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
To analyze the risk and
return of Banking
Industry in Pakistan
To analyze the risk and
return of Cement
Industry in Pakistan
To analyze the External
factors affecting the
return of Cement & Banking
Industry in Pakistan
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
Time Span: 2000-2007
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
Time Span: 2010-2014
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
Pooled Regression Fixed Effect Random Effect
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
Pooled Regression Fixed Effect Random Effect
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
Pooled Regression Fixed Effect Random Effect
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.
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