Risk & Return Analysis - Stock Market in Pakistan

RISK & RETURN ANALYSIS OF STOCK MARKET IN PAKISTAN

Faculty Advisor:Ms. Tazeen Arsalan

Researchers:Abdul Hadi Khanani

Armoghan MoinQandeel Fatima Memon

Samra JavedSyed Jahanzeb Haider

Yamna ShumasZoya Talat

SPECIAL THANKS TO CDC

Key Purpose:

PROJECT OVERVIEW

To analyze the Risk & Return in Stock Market of Pakistan

OBJECTIVES OF THE STUDY

To analyze the risk and

return of Stock Market in Pakistan


return of Banking

Industry in Pakistan


return of Cement


To analyze the External

factors affecting the

return of Cement & Banking


Helps to stimulate the course of economic growth in a country.

Providing a connection between the savers and investors.

Motivating savings, investments and economic growth.

Stabilizing the prices of securities and providing benefits to savers.

CAPITAL MARKET_ IMPORTANCE

Plays a significant role in creating an investment opportunity for the public

Acts as an economic indicator.

Pricing financial securities.

Providing safe transactions.

Adds to economic growth.

Motivating savings.

STOCK MARKET_ IMPORTANCE

Banking Industry

Cement Industry

Industry Segmentation

BANKING TIMELINEYear Event2005 Reduced the corporate tax rate on banks from 58% to 35%2007 Benazir Bhutto's assassination cost Pakistan approximately $2

billion dollars in lost tax revenue2008 Global Financial Crisis2009 fiscal problems continued during 2008-09, Revenues fell, policy rate

declined by 100 bps 2010-2011 NPLs to gross advances of Pakistani Banks crossed the 14% limit,

resulting a decline in real GDP to 3.0 %.2012 SBP followed a tight monetary policy till August 2011 and the

interest rates were moving up, the banking spread remained high.2013 Mergers and consolidation of many financial institutions and weeding

out of several weaker banks from the financial system.

2014 Net Foreign Assets (NFA) of the banking sector witnessed an increase and reached to Rs.220.1 bn

2015 Banks reported strong earnings growth mainly due to major investment in high yielding long term Pakistan Investment Bonds (PIBs) and deposit growth.

CEMENT TIMELINEYEAR EVENTS

2005 Massive earthquake of 2005, No new taxes, developmental projects

2006 Cement sector grows by 12.1%

Enhanced install capacity and rise in local demand

Mainly exported to and UAE

Restoration of duty drawback on cement exports in which duty could be draw back at Rs 25.08 per ton on export of cement

2007 Prices increased again increasing profitability

Shortage in middle east and meant increase in exports

Most of the companies now shifted to coal as a replacement for basic fuel

2008 Cement exports increased by 65%

Fall in domestic demand due slow construction activity in the country

Abnormally low profit period for the sector

YEAR EVENTS

2009 CARTEL under APCMA

Highlighted by Monopoly Control Authority

2010 Sluggish local demand, increased competition in international market and a fall in profit margins

Broken promises of PPP govt, disruption of distribution channels due to floods

2011 Industry dispatched around record level of 23.947 million tons

Exports declined by 9%

Capacity utilization remained stagnant due to sluggish export demand

2012 Turnaround year for cement sector

Profitability increased by almost seven times

2013 Cement production declines due to power outages

Overall costs rising because of constant increase in international coal prices

2014 Local sales increased and exports contracted

Local sales increased because of increase in PSDP (540 Billion) budget by federal government

Construction of low cost housing schemes and dams to improve electricity provision

2015 Local Demand for cement increases by 12 %

Attock, Cherat, DG Cement and Lucky Cement announced their plans to increase production

Export market has shrunk because of high sales tax and excise duty

LITERATURE REVIEW

Title Author Year SampleReturn and Risk Analysis of Selected Sectorial Stocks and its Impact on Portfolio Selection

Dr. S. Poornima

2016 Country: India

Companies: Seven stocks selected from CNX 100 index

Time Span: 2012-13 to 2013-14

A Study on Risk and Return Analysis of Selected Stocks in India

Dr. S. Krishnaprabha and Mr. M. Vijayakumar

2015 Country: India

Companies: Banking, IT and Pharmacy

Time Span: 2010-2014

Risk and Return Tradeoff in Emerging Markets- Evidence from Dhaka Stock Exchange

Abu T. Mollik and M Khokan Bhaperi

2015 Country: Bangladesh

Companies:110 stocks at DSE General Index


Title Author Year SampleRisk and Return Relationship an Empirical Study of BSE Sensex Companies in India

Betanta Bora and Anindita Adhikary

2015 Country: India

Companies: Banking, IT and Pharmacy


An Analysis of Risk and Return in Equity Investment in Banking Sector

Dr. Ratna Sinha

2013 Country: India

Companies: Eight banks listed in Bankex

Time Span: July 2012 – December 2012

Return and Risk in Short Period Using Asset Pricing Model in Cement Industry of Pakistan

Muhammad Iklas Khan and Dr. Syed Zulfiqar Ali Shah

2012 Country: Pakistan

Companies: 18 cement companies

Time Span: January 2007 – December 2011

Title Author Year SampleRisk Uncertainty and Returns at the Karachi Stock Exchange

Ahmad A. Zaman

2010 Country: Pakistan

Companies: 11 sectors and 4 sub divisions

Time Span: July 1992 – March 1997

The Risk Return Relationship in the South African Stock Market

Leroi Reputsone

2009 Country: South Africa

Companies: Johanessberg stock exchange listed companies

Time Span: 1995-2009Risk and Return Nexus in Malaysian Stock Market: Empirical Evidence from CAPM

Abu Hassan, Md Isa and Chin-Hong Puah

(2009) Country: Malaysia

Companies:

Time Span: January 1995 until December 2006

Title Author Year SampleAn Empirical Analysis of Market and Industry Factors in Stock Returns of Pakistan Cement Industry

Muhammad Saeedullah and Dr. Kashif-ur-Rehman

(2005) Country: Pakistan

Companies: Seven companies listed on Karachi Stock Exchange

Time Span: 1998 to 2004

Analysis of Risk-Return Characteristics of the Quoted Firms in the Nigerian Stock Market

Abdullahi Ibrahim Bello and Lawal Wahab Adedokun

(2005) Country: Nigeria

Companies: Those firms that had December fiscal Year

Time Span: 2000 – 2004

LITERATURE REVIEW FOR EXTERNAL FACTORS

Paper related to

STOCK MARKET BANKING INDUSTRY

CEMENT INDUSTRY

Research Paper Factors Affecting Performance of Stock Market: Evidence from South Asian Countries

Macro Economic Determinants of the Stock Market Return: The Casein Malaysia

Factors Affecting Stock Returns of Firms Quotedin ISE: Market: A Dynamic Panel Data Approach

Author(s) Dr. Aurangzeb Heng Lee Ting, SimChuit Feng, Tee WeeWen, Wong Kit Lee

Sebnem Er, Bengu Vuran

Factors Mentioned InflationInterest RateExchange RateFDI

InflationInterest RateMoney Supply

InflationInterest RateMoney SupplyGDPExchange Rate

Factors added becausethey affect the stockmarket as a whole

FDIER

FDI

METHODOLOGY

SAMPLE FRAME There are currently 22 banks operating in

Pakistan. There are currently 24 companies operating

in the cement industry of Pakistan.

Sample Chosen 12 private banks 20Cement companies listed in KSE 100 Index

MODELCapital Asset Pricing Model (CAPM) –most

commonly used

Expected Return = Rf + β(Rm – Rf)

Rf: 3-month Treasury bill rate

Time period : January 2005 to December 2015.

QUANTITATIVE TECHNIQUESThe following regressions are performed in our

study- Pooled Fixed Random

Two models are developed Return vs risk Return vs risk and other external factors

HYPOTHESIS

H1: There is a positive

relationship between risk and return.

H2: There is no positive relationship between risk and return.

FINDINGS

BETA AVERAGE Cement Industry

Mean beta: 0.9515 Out of the total 20 cement companies, there are 8

companies which are below the mean and the remaining 12 are above the mean.

Banking Industry Mean beta: 0.9733 Out of the total 12 banks, there are 5 companies which

are below the mean and the remaining 7 are above the mean.

REGRESSION WITH EXTERNAL FACTORS R square in the original model fits the data

weakly. There are factors other than risk that affect

return of stocks in both cement and banking industries.

BANKING INDUSTRY CEMENT INDUSTRYInterest Rate Interest RateMoney Supply Money SupplyInflation Rate Inflation RateFDI FDIExchange Rate Exchange Rate

GDP

MODEL 1 (BANKING INDUSTRY)

Independent Variables Coef. Std. Err. t P>t Coef. Std. Err. T P>t Coef. Std. Err. z P>z

Risk 0.206849 .11726 1.76 0.08.2175569

.1451.50

0.136

.2068494 .11726561.76

0.078

_cons 0.016848.118002

0.14 0.88 .0064259 .14455480.04

0.965

.0168475 .11800210.14

0.886

Pooled Regression Fixed Effect Random Effect

Adj R square 0.0159 R square R square

Within 0.0186 Within 0.0186

Overall 0.0234 Overall 0.0234

F test 3.11 F test 2.25

REGRESSION RESULTS FOR BANKING INDUSTRY Model 1: Risk and Return Relationship

R square is approximately: 1.6% in pooled regression, 1.9% in fixed effect 1.9% in random effect

Beta enjoys significant and positive relationship with return under all models.

Pooled or Fixed: F-test F test probability in fixed effect (2.25) is

insignificant, it is inferred that Pooled regression (with a F test probability of 3.11) is better than Fixed Effect.

Pooled or Random Effect: Chi Square Test Since chi square is not significant (0.00), it is

inferred that pooled regression results are more reliable than random effect results.

MODEL 2

Independent Variables

Coef. Std. Err.

t P>t Coef. Std. Err.

t P>t Coef. Std. Err. z P>z

Risk 0.1902 0.0641 2.96000. 0.0040 0.19620 0.08444 2.32 0.022 0.19023 0.06417 2.9600 0.003

Exchange Rate

-0.6798 0.4174 -1.6300 0.1060 -0.68166 0.43562 -1.56 0.120 -0.67983 0.41746 -1.6300 0.103

Inflation -2.0239 0.5186 -3.9000 0.0000 -2.02253 0.54103 -3.74 0.000 -2.02394 0.51869 -3.9000 0.000

Foreign Direct Investment

0.4990 0.0462 10.790 0.0000 0.49894 0.04824 10.34 0.000 0.49902 0.04626 10.790 0.000

Interest Rate 9.2014 1.3858 6.64000. 0.0000 9.16291 1.48838 6.16 0.000 9.20420 1.38582 6.6400 0.000

Money supply

0.5292 0.0338 15.6600 0.0000 0.52935 0.03525 15.02 0.000 0.52930 0.03380 15.660 0.000

_cons -0.4332 0.0976 -4.4400 0.0000 -0.4363 0.10534 -4.14 0.000 -0.43327 0.09765 -4.4400 0.000


Adj R square 0.7833 R square R square Within 0.7836 Within

0.7836 Overall 0.7833 Overall

0.7833

F test 75.30 F test 9

• Model 2:Interest Rate has the major impact

R square: Pooled regression: 78.33% The value of F is 75.3% indicating that the model is

ery good Fixed effect regression: 78.3% The value of F is 68.8%, which also indicates that the

model is very good Random effect regression: 78.3%

Pooled or Fixed: F-test F test is 9% which makes it insignificant. It

proves that Pooled Regression result is more reliable than Fixed Effect result.

Pooled or Random: Chi Square Test Since probability of chi square (0.00) is

insignificant, it is inferred that Pooled regression results are less biased than Random Effect.

Independent Variables Coef.

Std. Err. T P>t Coef.

Std. Err. t P>t Coef.

Std. Err. z P>z

Risk 0.199 0.050 4.01 0.000 0.280 0.077 3.600 0.000 0.199 0.050 4.01 0.000

_cons 0.045 0.051 0.87 0.338 -0.033 0.077 -0.430 0.667 0.045 0.051 0.87 0.837


Adj R square 0.0645 R square R square Within 0.062 Within 0.063Overall 0.069 Overall 0.069

F test 16.09 F test 13.27

Model 1: Risk and Return Relationship CEMENT INDUSTRY

REGRESSION RESULTS FOR CEMENT INDUSTRY Model 1: Risk and Return Relationship R square is approximately:

6.5% in pooled regression, 6.2% in fixed effect 6.3% in random effect

Beta enjoys significant and positive relationship with return under all models.

Pooled or Fixed: F-test F test probability in fixed effect (13.27) is

insignificant, it is inferred that Pooled regression (with a F test probability of 16.09) is better than Fixed Effect.

Pooled or Random Effect: Chi Square Test Since chi square is not significant (0.00), it is

inferred that pooled regression results are more reliable than random effect results.

Independent Variables

Coef. Std. Err.

t P>t Coef. Std. Err.

T P>t Coef. Std. Err. z P>z

Risk 0.1764 0.0331. 5.3300. 0.0000 0.2452. 0.0550. 4.460 0.0000. 0.1764 0.03312015 5.33 0.000

Exchange Rate

-0.4689 0.4232. -1.1100. 0.2690 -0.5780. 0.4435. -1.300 0.1940. -0.468926 0.4231651 -1.11 0.268

GDP 5.5320 3.4030. 1.6300. 0.1060 6.4192. 3.5671. 1.8000. 0.0730. 5.53201 3.402984 1.63 0.104

Inflation

-0.3405 0.8984. -0.3800. 0.7050 0.0634. 0.9638. 0.0700. 0.9480. -0.3405335 0.8983812 -0.38 0.705

Foreign Direct Investment

0.4417 0.0501. 8.8200. 0.0000 0.4366. 0.0520. 8.4000. 0.0000. 0.4417107 0.0500813 8.82 0.000

Interest Rate

11.957 2.3931. 5.0000. 0.0000 12.0555. 2.4788. 4.8600. 0.0000. 11.95751 2.393059 5.00 0.000

Money supply

0.4190 0.0345. 12.1400. 0.0000 0.4223. 0.0358. 11.8000. 0.0000. 0.4190384 0.0345175 12.14 0.000

_cons -0.9774 0.3548. -2.7500. 0.0060 -1.1207. 0.3782. -2.9600. 0.0030. -0.97735 0.3548086 -2.75 0006


Adj R square 0.6201 R square R square Within 0.6333 Within 0.6305

Overall 0.6258 Overall 0.6323 F test 52.07 F test 25

MODEL 2

• Model 2:Interest Rate has the major impact

R square: Pooled regression: 62.01% The value of F is 52% indicating that the model is

ery good Fixed effect regression: 62.6% The value of F is 47.63%, which also indicates

that the model is very good Random effect regression: 63%

LIMITATIONS OF THE STUDY Only took two sectors.

Only limited 11 years of data is included.

Effect of stock market clashes were not analyzed in detail.

Data of only private banks operational after 2002 is used in the study.

THANK YOU

Data & Analytics

Risk & Return Analysis - Stock Market in Pakistan