regressions for the year-by-year and average

data. The table indicates that leverage is positively significant at p < 0.05 (for 1999,

2000 and 2001) and at p < 0.10 (1998 and average data). A departure from previous

results in Table B 1 is that the price-earnings ratio and R&D are documented as being

significantly related to transparency. The price-earnings ratio is positive and

significantly related to transparency at p < 0.05 for 1998 and at p = 0.01 for 2000 and

for the average data. However, R&D is negatively related to transparency at p =

0.0631 in 2001. Consistent with the findings presented earlier in Table B 1, size is not

significant in 1998 to 200089.

89 The model is re-estimated with the outliers excluded, and the results are consistent with the full sample for 1999 and 2001. Also documented as significant are size (p = 0.096) in 1998, and type (p = 0.10) and market-to-book ratio (p = 0.10) in 2000. Please refer to Table E 4 Appendix E.

Appendix B

222

Table B 2: Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics: Ranked Transformation Year-by-Year

Basis and an Average of Four Years Data (n=65)

RTRANSPit=α0+α1RSIZEit+α2RPROFITit+α3RPEit+α4TYPEit+α5AUDITit

+α6RMTBit+α7R&Dit +α8RDTEit+ εit

Variable Sign 1998 1999 2000 2001 Average

Constant ? -1.6311(-0.1513) 11.3338(1.0231) -0.3126(-0.0327) 12.2362(1.3574) -0.3448(-0.0401)

RSIZE + 0.3489(1.5457) 0.3441(1.3742) 0.3082(1.4997) 0.5289(2.5131)** 0.4950(2.4063)**

RPROFIT + 0.0651(0.5202) 0.0449(0.3207) 0.1007(0.7465) -0.1327(-0.8845) -0.1118(-0.8609)

RPE + 0.2538(2.2119)** 0.1295(0.9058) 0.4025(3.1028)*** 0.1262(09221) 0.3289(2.9072)***

TYPE - 4.5127(0.6987) 7.3515(1.1111) 8.4531(1.5461) -1.9163(-0.3543) 4.3767(0.8670)

Audit +/- 4.3953(0.8124) -8.4463(-1.3260) 0.2320(0.0358) -6.6545(-0.9327) -1.1674(-0.1891)

RMTB + -0.0546(-0.5281) -0.0501(-0.4307) -0.1632(-1.5469) 0.1154(1.0071) 0.0914(0.8493)

R&D + -1.7565(-0.3421) 1.2673(0.2052) -5.0549(-0.8653) -11.4372(-1.8960)* -6.4618(-1.1869)

RDTE +/- 0.2880(1.9988)* 0.3026(2.0738)** 0.3180(2.2104)** 0.2861(2.1446)** 0.2498(1.9579)*

Adjusted R2 0.3988 0.2047 0.3248 0.2804 0.3831 F statistics 6.3065 3.0590 4.8482 4.1170 5.9673 p-value 0.0000 0.0063 0.0001 0.0006 0.0000 Durbin-Watson Stat.

2.0934 2.0785 1.8453 1.8655 2.0722

Note: The italic number (in bracket) represents the t-value.


Variable Definitions: RTRANSP = rank of disclosure (transparency) RSIZE = rank of total assets (in thousands) RPROFIT = rank of profitability RPE = rank of price/earnings ratio TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise RMTB = rank of market-to-book ratio R&D = 1 for R&D firm, 0 otherwise. RDTE = rank of total liabilities divided by book value of common equity i = firm t = year

Appendix C

223

APPENDIX C: RESULTS ON MARKET VALUE MODEL FOR YEAR-BY-YEAR ANALYSIS

Appendix C

224

1. Results of the Year-By-Year Analysis

The evidence reported previously in section 8.2 in chapter 8 indicates that the

significance of derivative disclosures varies for the pooled data. Hence, equations 6.3

to 6.12 are re-estimated on a year-by-year basis. Table C 1 presents the results for

equations 6.3 and 6.4. Panel A indicates that the disclosure quality of derivative

information is positive and significant at p < 0.01 in 1998, 2000 and 2001 and at p <

0.05 in 1999. These significant results indicate that market participants consider the

quality of derivative information as an important factor in firm valuation. However,

the only component that is consistently significant for each of the four years is hedge

information. Panel B also indicates that the net fair value component is significant at p

< 0.10 in 2001. The risk component also significant at p < 0.05 in 2000 and at p <

0.01 in 2001.

Appendix C

225

Table C 1: The Association between the Information Quality of Derivatives Disclosures and the Market Value of the Firms: Year-by-Year Analysis

Variables 1998 (n=62) 1999 (n=63) 20001,2 (n=64) 20012 (n=64) Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 BV 9.60E-10

(1.8280)** 2.12E-09

(4.8955)*** 1.74E-09

(2.9314)*** 1.90E-09

(4.9908)*** E 2.22E-08

(3.3506)*** 5.18E-09

(1.5391) 1.23E-09

(0.7186) 4.50E-10

(0.2684) TRANSP 6.2074

(4.7827)*** 3.7722

(2.2127)** 6.2201

(3.2991)*** 7.7769

(3.4698)*** Constant 11.9192

(10.8048)*** 13.9640 (9.2955)***

12.0052 (7.1307)***

10.4535 (5.1927)***

Adj R2 0.6143 0.5474 0.4904 0.5200 F-statistics (Prob)

33.3831 (0.0000)

25.9926 (0.0000)

21.2115 (0.0000)

23.7502 (0.0000)

DW Stat. 2.1325 1.8881 1.8653 1.7137 Panel B : Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4CINFV,it + α5CIRisk, it+εit

BV 1.11E-09 (1.9517)*

2.05E-09 (4.8961)***

1.68E-09 (2.9489)***

1.71E-09 (3.5450)***

E 2.18E-08 (3.0286)***

4.73E-09 (1.4374)

1.07E-09 (0.6291)

7.32E-10 (0.4839)

CINFV -0.9886 (-0.2646)

-2.0255 (-0.5860)

-2.9588 (-0.6628)

-9.7456 (-1.8169)*

CIHedge 5.6407 (2.9637)***

5.7964 (2.7886)***

6.9535 (3.9751)***

9.3052 (3.7819)***

CIRisk 2.6299 (1.4782)

1.2357 (0.5573)

5.6093 (2.5844)**

8.3618 (3.2436)***

Constant 16.0884 (17.6068)***

16.5537 (18.0234)***

15.7460 (14.8271)***

15.8661 (13.5434)***

Adj R2 0.5467 0.5753 0.5005 0.5469 F-stat. (Prob)

15.7109 (0.0000)

17.7995 (0.0000)

13.6264 (0.0000)

16.2062 (0.0000)

DW Stat. 2.1016 1.8952 1.8265 1.5996 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively. Note: Number in italic represents t-statistics. 1 Results for Equation 6.3 (Panel A) are based on White-Heteroscedasticity Corrected Regression. 2 Results for Equation 6.4 (Panel B) are based on White-Heteroscedasticity Corrected Regression. Variable definitions: Pit = natural log market value of firms’ common equity measured three

months following the financial year t for firm I, BVit = book value of equity at year end t for firm I, Eit = earnings for year t available to firm i’s common shareholders, TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s

total possible disclosure scores CIHedge,it = component score of hedge information, CINFV,it = component score of net fair value, CIRisk,it = component score of risk information.

Appendix C

226

Consistent with the results presented in Table 8.7 in chapter 8, market participants

consider qualitative information (hedge information) as value relevant. Table C 2

indicates that the component score of hedge information is positive and significant at

p < 0.01 in 1998 to 2001. The book value of equity is positive and significant in all

four years. Earnings is recorded significant in 1998 to 2000. Also significant are

URGL at p < 0.05 in 1998 and OBDI at p < 0.05 in 2000. The adjusted R2 for equation

6.6 is relatively consistent in all years, within the small range of variation the highest

adjusted R2 was in 1999 and the lowest adjusted R2 was in 2000.

Table C 2: The Association between Hedge Disclosures and the Market Value of the Firms: Year-by-Year Analysis

Variables 1998 (n=62) 1999 (n=63) 20001 (n=64) 2001 (n=64)

Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4OBDIit + α5URGL it+ εit

BV 1.10E-09 (1.9718)*

1.91E-09 (4.8420)***

1.34E-09 (2.8357)***

1.66E-09 (4.0633)***

E 2.78E-08 (3.6127)***

6.17E-09 (1.7610)*

3.49E-09 (2.2104)**

1.70E-09 (0.8873)

CIHedge 6.2007 (3.6207)***

5.9147 (3.4356)***

5.5855 (3.4105)***

7.4276 (2.8001)***

URGL 1.71E-08 (2.2541)**

7.84E-09 (0.8124)

2.44E-09 (0.5582)

4.97E-09 (0.5890)

OBDI -1.26E-08 (-1.3240)

-4.45E-09 (-0.4268)

-1.42E-08 (-2.0938)**

-8.65E-09 (-0.9895)

Constant 16.2525 (56.0922)***

16.2958 (52.8491)***

16.3990 (54.4854)***

15.8307 (29.1990)***

Adj R2 0.5749 0.6278 0.5367 0.5630 F Stat (Prob)

17.5002 (0.0000)

21.9164 (0.0000)

15.5946 (0.0000)

17.2338 (0.0000)

DW Stat 2.2629 1.8951 1.9967 1.6835 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three


Appendix C

227

Table C 3 provides evidence on the value relevance of the net fair value of financial

instruments and off-balance sheet derivative financial instruments. The results are

based on the estimation without the TBFI since the variable is correlated with TFFI.

Table C 3 indicates that BVNFI is significant in all years at p < 0.001 in 1998 and

1999, and at p < 0.10 in 2000 and 2001. Also recorded as significant are earnings

(1998 and 2000), TFFI in 1998 and 1999 and OBDI in 1999 to 2001.

Table C 3:The Association between Net Fair Value and Market Value: Year-by-Year Analysis

Variables 19981 (n=62) 1999 (n=63) 2000 (n=64) 20011 (n=64) Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it +εit

BVNFI 4.02E-09 (3.6659)***

2.92E-09 (3.8317)***

1.19E-09 (1.8444)*

1.25E-09 (1.7059)*

E 1.43E-08 (1.8285)*

5.05E-09 (1.5198)

3.78E-09 (2.3254)**

2.45E-09 (1.5910)

TFFI 6.37E-09 (3.0235)***

3.82E-09 (2.6034)**

6.96E-10 (0.5111)

5.38E-10 (0.3202)

OBDI 6.05E-10 (0.0836)

2.97E-09 (1.8499)*

-1.21E-08 (-3.0834)***

-3.64E-09 (-5.4590)***

CINFV -3.9422 (-1.1234)

-3.2602 (-1.0452)

-3.4104 (-0.8656)

-5.2243 (-0.9988)

Constant 17.8207 (24.0082)***

17.8219 (26.6834)***

18.0477 (22.7052)***

18.2999 (16.9152)***


13.2081 (0.0000)

17.3840 (0.0000)

13.0652 (0.0000)

14.3580 (0.0000)

DW Stat. 2.3239 1.9930 2.0815 1.9024 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively. Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three


Appendix C

228

Table C 4 provides evidence on the value relevance of the unrealised gain or loss of

financial instruments. Table C 4 indicates that BVNFI and earnings are significant in

all four years. DIFFA and DIFFL are positive and significantly related to market

value in 1998 and 1990. However OBDI is negative and significantly related to

market value in 2000 and 2001.90.

Table C 4: The Association between the Market Value of Firms and the Difference between Net Fair Value and Book Value of Financial Instruments

(Unrealised Gain or Loss)

Variables 1998 (n=62) 1999 (n=63) 2000 (n=64) 20011 (n=64) Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit +

α6CINFV,it + εit BVNFI 5.53E-09

(4.0964)*** 2.70E-09

(4.4167)*** 1.29E-09

(1.9714)* 1.49E-09

(1.7129)* E 2.23E-08

(2.6427)** 1.45E-08

(3.3839)*** 4.99E-09

(2.6470)** 4.12E-09

(1.9379)* DIFFA 1.75E-08

(2.3499)** 4.94E-08

(2.7261)*** 3.14E-08

(1.1442) 1.82E-08

(1.6516) DIFFL 9.21E-09

(3.7236)*** 3.33E-09

(2.7956)*** 9.41E-10

(0.6857) 1.29E-09

(0.6964) CINFV -3.6894

(-1.0843) -3.4329

(-1.1405) -3.3784

(-0.8618) -4.2177

(-0.7946) OBDI 9.20E-10

(0.1665) 2.30E-09

(1.4366) -1.53E-08

(-3.0437)*** -3.78E-09

(-5.2193)***

Constant 17.6430 (24.5204)***

17.8263 (27.7095)***

17.9972 (22.7136)***

18.0481 (16.3923)***


14.2600 (0.0000)

16.4025 (0.0000)

11.2408 (0.0000)

12.7152 (0.0000)

DW Stat. 2.3430 2.1222 2.1103 2.0600 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p< 0.10, respectively Note: Number in italic represents standard errors 1 Results are based on White-Heteroscedasticity Corrected Regression.

90 Results on full data are presented in Appendix D.

Appendix C

229





Appendix D

230

APPENDIX D: FURTHER TEST

Appendix D

231

Results for Refined Data

This appendix provides supporting evidence for the results presented in chapter eight.

The results presented in this appendix generally support the results presented in

previous chapters. This appendix provides evidence that the quality of derivatives

disclosure in the financial statement is value relevant as reported in chapter eight.

Market participants regard the net fair value information and risk information as

important as the book value of equity. However, these results have undoubtedly been

influenced by certain periods of time and this is especially the case for net fair value

information. To a limited extent, net fair value of financial instruments, the unrealised

gain or loss of financial liabilities and off-balance sheet derivative financial

instruments are value relevant.

Venkatachalam (1996) indicates that one plausible explanation for insignificant and

inconsistent results for the fair value of off-balance sheet items was due to setting this

variable to zero for firms that either reported no off-balance sheet obligations or

considered the fair value to be equal to the carrying value. The same approach was

performed in the current study, where the off-balance sheet derivative financial

instruments (OBDI) of 93 observations is set to zero for firms either reporting no

OBDI or firms that did not report anything about OBDI. Hence the above results may

be subject to the same distortion. Therefore, the above models are re-estimated using

the refined data of 162 firms. Further, six of the firm/years are deleted due to

substantial differences in the book value of equity and earnings compared to the rest

of the sample.

Appendix D

232

1. Results on the Pooled Data

Panel A of Table D 1 indicates that book value of equity and transparency are positive

and significant at p < 0.01. Panel B of Table D 1 provides evidence that the book

value of equity and the component score of risk information are positive and

significant at p < 0.001. However, the component score of net fair value is negative

and significant at p < 0.05. Except for the component score of net fair value, these

results are similar to those reported for the full data set presented in Table 8.6 in

chapter 8. The adjusted R2 of equation 6.3 for this data set is almost equal to the

adjusted R2 presented in Panel A Table 8.6 (56.46% as compared to 56.43%).

However, this does not hold true for equation 6.4.

Table D 1: The Association between Information Quality of Derivative Disclosures and the Market Value of the Firms (n=156)1

Variables Coefficient Std Error t-Statistics Prob

Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 (equation 6.3) BV 2.69E-09 2.43E-10 11.1006 0.0000*** E -1.03E-09 1.54E-09 -0.6667 0.5060 TRANSP 5.5682 1.0246 5.4347 0.0000*** Constant 12.6334 0.9203 13.7281 0.0000 Adj R2 = 0.5646 DW Statistics = 1.8270 F-statistics = 68.0110 Prob. = 0.0000 Panel B : Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+εit (equation 6.4)

BV 2.58E-09 2.49E-10 10.3380 0.0000*** E -1.01E-09 1.59E-09 -0.6361 0.5257 CINFV -5.0573 2.5573 -1.9775 0.0498** CIHedge 0.3869 2.6776 0.1445 0.8853 CIRisk 7.6979 1.6002 4.8106 0.0000*** Constant 17.1312 0.6380 26.8519 0.0000 Adj R2 = 0.5507 DW Statistics = 1.9333 F-statistics = 38.9914 Prob. = 0.0000 *** and ** indicate significance at p < 0.01 and p < 0.05, respectively. 1 Results are based on White’s heteroscedasticity-corrected regression

Appendix D

233


months following the financial year BV = book value of equity at year end E = earnings for year available to firm’s common shareholders TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s

total possible disclosure scores CIHedge, = component score of hedge information CINFV = component score of net fair value CIRisk = component score of risk information i = firm t = year

Table D 2 presents the results for the value relevance of hedge disclosures and the

market value of the firms. The results indicate that only book value of equity is

significant in both models. A departure from the results previously presented in Table

8.7 is the insignificance of hedge information in both models.

Table D 2: The Association between Hedge Disclosure and the Market Value of the Firms (n=156)1

Variables Coefficient Std Error T-statistics Prob Pit = α0 + α1BVit+ α2Eit + α3CHedge,it + α4OBDIit + α5URGL it+ εit BV 2.88E-09 2.67E-10 10.7693 0.0000*** E -9.77E-10 1.57E-09 -0.6229 0.5343 CIHedge 3.0556 2.6225 1.1652 0.2458 OBDI 4.71E-09 6.38E-09 0.7378 0.4618 URGL -5.34E-09 6.45E-09 -0.8279 0.4091 Constant 17.0214 0.5538 30.7350 0.0000 Adj R2 = 0.5192 Durbin Watson = 1.8828 F-statistics = 32.3970 Prob 0.0000

*** indicates significance at p < 0.01 1 Results are based on White’s Heteroscedasticity Corrected Regression Variable definitions: P = natural log market value of firms’ common equity measured three


Appendix D

234

Table D 3 indicates that the net fair value fair value of financial instruments (TFFI)

and BVNFI are significant. The adjusted R2 for this data set is 49.10% compared to

49.98% for the results presented in Table 8.8.

Table D 3: The Association between Net Fair Value and Market Value (n=156) Variables Coefficient Std Error t-statistics Prob Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it +εit

BVNFI 2.57E-09 4.55E-10 5.6497 0.0000*** E -9.14E-10 1.76E-09 -0.5193 0.6043 TFFI 2.05E-09 9.56E10 2.1489 0.0332** OBDI -6.62E-10 8.32E-10 -0.7951 0.4278 CINFV -0.6293 2.6363 -0.2387 0.8117 Constant 17.7700 0.5187 34.2561 0.0000 Adj R2 = 0.4910 Durbin Watson = 1.9344 F-statistics = 30.9026 Prob 0.0000 ***, ** and * indicate significance at p < 0.01 level, p < 0.05 and p < 0.10, respectively Variable definitions: P = natural log market value of firms’ common equity measured three


Table D 4 provides evidence on the association between URGL and the market value

of the firm. Consistent with the results of Simko (1999), Table D 4 indicates that the

unrealised gain or loss of financial liabilities is significant at p < 0.001. The adjusted

R2 for the equations presented in Table D 4 is higher than the adjusted R2 of Table 8.9.

Appendix D

235

Table D 4: TheAssociation between the Market Value of Firms and the Difference between Net Fair Value and Book Value of Financial Instruments

(n=156) Variables Coefficient Std Error t-statistics Prob Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit +α6CINFV,it + εit

[ White’s heteroscedasticity-corrected regression] BVNFI 2.00E-09 2.25E-10 8.9113 0.0000*** E 4.90E-09 2.03E-09 2.4113 0.0171** DIFFA -2.95E-09 3.97E-09 -0.7420 0.4592 DIFFL 3.20E-08 9.05E-09 3.5343 0.0005*** CINFV -2.1688 2.4166 -0.8975 0.3709 OBDI 2.68E-10 9.05E-10 0.2964 0.7674

Constant 18.0266 0.4761 37.8623 0.0000 Adj R2 = 0.5151 Durbin Watson = 1.9689 F-statistics = 28.4448 Prob = 0.0000

***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively. Variable definitions: P = natural log market value of firms’ common equity measured three




Even though there are inconsistent results in the refined data set, a general conclusion

on the value relevance of financial instruments, especially derivative instruments can

be made. This is because only data of the firms that disclosed off-balance sheet

derivative financial instruments information are estimated. Results on Table D 2 to

Table D 4 provide evidence that net fair value of financial instruments and the

unrealised gain or loss of financial liabilities are value relevant. Hence, these results

Appendix D

236

indicate that market participants regard the disclosed quantitative information as value

relevant for recognised items.

2. Results on the Year-By-Year Analysis

Results on year-by-year analysis for the refined data set are illustrated in Table D 5 to

Table D 8. Disclosure quality is significantly associated with market value in all

years, except in 2000. Panel B Table D 5 indicates that the component score of the

hedge information is not significant in all years and this result is consistent with the

pooled data reported in Table D 1. Even though the component score of the net fair

value is significant only in 1999, these results may have influenced the significance of

the net fair value in the pooled data set.

Table D 5:The Association between the Information Quality of Derivatives Disclosures and the Market Value of the Firms: Year-by-Year Analysis

Variables 1998 (n=39) 1999 (n=36) 20001,2 (n=40) 20012 (n=42) Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 BV 1.19E-09

(1.3325) 3.03E-09

(3.4766)*** 1.96E-09

(1.9266)* 2.35E-09

(4.6339)*** E 1.81E-08

(1.9866)* -3.97E-09

(-0.6375) 8.50E-10

(0.2355) -8.60E-10

(-0.3163) TRANSP 6.4426

(3.3384)*** 4.9754

(2.3160)** 4.1965

(1.4856) 6.5506

(2.5290)** Constant 11.7783

(6.7913)*** 13.1281 (6.7401)**

14.0060 (5.4596)***

11.8888 (5.0846)***


22.6885 (0.0000)

18.2895 (0.0000)

7.1642 (0.0007)

13.6869 (0.0000)

DW Stat. 0.8407 1.3635 1.7542 1.5406

Appendix D

237

Panel B : Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4CINFV,it + α5CIRisk, it+εit

BV 1.80E-09 (1.7069)*

2.72E-09 (3.0101)***

1.86E-09 (1.8868)*

1.97E-09 (3.5357)***

E 1.31E-08 (1.2303)

-3.18E-09 (-0.5080)

4.33E-10 (0.1229)

4.59E-10 (0.1466)

CINFV 2.6527 (0.4982)

-10.8648 (-2.3837)**

-4.9873 (-0.8711)

-9.5838 (-1.1732)

CIHedge -6.2751 (-1.0363)

-0.9937 (-0.2366)

4.0046 (0.9818)

2.4856 (0.3022)

CIRisk 4.9100 (1.7154)*

8.5689 (2.4367)**

7.4287 (1.9230)*

12.5015 (2.7161)**

Constant 17.3722 (13.8069)***

18.3860 (15.8976)***

16.5026 (11.9650)***

16.7912 (9.4107)***

Adj R2 0.5460 0.6023 0.3275 0.4893 F-stat. (Prob)

10.1407 (0.0000)

11.6023 (0.0000)

4.7981 (0.0020)

8.8566 (0.0000)

DW Stat. 0.7108 1.5806 1.7408 1.81407 ***, ** and * indicate significance at p < 0.01 level, p < 0.05 level and p < 0.10, respectively. Note: Number in italic represents t-statistics 1 Results for Equation 6.3 (Panel A) are based on White-Heteroscedasticity Corrected Regression. 2 Results for Equation 6.4 (Panel B) are based on White-Heteroscedasticity Corrected Regression. Variable definitions: Pit = natural log market value of firms’ common equity measured three

months following the financial year t for firm i, BVit = book value of equity at year end t for firm i, Eit = earnings for year t available to firm i’s common shareholders, TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s

total possible disclosure scores CIHedge = component score of hedge information, CINFV = component score of net fair value, CIRisk = component score of risk information. t = time i = firm

Table D 6 presents the results on the association between hedge disclosure and the

market value of the firms. Table D 6 indicates that URGL is significant in 1998 and

2000 at p < 0.10. OBDI is also significant in 1998 at p < 0.10. In addition to that

BVNFI is significant in 1998 and 1999, E is significant in 2000 at p < 0.05 and CINFV


Appendix D

238

Table D 6: The Association between Hedge Disclosures and the Market Value of the Firms: Year-by-Year Analysis

Variables 1998 (n=39) 1999 (n=36) 20001 (n=40) 2001 (n=42)


BV 3.05E-09 (2.3544)**

2.23E-09 (2.8727)***

1.50E-09 (1.3536)

2.33E-09 (3.2115)

E 1.65E-08 (1.5214)

-2.43E-09 (-0.3849)

1.22E-08 (2.1390)**

-1.15E-10 (-0.0415)

CIHedge -3.0498 (-0.5064)

4.5426 (1.1196)

6.6778 (2.0007)*

10.4165 (1.5007)

URGL 2.59E-08 (1.7203)*

1.57E-08 (1.57E-08)

-2.84E-08 (-1.9362)*

-7.00E-09 (-0.5489)

OBDI -2.74E-08 (-1.7295)*

-1.32E-08 (-0.4857)

1.44E-08 (0.8852)

4.38E-09 (0.3287)

Constant 18.0121 (15.3596)***

16.7038 (20.5573)***

16.0914 (21.9677)***

15.4484 (10.4718)***

Adj R2 0.5299 0.6021 0.4756 0.4980 F Stat (Prob)

9.5652 (0.0000)

11.5914 (0.0000)

8.0737 (0.0000)

9.1346 (0.0000)

DW Stat 0.8403 1.6027 1.8051 1.3421 ***, ** and * indicate significance at p < 0.01 level, p < 0.05 level and p < 0.10, respectively Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year, BV = book value of equity at year end, E = earnings for year t available to firm i’s common shareholders CIHedge = component score of hedge information URGL = unrealised gain or loss of financial assets and financial liabilities OBDI = off-balance sheet derivative financial instruments t = time i = firm Table D 7 reports that OBDI is significant in 1999 to 2001. While OBDI is positive

and significant at p < 0.10, the variable is negatively related in 2000 and 2001. Table

D 7 also provides evidence that BVNFI and E are significant in 1999 and 2000,

respectively.

Appendix D

239

Table D 7: The Association between Net Fair Value and Market Value: Year-by-Year Analysis

Variables 1998 (n=39) 1999 (n=36) 20001 (n=40) 2001 (n=42) Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it +εit

BVNFI 1.27E-09 (0.8247)

2.85E-09 (2.9455)***

2.66E-10 (0.3504)

1.11E-09 (1.3508)

E 1.63E-08 (1.5414)

-3.73E-09 (-0.5626)

8.37E-09 (2.3470)**

3.09E-09 (0.9627)

TFFI 6.84E-10 (0.2369)

2.67E-09 (1.6730)

-1.70E-09 (-1.0784)

-5.13E-10 (-0.2879)

OBDI 1.29E-09 (0.4234)

2.73E-09 (1.8984)*

-1.25E-08 (-3.0985)***

-2.64E-09 (-2.5132)**

CINFV 5.2854 (0.9744)

-1.8322 (-0.4565)

-0.5406 (-0.1067)

-2.3893 (-0.3547)

Constant 16.5349 (15.9925)***

17.9216 (22.5149)***

17.7402 (17.8595)***

18.0809 (15.1789)***


8.7704 (0.0000)

10.8447 (0.0000)

7.2398 (0.0001)

8.8004 (0.0000)

DW Stat. 0.7195 1.5187 2.1647 1.6617 ***, ** and * indicate significance at p < 0.01 level, p < 0.05 level and p < 0.10, respectively Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments TFFI = net fair value of financial instruments OBDI = off-balance sheet derivative financial instruments CINFV = component score of net fair value t = time i = firm Replacing TFFI with DIFFA and DIFFL cause BVNFI to be significantly related with

market value in all four years. Table D 8 provides evidence that BVNFI is significant

at p < 0.05 in 1998 to 2000 and at p < 0.001 in 2001. Also reported significant are E

in 1998, 2000 and 2001, DIFFL in 1998 and 2000 and OBDI in 2000.

Appendix D

240

Table D 8: The Association between the Market Value of Firms and the Difference between Net Fair Value and Book Value of Financial Instruments

Variables 1998 (n=39) 1999 (n=36) 20001 (n=40) 2001 (n=42) Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit +

α6CINFV,it + εit BVNFI 1.23E-09

(2.0456)** 1.60E-09

(2.6580)** 1.13E-09

(2.3627)** 2.05E-09

(5.1683)*** E 2.72E-08

(2.8756)*** 6.07E-09

(0.7136) 1.43E-08

(2.5442)** 9.46E-09

(2.8404***) DIFFA -1.90E-09

(-0.1295) 1.93E-08

(0.7467) -7.28E-08

(-1.6753) 1.85E-09

(0.1346) DIFFL 5.88E-08

(1.9770)* 6.72E-09

(0.1942) 3.25E-08

(2.3508)** 8.38E-08

(3.1590) CINFV 5.3516

(1.1622) -1.1981

(-0.2835) -1.2349

(-0.2874) -6.0550

(-1.1342) OBDI 1.21E-09

(0.6690) 3.67E-09

(1.3921) -1.04E-08

(-2.5701)** -1.07E-09

(-0.9674)

Constant 16.3600 (18.3168)***

17.8362 (21.1015)***

17.6892 (19.5991)***

18.6434 (18.4579)***


11.2875 (0.0000)

8.3648 (0.0000)

7.4815 (0.0000)

10.7878 (0.0000)

DW Stat. 1.2401 1.9726 2.1308 2.1543 ***, ** and * indicate significance at p < 0.01 level, p < 0.05 level and p < 0.10, respectively Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three




Appendix D

241

3. The Incremental Explanatory Power of Net Fair Value and the

Unrealised Gain or Loss on Financial Instruments

Table D 9 and Table D 10 present the results on the incremental explanatory power of

net fair value and the unrealised gain or loss on financial instruments. Both tables

indicate that the incremental explanatory power of net fair value and the unrealised

gain or loss on financial instruments beyond the book value of financial and non-

financial instruments and earnings valued at historical cost for the pooled data is very

low. Table D 9 indicates that the incremental explanatory power of net fair value is -

0.19%. However, the incremental explanatory power of book value of financial and

non-financial instruments and earnings beyond the net fair value on financial

instruments for the pooled data is 18.11%. The low incremental explanatory power of

net fair value indicates that net fair value does not provide additional information

beyond the book value. This is consistent with results presented in chapter 8.

The incremental explanatory power of net fair value beyond the book value of

financial and non-financial instruments and earnings valued at historical cost has

increased dramatically from 0.90% (1998) to 13.22% (2000). However, unlike the

results using the full data, the incremental explanatory power of the book value of

financial and non-financial instruments and earnings beyond the net fair value also

increased from 9.10% (1998) to 21.70% (2000). Nevertheless, the incremental

explanatory power of each variable beyond the other reduced in 2001.

Appendix D

242

Table D 9: Incremental Explanatory Power of Net Fair Value Beyond the Book Value of Financial and Non-financial Instruments and Earnings Valued at the

Historical Cost

Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4TFFIit + α5OBDIit + α6CINFV,it +εit - AdjR2



h AdjR2

h,nfv AdjR2h AdjR2


h/nfv 1998 0.5253 0.5163 0.4343 0.0090 0.0910 1999 0.5898 0.5300 0.4043 0.0598 0.1855 2000 0.5027 0.3705 0.2857 0.1322 0.2170 2001 0.4945 0.4261 0.3928 0.0684 0.1017 Pooled data 0.4972 0.4991 0.3161 -0.0019 0.1811



h,nfv = the total explanatory power of book value of financial and non-financial and earnings valued at historical cost and net fair value

AdjR2h = the explanatory power of book value of financial and non-financial and earnings valued at


nfv = the explanatory power of net fair value AdjR2 nfv/h = the incremental explanatory power of net fair value AdjR2

h/nfv = the incremental explanatory power of book value of financial and non-financial and earnings valued at historical cost

Table D 10 presents the results of the incremental explanatory power of the unrealised

gain or loss on financial instruments beyond the book value of financial and non-

financial instruments and earnings valued at historical cost. Table D 10 indicates that

the incremental explanatory power of the unrealised gain or loss on financial

instruments is 5.09% for the pooled data. Nevertheless, the incremental explanatory

Appendix D

243

power of the book value of financial and non-financial instruments and earnings

valued at historical cost beyond the unrealised gain or loss on financial instruments is

42.85%. As in above, this indicates that the incremental explanatory power of URGL

does not provide additional information above the book value and earnings.

Unlike the net fair value, the incremental explanatory power of the unrealised gain or

loss on financial instruments beyond the book value of financial and non-financial

instruments and earnings at historical cost has increased from 1998 to 2001. The

incremental explanatory power of the book value of financial and non-financial

instruments and earnings beyond the unrealised gain or loss on financial instruments

has also increased from 34.08% (1998) to 38.55% (2001).

Table D 10: Incremental Explanatory Power of Unrealised Gain or Loss on Financial Instruments Beyond the Book Value of Financial and Non-financial

Instruments and Earnings Valued at the Historical Cost Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit + α6OBDIit + α7CINFV,it




h AdjR2

h,urgl AdjR2h AdjR2

urgl AdjR2urgl/h AdjR2

h/urgl 1998 0.6258 0.5163 0.2850 0.1095 0.3408 1999 0.6307 0.5300 0.3337 0.1007 0.2970 2000 0.5051 0.3705 0.1544 0.1346 0.3507 2001 0.6192 0.4261 0.2337 0.1931 0.3855 Pooled data 0.5500 0.4991 0.1215 0.0509 0.4285


months following the financial year BVNFI = book value of non financial instruments E = earnings for year t available to firm i’s common shareholders TBFI = total book value of financial instruments OBDI = off-balance sheet derivative financial instruments

Appendix D

244

DIFFA = difference between net fair value of financial assets and book value of financial assets.

DIFFL = difference between net fair value of financial liabilities and book value of financial liabilities.

CINF, = component score of net fair value t = times i = firms AdjR2

h,urgl = the total explanatory power of book value of financial and non-financial and earnings valued at historical cost and unrealised gain or loss on financial instruments

AdjR2h = the explanatory power of book value of financial and non-financial and earnings valued at


urgl = the explanatory power of unrealised gain or loss on financial instruments AdjR2

urgl/h = the incremental explanatory power of unrealised gain or loss on financial instruments 4. Discussion of Results for the Refined Data Consistent with the suggested approach of Venkatachalam (1996), this appendix

presents the results for multiple regression analysis for a refined data set. Results

presented in Table D 1 indicate that market participants regard disclosure quality of

derivative information as important in firm valuation. This result is consistent with the

results from the full data set. This indicates that high quality derivative information

contributes to higher share prices. As for the full data set, Panel B Table D 1 indicates

that book value of equity, the component score of net fair value and the component

score of risk information are value relevant. However, the net fair value information is

negatively related to the market value of firms. The negative relationship may have

been influenced by certain years. Panel B Table D 5 indicates the negative

relationship between market value and the component score of net fair value for three

years out of the four year period (1999 to 2001). However, the component score of net

fair value is only significant in 1999.

The insignificance of hedge information in the pooled data has been influenced by a

firm specific year. The year-by-year analysis indicates that the component score of

hedge information and the information required by paragraph 5.8 AASB 1033 are

Appendix D

245

significant in some years, but not in other years. Table D 6 indicates that the

component score of hedge information is significant in 2000, while the other hedge

variables (URGL and OBDI,) are significant in 1998. URGL also significant in 2000.

Although the results for the pooled data indicate that none of the hedge information

variables are significant, the results for the year-by-year analysis indicate that the

information is significant in some years, providing evidence of their value relevant.

Table D 3 indicates that the off-balance sheet derivative financial instruments are

significant. However, this could be influenced by a time factor since the variable is

significant in 1998 to 2001. The net fair value of financial instruments is significant in

the pooled data. However, it is not significant for the year-by-year analysis. This

result indicates that net fair value may not provide better information for decision

making. Furthermore, the incremental explanatory power of net fair value and the

unrealised gain or loss on financial instruments is lower than the incremental

explanatory power of book value of financial and non-financial instruments and

earnings beyond the net fair value and the unrealised gain or loss on financial

instruments.

The inconsistencies in the significance of net fair value and the unrealised gain or loss

of financial instruments provides evidence that market participants in Australia,

especially with regard to the extractive industries, are not ready to move to a fair

value accounting regime. This is consistent with prior Australian studies (Deloitte

Touche Tohmatsu, 2000; Fargher, 2001; Tan et al., 2003), which fail to provide

conclusive evidence on the perceptions of managers and financial statement users on

Appendix D

246

fair value accounting. These results provide information to the standard setting bodies

in Australia in developing or adopting accounting standards related to fair values.

5. Summary This appendix provides evidence on the significance, or value relevance, of derivative

information based on a refined data set. Results presented in this appendix are

generally similar to those for the full data. This appendix indicates that market

participants regard the quality of derivative disclosures in the financial statements as

value relevant. They also value net fair value information and risk information as

being as important as the book value of equity. However, these results may have been

influenced by year effects, especially for the net fair value information. To a limited

extent, net fair value of financial instruments and the unrealised gain or loss of

financial liabilities also value relevant.

Appendix E

247

APPENDIX E: FIRM CHARACTERISTICS MODEL ESTIMATION WITHOUT THE OUTLIERS (REFINED DATA)

Appendix E

248

Table E 1: Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics (n=254)




Constant ? 0.5346 0.0823 6.4968 0.0000

SIZE + 0.0195 0.0046 4.2494 0.0000***

PROFIT + 0.0183 0.0100 1.8360 0.0676*

PE + 3.67E-05 1.98E-05 1.8573 0.0645*

TYPE - 0.0023 0.0148 0.1557 0.8764

Audit +/- -0.0182 0.0187 -0.9732 0.3314

MTB + -8.23E-05 2.66E-05 -3.0955 0.0022***

R&D + 0.0078 0.0137 0.5695 0.5695

DTE +/- 0.0228 0.0103 2.2039 0.0285**

Adjusted R2 for model = 0.2124 Durbin-Watson Statistics = 2.0778 F statistics for model = 9.5308 p-value for model 0.0000 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10 respectively.

Variable Definitions: TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity

Appendix E

249

Table E 2: The Association between Firms Characteristics and Disclosure Quality on a Year-by-Year Basis and an Average of Four Years Data

TRANSPit=α0+α1SIZEit+α2PROFITit+α3PEit+α4TYPEit+α5AUDITit+α6MTBit +α7R&Dit +α8DTEit+ εit

Variable Sign 1998 (n=62) 1999 (n=61) 2000 (n=62) 2001 (n=61) Average (n=60) Constant ? 0.5384 (2.1424)** 0.6097 (2.6516)** 0.6625 (4.1088)*** 0.4925 (3.1190)*** 0.4895 (2.9641)*** SIZE + 0.0144 (1.0585) 0.0144 (1.1517) 0.0130 (1.4390) 0.0230 (2.5979)** 0.0212 (2.2652)** PROFIT + 0.0317 (0.6571) 0.0054 (0.0858) 0.1087 (2.2823)** -0.0050 (-0.1367) 0.0140 (0.2173) PE + 0.0005 (1.1154) 1.36E-05 (0.2843) 0.0006 (1.4837) 0.0005 (0.9345) 0.0009 (1.9958)** TYPE - -0.0041 (-0.0999) 0.0064 (0.1760) 0.0268 (1.0083) 0.0066 (0.2716) 0.0272 (1.1295) Audit +/- 0.0430 (1.0931) -0.0229 (-0.6343) -0.0284 (-0.8812) -0.0175 (-0.5282) -0.0105 (-0.3307) MTB + 0.0002 (0.1330) 0.0010 (0.6251) -0.0012 (-0.9501) 0.0005 (0.7908) 3.07E-05 (0.0879) R&D + 0.0110 (0.3006) 0.0247 (0.6900) 0.0058 (0.1983) -0.0296 (-1.0386) -0.0200 (-0.7415) DTE +/- 0.0811 (1.6405) 0.0541 (1.3347) 0.0345 (0.9790) 0.0083 (0.2637) 0.0389 (0.9366) Adjusted R2 0.2788 0.1524 0.2258 0.1318 0.2231 F statistics 3.9479 2.3481 3.2235 2.1389 3.1177 p-value 0.0010 0.0309 0.0047 0.0483 0.0061 Durbin-Watson Stat. 2.1464 2.2505 1.7679 1.6205 2.1651


Note: Number in italic represents t-statistics Variable Definitions:

TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total possible disclosure scores SIZE = log of total assets PROFIT = earnings before tax / total assets PE = price/earnings before extraordinary items per share TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise MTB = market value/net book value of tangible assets for the given class of equity R&D = 1 for R&D firm, 0 otherwise. DTE = total liabilities divided by book value of common equity

Appendix E

250

Table E 3: Results of Regression Analysis of the Association between Disclosure Transparency and Firms Characteristics: Ranked Transformation (n=254)

TRANSPit=α0+α1RSIZEit+α2RPROFITit+α3RPEit+α4TYPEit+α5AUDITit+α6RMTBit +α7R&Dit +α8RDTEit+ εit

Variable Predicted Sign Coefficient Std. Error t-Statistics Prob Constant ? 32.2153 19.5372 1.6489 0.1004 RSIZE + 0.3410 0.1011 3.3735 0.0009*** RPROFIT + 0.0371 0.0678 0.5469 0.5850 RPE + 0.2052 0.0649 3.1626 0.0018*** TYPE - 15.0826 11.3072 1.3339 0.1835 Audit +/- -14.4772 12.4769 -1.1603 0.2470 RMTB + -0.0547 0.0491 -1.1136 0.2666 R&D + -8.2306 10.3070 -0.7985 0.4253 RDTE +/- 0.2905 0.0727 3.9967 0.0001***

Adjusted R2 for model = 0.3215 Durbin-Watson Statistics = 2.0607 F statistics for model = 15.9878 p-value for model = 0.0000 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10 respectively.

Variable Definitions: RTRANSP = rank of disclosure (transparency) RSIZE = rank of total assets (in thousands) RPROFIT = rank of profitability RPE = rank of price/earnings ratio TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise RMTB = rank of market-to-book ratio R&D = 1 for R&D firm, 0 otherwise. RDTE = rank of total liabilities divided by book value of common equity

Appendix E

251

Table E 4: Refined Data: Association between Disclosure Transparency and Firms Characteristics: Ranked Transformation Year-by-Year Basis and an

Average of Four Years Data

TRANSPit=α0+α1RSIZEit+α2RPROFITit+α3RPEit+α4TYPEit+α5AUDITit+α6RMTBit +α7R&Dit +α8RDTEit+ εit

Variable Sign 1998 (n=62) 1999 (n=61) 2000 (n=62) 2001 (n=61) Average (n=60) Constant ? -3.8273(-0.3489) 9.2947(0.8809) 3.8887(0.4110) 9.3183(0.9764) -0.1688(-0.0200) RSIZE + 0.3962(1.6941)* 0.2451(0.9729) 0.2565(1.2327) 0.5419(2.4428)** 0.5610(2.6032)** RPROFIT + 0.0552(0.4286) 0.1052(0.7644) 0.0766(0.5620) -0.1539(-0.9463) -0.1831(-1.3550) RPE + 0.2706(2.3182)** 0.1827(1.2958) 0.4495(3.4934)*** 0.1296(0.8695) 0.4093(3.4449)*** TYPE - 5.5808(0.8688) 5.6231(0.8850) 9.5398(1.8181)* -0.3450(-0.0640) 7.1830(1.4798) Audit +/- 4.4076(0.8243) -6.3022(-1.0307) -1.1182(-0.1753) -4.5597(-0.6359) -1.7990(-0.2871) RMTB + -0.0424(-0.4028) -0.0875(-0.7482) -0.2163(-1.9979)* 0.1332(1.0856) 0.0253(0.2284) R&D + -2.4918(-0.4772) 2.5139(0.4211) -1.9442(-0.3401) -10.6214(-1.7057)* -7.5740(-1.4494) RDTE +/- 0.2794(1.8821)* 0.3283(2.2431)** 0.2586(1.7920)* 0.2815(2.0783)** 0.2450(1.8300)* Adjusted R2 0.3861 0.2347 0.3348 0.2510 0.3919 F statistics 5.7951 3.2998 4.8371 3.5130 5.7528 p-value 0.000 0.0040 0.0002 0.0026 0.0000 Durbin-Watson Stat. 2.1818 2.2635 1.7345 1.9162 2.0790

***, ** and * indicate significance at p < 0.01 , p < 0.05 and p < 0.10 respectively.

Note: Number in italic represents t-statistics Variable Definitions: RTRANSP = rank of disclosure (transparency) RSIZE = rank of total assets (in thousands) RPROFIT = rank of profitability RPE = rank of price/earnings ratio TYPE = 1 for no-liability company, 0 otherwise. AUDIT = 1 for Big-5/6 auditor, 0 otherwise RMTB = rank of market-to-book ratio R&D = 1 for R&D firm, 0 otherwise. RDTE = rank of total liabilities divided by book value of common equity

Appendix F

252

APPENDIX F: RESULTS ON MARKET VALUE MODEL FOR FULL DATA

Appendix F

253

1. Full Data Sample 2.1. Pooled Data

Table F 1: The Association between Information Quality of Derivative Disclosures and the Market Value of the Firms (n=260)1


Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 BV 7.22E-10 1.10E-10 6.5836 0.0000*** E 7.46E-10 5.85E-10 1.2746 0.2036 Transp 7.6693 0.8854 8.6623 0.0000*** Constant 10.9153 0.7752 14.0804 0.0000*** Adj R2 = 0.4709 DW Statistics = 1.9205 F-statistics = 77.8289 Prob. = 0.0000 Panel B : Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+εit

BV 7.35E-10 1.03E-10 7.1432 0.0000*** E 7.24E-10 5.46E-10 1.3270 0.1857 CINFV -2.1492 2.2140 -09707 0.3326 CIHedge 8.5090 1.1452 7.4299 0.0000*** CIRisk 5.5022 1.2193 4.5124 0.0000*** Constant 15.5687 0.5288 29.4439 0.0000*** Adj R2 = 0.4855 DW Statistics = 1.8066 F-statistics = 49.8893 Prob. = 0.0000 *** indicates significance at p < 0.001 1 Results are based on White’s Heteroscedasticity Correction Regression Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BV = book value of equity at year end E = earnings for year available to firm’s common shareholders TRANSP = disclosure transparency = firm’s actual disclosure scores/firm’s total

possible disclosure scores CIHedge = component score of hedge information CINFV = component score of net fair value CIRisk = component score of risk information i = firm t = year

Appendix F

254

Table F 2: The Association between Information Quality of Derivative Disclosures and the Market Value of the Firms – Ranked based on Large and

Small (n=128)

Variables Coefficient Std Error T-statistics Prob Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 (Newey-West HAC Standard Errors & Covariance) BV 1.88E-09 3.94E-10 4.7719 0.0000*** E 8.96E-10 1.16E-09 0.7688 0.4434 Transp 3.4407 1.0128 3.3973 0.0009*** Constant 14.4789 0.8910 16.2507 0.0000*** Adj R2 = 0.4642 DW Statistics = 1.4457 F-statistics = 37.6746 Prob. = 0.0000 Panel B : Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+εit

BV 1.84E-09 3.80E-10 4.8450 0.0000*** E 1.03E-10 1.09E-09 0.9430 0.3475 CINFV -4.1369 2.5571 -1.6178 0.1083 CIHedge 0.8572 1.5877 0.5399 0.5903 CIRisk 4.3265 1.5034 2.8779 0.0047*** Constant 17.3598 0.6377 27.2222 0.0000*** Adj R2 = 0.4570 DW Statistics = 1.4455 F-statistics = 22.3803 Prob. = 0.0000 *** indicates significance at p < 0.001


months following the financial year BV = book value of equity at year end E = earnings for year available to firm’s common shareholders TRANSP = disclosure transparency = firm’s actual disclosure

scores/firm’s total possible disclosure scores CIHedge = component score of hedge information CINFV = component score of net fair value CIRisk = component score of risk information i = firm t = year

Appendix F

255

Table F 3: The Association between Information Quality of Derivative Disclosures and the Market Value of the Large and Small Firms

Variables Small firms (n=64) Large firms (n=64)1

Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 BV 1.34E-08 (2.99E-09)*** 1.63E-09 (4.04E-10)*** E 9.82E-09 (5.59E-09)* 8.96E-10 (1.23E-09) Transp 0.0199 (1.0384) 6.9797 (1.1247)*** Constant 16.9718 (0.9450)*** 11.5517 (0.8795)*** Adj R2 0.2511 0.5832 DW Statistics 1.7969 1.4432 F-statistics (Prob.) 8.0410 (0.0001) 30.3859 (0.0000) Panel B : Pit = α0 + α1BVit+ α2Eit + α3CINFV,it + α4CIHedge,it + α5CIRisk, it+εit

BV 1.52E-08 (3.20E-09)*** 1.46E-09 (3.42E-10)*** E 9.59E-09 (5.60E-09)* 1.30E-09 (1.06E-09) CINFV -0.8730 (2.7394) -7.7563 (4.0236)* CIHedge -2.6425 (1.6874) 2.8661 (1.5754)* CIRisk 0.1629 (1.4843) 9.6935 (1.9023)*** Constant 17.5557 (0.6572)*** 16.9428 (0.9129)*** Adj R2 0.2596 0.5670 DW Statistics 1.9214 1.5589 F-statistics (Prob.) 5.4182 (0.0004) 19.6648 (0.0000) *** and * indicate significance at p < 0.001 and p < 0.10, respectively. 1 Results are based on the Newey-West HAC Standard Errors & Covariance. Variable definitions: P = natural log market value of firms’ common equity measured three

months following the financial year BV = book value of equity at year end E = earnings for year available to firm’s common shareholders TRANSP = disclosure transparency = firm’s actual disclosure

scores/firm’s total possible disclosure scores CIHedge = component score of hedge information CINFV = component score of net fair value CIRisk = component score of risk information i = firm t = year

Appendix F

256

2.2 Year-By-Year Analysis

Table F 4: The Association between Information Quality of Derivatives Disclosures and Market Value of the Firms: Year-by-Year Analysis (n=65)

Variables 1998 1999 20001 20011,2

Panel A : ititititti TRANSPEBVP εαααα ++++= 3210 BV 1.67E-09

(9.0223)*** 2.05E-09

(8.140)*** 4.64E-10

(1.1234) 9.10E-10

(1.7506)* E 9.76E-09

(6.4373)*** 5.49E-09

(5.2821)*** 1.68E-09

(0.6905) -9.74E-10

(-0.3286) TRANSP 5.9448

(4.5781)*** 4.2002

(2.4863)** 7.7565

(3.7334)*** 9.2997

(4.2603)*** Constant 12.1233

(10.9480)*** 13.6261 (9.1229)***

10.9346 (5.9024)***

9.3413 (4.8726)***


51.8411 (0.0000)

33.5711 (0.0000)

17.7191 (0.0000)

17.8690 (0.0000)

DW Stat. 2.1661 2.0880 1.7728 1.7088 Panel B : Pit = α0 + α1BVit+ α2Eit + α3CIHedge,it + α4CINFV,it + α5CIRisk, it+εit

BV 1.74E-09 (8.7771)***

1.96E-09 (7.8296)***

5.26E-10 (1.9240)*

8.20E-10 (1.7646)*

E 1.03E-08 (6.2590)***

5.15E-09 (4.9710)***

1.38E-09 (0.8657)

-4.59E-10 (-0.1744)

CINFV -1.5012 (-0.4116)

-1.2027 (-0.3509)

-2.3405 (-0.4903)

-11.2556 (-1.8532)*

CIHedge 5.1791 (2.7532)***

6.4864 (3.1750)***

9.0004 (3.6252)***

11.6158 (3.9741)***

CIRisk 2.7526 (1.5446)

1.5135 (0.6815)

7.2766 (2.5096)**

10.2881 (3.5392)***

Constant 16.2240 (18.1521)***

16.2642 (18.0474)***

15.1886 (12.7230)***

15.5483 (12.2935)***

Adj R2 0.6581 0.6288 0.4691 0.4963 F-stat. (Prob)

25.6370 (0.0000)

22.6807 (0.0000)

12.3088 (0.0000)

13.6118 (0.0000)

DW Stat. 2.2107 2.1168 1.6844 1.6515 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively. Note: Number in italic represents t-statistics. 1 Results for Equation 6.3 (Panel A) are based on White-Heteroscedasticity Corrected Regression. 2 Results for Equation 6.4 (Panel B) are based on White-Heteroscedasticity Corrected Regression. Variable definitions: Pit = natural log market value of firms’ common equity measured three

months following the financial year t for firm i, BVit = book value of equity at year end t for firm i, Eit = earnings for year t available to firm i’s common shareholders, TRANSP = disclosure transparency = firm’s actual disclosure

scores/firm’s total possible disclosure scores CIHedge,it = component score of hedge information, CINFV,it = component score of net fair value, CIRisk,it = component score of risk information.

Appendix F

257

Table F 5: The Association between Hedge Disclosure and Market Value of the Firms: Year-by-Year Analysis (n=65)

Variables 1998 1999 20001 20011


BV 2.05E-09 (6.6114)***

2.23E-09 (7.9905)***

1.01E-10 (0.1753)

5.55E-10 (0.9616)

E 8.86E-09 (3.8350)***

4.41E-09 (4.1339)***

3.13E-09 (1.1387)

7.12E-10 (0.2106)

CIHedge 5.3589 (3.0608)***

5.8361 (3.3488)***

-3.6061 (-0.7793)

9.9196 (3.8064)***

URGL 7.12E-09 (1.0731)

1.02E-08 (1.4369)

7.23E-09 (0.6553)

1.29E-08 (1.4954)

OBDI -6.21E-09 (-0.9071)

-9.27E-09 (-1.3006)

-1.37E-08 (-0.9584)

-1.54E-08 (-1.6795)*

Constant 16.3540 (54.5371)***

16.3193 (52.3332)***

18.5052 (20.5545)***

15.6546 (31.9009)***

Adj R2 0.6525 0.6695 0.3287 0.4388 F Stat (Prob)

25.0328 (0.0000)

26.9244 (0.0000)

7.2676 (0.0000)

11.0072 (0.0000)

DW Stat 2.3433 2.0163 1.8655 1.5347 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three


Appendix F

258

Table F 6: The Association between Net Fair Value and Market Value: Year-by-Year Analysis (n=65)

Variables 1998 1999 20001 20011

Pit = α0 + α1BVNFIit+ α2Eit + α3TFFIit + α4OBDIit + α5CINFV,it +εit

BVNFI 4.24E-09 (4.3487)***

3.25E-09 (4.7248)***

5.46E-10 (0.5788)***

5.44E-10 (0.3471)

E 9.77E-09 (4.4994)***

3.81E-09 (2.3547)**

2.64E-09 (1.0313)

-3.62E-10 (-0.0879)

TFFI 6.49E-09 (3.3554)***

4.34E-09 (3.1360)***

1.02E-09 (0.4842)

2.69E-10 (0.0848)

OBDI -3.93E-09 (-1.554)

1.28E-09 (2.9999)***

-5.48E-09 (-0.8729)

-1.97E-09 (-1.7711)*

CINFV -4.3412 (-1.2343)

-4.0997 (-1.3695)

-3.8381 (-0.8308)

-4.9687 (-0.7936)

Constant 17.8867 (24.2409)***

17.9950 (28.0841)***

18.5207 (20.7193)***

18.6169 (14.5620)***


23.3392 (0.0000)

22.9361 (0.0000)

7.1826 (0.0000)

7.3764 (0.0000)

DW Stat. 2.4625 2.0539 1.8406 1.7596 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p < 0.10, respectively Note: Number in italic represents t-statistics. 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three


Appendix F

259

Table F 7: The Association between Market Value of Firms and Difference between Net Fair Value and Book Value of Financial Instruments (n=65)

Variables 1998 1999 20001 20011

Pit = α0 + α1BVNFIit+ α2Eit + α3DIFFAit + α4DIFFLit + α5OBDIit + α6CINFV,it + εit BVNFI 5.78E-09

(5.1723)*** 2.95E-09

(5.0739)*** 8.33E-10

(0.8103) 2.88E-10

(0.2134) E 1.19E-08

(5.7733)*** 5.52E-09

(3.5709)*** 2.73E-09

(0.9667) -7.42E-10

(-0.2241) DIFFA 1.14E-08

(1.7214)* 2.37E-08

(1.7110)* 3.36E-09

(0.0878) -8.68E-09

(-0.4231) DIFFL 9.44E-09

(4.2955)*** 3.33E-09

(2.9741)*** 1.64E-09

(0.7315) -4.68E-10

(-0.1700) CINFV -4.6213

(-1.3703) -3.9933

(-1.3275) -3.8904

(-0.8453) -5.7549

(-0.9096) OBDI -5.62E-09

(-2.2320)** 1.10E-09

(2.5982)** -5.24E-09

(-0.7843) -1.97E-09

(-1.7996)*

Constant 17.8376 (25.2168)***

17.9676 (27.9092)***

18.5027 (20.4372)***

18.7862 (14.5201)***


22.2390 (0.0000)

19.1548 (0.0000)

6.0381 (0.0000)

6.1436 (0.0000)

DW Stat. 2.5187 2.0912 1.8381 1.7437 ***, ** and * indicate significance at p < 0.01, p < 0.05 and p< 0.10, respectively Note: Number in italic represents standard errors 1 Results are based on White-Heteroscedasticity Corrected Regression. Variable definitions: P = natural log market value of firms’ common equity measured three



value of financial liabilities. CINFV = Component score of net fair value t = time i = firm

Appendix F

260

Table F 8: Incremental Explanatory Power of Net Fair Value Beyond Book Value of Financial and Non-Financial Instruments and Earnings Valued at

Historical Cost Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4TFFIit + α5OBDIit + α6CINFV,it +εit - AdjR2



h AdjR2

h,nfv AdjR2h AdjR2


h/nfv 1998 0.6316 0.6193 0.3320 0.0123 0.2996 1999 0.6252 0.5713 0.2776 0.0539 0.3476 2000 0.3256 0.3239 0.2944 0.0017 0.0312 2001 0.3452 0.3206 0.3490 0.0246 -0.0038



h,urgl = the total explanatory power of book value and earnings valued at historical cost and net fair value

AdjR2h = the explanatory power of book value and earnings valued at historical cost

AdjR2nfv = the explanatory power of net fair value

AdjR2urgl/h = the incremental explanatory power of net fair value

AdjR2h/urgl = the incremental explanatory power of book value and earnings valued at historical cost

Appendix F

261

Table F 9: Incremental Explanatory Power of Unrecognised Gain or Loss Beyond Book Value of Financial and Non-Financial Instruments and Earnings

Valued at Historical Cost Pit = α0 + α1BVNFIit+ α2Eit + α3TBFI + α4DIFFAit + α5DIFFLit + α6OBDIit + α7CINFV,it




h AdjR2

h,urgl AdjR2h AdjR2

urgl AdjR2 urgl/h AdjR2

h/urgl 1998 0.6600 0.6193 0.3505 0.0407 0.3095 1999 0.6872 0.5713 0.3103 0.1159 0.3769 2000 0.3939 0.3239 0.2951 0.0700 0.0988 2001 0.3144 0.3206 0.3467 -0.0062 -0.0323




value of financial liabilities. CINFV = component score of net fair value t = time i = firm AdjR2

h,urgl = the total explanatory power of book value and earnings valued at historical cost and unrecognised gain or loss of financial instruments

AdjR2h = the explanatory power of book value and earnings at historical cost

AdjR2urgl = the explanatory power of unrecognised gain or loss on financial instruments

AdjR2urgl/h = the incremental explanatory power of unrecognised gain or loss on financial instruments

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Documents

THE INFORMATION QUALITY OF DERIVATIVE DISCLOSURES …Mohamat Sabri Hassan B.Ac (Hons.) (Universiti Kebangsaan Malaysia), ... This thesis investigates the transparency or information