This document analyzes the risk and return of the stock market, banking industry, and cement industry in Pakistan from January 2005 to December 2015. It finds: 1) For the banking industry, the capital asset pricing model (CAPM) explains 1.6-1.9% of the variation in returns. Risk (as measured by beta) has a positive relationship with return. A second model incorporating external factors like interest rates, inflation, and money supply explains 78.3% of returns. 2) For the cement industry, CAPM explains 6.2-6.5% of returns. Again, higher risk equates to higher returns. 3) Key factors affecting banking industry returns are interest

1. RISK & RETURN ANALYSIS OF
STOCK MARKET IN PAKISTAN
Faculty Advisor:
Ms. Tazeen Arsalan
Researchers:
Abdul Hadi Khanani
Armoghan Moin
Qandeel Fatima Memon
Samra Javed
Syed Jahanzeb Haider
Yamna Shumas
Zoya Talat

2. SPECIAL THANKS TO CDC

3.  Key Purpose:
To analyze the Risk & Return in Stock Market
of Pakistan

4. 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

5. 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

6. 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

7. Banking
Industry
Cement
Industry
Industry Segmentation

8. BANKING TIMELINE
Year Event
2005 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 revenue
2008 Global Financial Crisis
2009 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.

9. CEMENT TIMELINE
YEAR 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

10. 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

11. LITERATURE REVIEW

12. Title Author Year Sample
Return 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

13. Title Author Year Sample
Risk 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

14. Title Author Year Sample
Risk 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: 1995-2009
Risk 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

15. Title Author Year Sample
An 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

16. 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 Case
in Malaysia
Factors Affecting Stock
Returns of Firms Quoted
in ISE: Market: A Dynamic
Panel Data Approach
Author(s) Dr. Aurangzeb Heng Lee Ting, Sim
Chuit Feng, Tee Wee
Wen, Wong Kit Lee
Sebnem Er, Bengu Vuran
Factors Mentioned Inflation
Interest Rate
Exchange Rate
FDI
Inflation
Interest Rate
Money Supply
Inflation
Interest Rate
Money Supply
GDP
Exchange Rate
Factors added
because
they affect the stock
market as a whole
FDI
ER
FDI

17. METHODOLOGY

18. 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

19. MODEL
Capital 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.

20. QUANTITATIVE TECHNIQUES
The following regressions are performed in our study-
 Pooled
 Fixed
 Random
Two models are developed
 Return vs risk
 Return vs risk and other external factors

21. HYPOTHESIS
H1: There is
a positive
relationship
between risk
and return.
H2: There is
no positive
relationship
between risk
and return.

22. FINDINGS

23. 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.

24. 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 INDUSTRY
Interest Rate Interest Rate
Money Supply Money Supply
Inflation Rate Inflation Rate
FDI FDI
Exchange Rate Exchange Rate
GDP

25. 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 .145 1.50 0.136 .2068494 .1172656 1.76 0.078
_cons 0.016848 .118002 0.14 0.88 .0064259 .1445548 0.04 0.965 .0168475 .1180021 0.14 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

26. 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.

27.  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.

28. 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

29. • 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%

30.  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.

31. 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 0.063
Overall 0.069 Overall 0.069
F test 16.09 F test 13.27
Model 1: Risk and Return Relationship
CEMENT INDUSTRY

32. 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.

33.  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.

34. Indepe
ndent
Variabl
es
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
Exchan
ge 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
Inflatio
n
-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
Investm
ent
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

35. • 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%

36. 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.

37. THANK YOU
