Effect of Leverage on Expected Stock Returns and Size of the Firm
av
1. Impact of Macro-economic variables on stock prices
in Pakistan
Ahmad Waleed-14F-9217
Muhammad Hashaam Aslam- 14F-9201
Date: 08-08-2015
FAST School of Management
National University of Computer and Emerging Sciences
2. 1
Contents
1. Introduction.............................................................................................................5
1.1. Problem statement...........................................................................................7
1.2. Research Question...........................................................................................7
1.3. Hypotheses ......................................................................................................7
2. Review of Literature ...............................................................................................8
2.1. Identification of Variables.............................................................................14
2.1.1. OilPrices (OP)........................................................................................14
2.1.2. Exchange Rate (E) .................................................................................14
2.1.3. Interest Rate (R).....................................................................................15
2.1.4. Inflation Rate (I).....................................................................................15
3. Methodology.........................................................................................................16
3.1. Description of Tests ......................................................................................16
3.1.1. Unit Root................................................................................................16
3.1.2. Stationarity.............................................................................................16
3.1.3. Co-Integration........................................................................................16
3.1.4. Vector error correction...........................................................................17
3.2. Description of variables ................................................................................17
3.2.1. Exchange rate (E)...................................................................................17
3.2.2. Inflation rate (I)......................................................................................17
3.2.3. Oil price (OP).........................................................................................18
3.2.4. Interest rate (R) ......................................................................................18
4. 3
Acknowledgements
All the praises and acclamations for Allah Almighty the benevolent that know
mysteries and secrets of universe and his Holy Prophet (S.A.W) whose blessings
enabled me to pursue higher goals of life and whose teachings have served as beacon
for the humanity in the hours of despair and darkness.
I am heartily thankful to my supervisor Umer Iqbal whose encouragement, guidance
and support from the initial to final level enabled me to develop an understanding of
the subject. He has left no stone unturned during supervision of this project. I would
also like to acknowledge my dear friends for their encouragement and corporation.
Last but not least, ordinary words of appreciation do not cover my parent’s true love
and their guidance at every corner of my life. Their keen interest, prayers and
encouragement have been a very strong support for me and enabled me to finish my
project in time. I also owe my thanks to all others who encouraged and helped me
throughout my research work.
5. 4
Abstract
This study is purposed to investigates the relationship of macro-economic variables
i.e. oil prices, inflation, interest rate, exchange rate with stock prices.The authors
analyzed the relationship of macro-economic variables evidences from past six year’s
monthly data by taking 72 observations for each variable. The data is analyzed by
testing pairwise Granger causality, Johanson’s Co-integration, Likelihood Co-
integration and Vector Error Correction Model (VECM).Pair wise Granger causality
results causal relationship of interest rate and exchange rate with stock prices,
whereas inflation rate and oil prices do not cause stock prices. Likelihood Co-
integration finds long term and significant relationship between macro-economical
variables and stock prices. Johanson’s Co-Integration results three CO-integrating
vectors having probability value less than 5%. However, VECM and Co-Integration
test suggests insignificant negative relationship of Oil Prices and Inflation with stock
prices, although insignificant positive relation of Exchange rate and Interest rate with
Stock prices in short run. Inversely, it reveals significant positive relationship of Oil
Prices and Inflation with stock price and significant negative relation of Exchange rate
and Interest rate with Stock prices in long term.
Keywords: Stock prices, Inflation rate, Oil Prices, Interest rate, Exchange rate, Co-
integration, Vector Error Correction.
6. 5
1. Introduction
Stock market is place where buyers and sellers meets in order to purchase or sale
securities like shares. These securities may include securities listed in market or also
the non-listed also be traded. In Pakistan, there are three stock exchanges which
include “Karachi Stock Exchange”, “Lahore Stock Exchange” and “Islamabad Stock
Exchange” and their sizes are $75Billion, $36Million and $4Million respectively
according to Market Capitalization. Pakistani Stock Exchanges are trading stocks of
more than 1500 companies which include small, medium and large sizes. The major
market in Pakistan for stock trading is Karachi Stock Exchange (KSE) having 579
companies listed. In this study the main focus is to find out macro-economical
influences on stock markets of Pakistan. These are Interest Rate, Inflation Rate,
Exchange Rate and Oil Prices. Interest rate is an important variable which may
influence the stock prices and returns. In Pakistan the basic interest rate is Karachi
Inter-Bank Offer Rate (KIBOR). Monetary policy or interest rates affect stock prices
in US and Canadian stock markets (Li, Iscan and Xu, 2007). Although, there is an
empirical relationship between stock prices and interest rate (Alam and Uddin, 2009).
Exchange Rate refers to the rate of a currency in another currency. These currencies
are traded in Foreign Exchange Market (also known as FOREX) where different
institutional buyers and sellers undergo the process of sale and purchase of currencies.
Exchange rate is responsible to influence positively and significantly on stock market
index (Jiranyakul, 2009). There is a positive and significant influence of exchange
rate on stock prices (Sohail and Hussain, 2009).Increased inflation rate influences the
whole economy on a large scale. Inflation results downfall in value of money and
increases its supply. In South African stock market, Inflation influences significantly
on stock returns (Gupta and Reid, 2013). However, the Oil Price Index is also an
7. 6
important variable that influences on stock market. Oil market is the most volatile
market, because its response to the minor change results a major fluctuation in oil
prices. Nigeria is an oil exporting country, so there is an insignificant and positive
effect of oil prices shocks on stock market returns. Oil price poses negative impact on
stock market returns and its prices following up by time and nature of its changes
(Babatunde and Adenikinju, 2013).
There is high volatility in oil prices from past 2-3 years and these variables play an
important role in economy whereas in Pakistan high fluctuating inflation rate,
exchange rate and interest rate are changing the economic direction. These changes in
economic directions are directly or somewhere indirectly influencing the stock
markets. The stock markets in Pakistan are on its boom now a day and these markets
are performing much better from past years. The impact of these fluctuations in
economic variables needs to be defined in context of boom of stock markets of
Pakistan. There are some objectives regarding our study.
Find out the effects of inflation on Karachi stock exchange, Pakistan.
Find impact of oil prices on stocks when oil prices are very low and stock
market is on its boom.
Investigate the volatility of Karachi stock exchange against volatile exchange
rates, and interest rate.
Oil prices has experienced a historical low in past 2-3 years, this study is conducted
significantly to reveal the influences on Pakistan’s stock markets which are now a day
experiencing a high growth. Previously the researchers have focused on variables like
interest rate, inflation, exchange rate and other macro-economic variables like money
supply, industrial production index and political uncertainty. No previous study is
8. 7
conducted on impact of these four variables cumulatively on stock prices providing
evidences from last six (6) years.
1.1.Problem statement
Stock markets in Pakistan are trending a high growth from previous years while
macro-economic variables i.e. oil prices, exchange rate, interest rate and inflation rate
are experiencing a downfall, so there must be a relationship between stock prices and
above mentioned macro-economic variables.
1.2.ResearchQuestion
Whether stock prices in Pakistan are being affected by macro-economic variables i.e.
oil prices, exchange rate, interest rate and inflation rate?
1.3.Hypotheses
H1: There is significant impact of Oil Price Index on Stock Market.
H2: There is significant impact of inflation on Stock Market.
H3: There is significant impact of interest rate on Stock Market.
H4: There is significant impact of exchange rate on Stock Market.
9. 8
2. Review of Literature
Stock market is an essential component to represent state of an economy. It attracts
investors and allows companies to be listed and raise wealth of their owners. In
contrast of developed economies stock exchanges in developing or emerging
countries represent a higher volatility (Engel and Rangel, 2005). However there are
some major macro-economic variables that influence stock markets and its prices.
These include industrial production index, money supply, short term interest rate (T-
Bills rate), exchange rate, unemployment rate, inflation rate (Consumer price index),
oil price index, gold prices and political uncertainty. In a case of Indian stock
exchange (BSE Sensex) under Johansen co-integration model and vector error
correction model (VECM) analyses, there is a positive relationship of stock prices
with industrial production and money supply. While, stock prices are negatively
influenced by inflation rate (CPI). Short term interest rate and exchange rate are
insignificantly related with stock prices(Naik and Puja 2012). Theory suggests that
inflation rate, money supply, interest rates and other macro-economical factors are
exclusively important to understand behaviors and trends of stock prices and also
keeps in account the movement in exchange rate. After studying a wide range of
empirical evidences it is affirmed that there is an absence of any long term
relationship between stock prices and exchange rate (Bahmani et al. 1992) (Nieh &
Lee, 2001), but there is short term influence of exchange rates on stock prices(Kutty
2010). In a comparison of twelve countries nexus of exchange rate and stock prices,
through interpretation with vector error correction model (VECM) and Granger
methods for causality test, seven countries resulted long term linkage between stock
prices and exchange rate (Chen and Chen 2012). However, other countries exchange
rate and stock prices represents a short term linear and non-linear relationship.
10. 9
Monetary policy or interest rates affect stock markets in US and Canadian stock
markets (Li, İşcan and Xu 2007). Small economies (Canada) rely mostly on small
number of industries. In contrast, larger economies (United Sates) have highly
diversified industrial structures that are contributing in their economies. Therefore,
interest rate fluctuation leads to instant small and dynamically brief response in
Canada. Inversely, interest rate fluctuation is a reason for instantly large and
dynamically pro-longed response in US stock market and therefore, similar effect on
stock prices. Although, there is an empirical relationship between stock prices and
interest rate and also there are relationships between stock price fluctuation and
fluctuation in interest rates (Alam and Uddin 2009). Interest rate is actually an
expense to use funds by borrower and it is an income for the lender. Logically, when
interest rate is on its peak will lead an investor towards term deposits in banks. On the
other hand, if interest rate declines this forces the investors to try their luck by
investing in stock market. Similarly, the interest rate sensitivity of assets and
liabilities of any company varies because of different timings and profit generation
capabilities. If assets and liabilities of any company are more sensitive to interest rate
variations it means there is a higher risk and also responsible for higher expected
return (Park and Choi 2011). This will lead to reliance of investor to invest in stocks
and to place higher bids which will eventually increase stock prices. Thus, stock
prices will tend to increase when interest rate stands on a higher side. But stock
market is actually is a risky place for investment. Yet it surprises its investors by
interesting combinations of risk and returns, also forces its investors to predict and
analyze trends in order to gain abnormal profits and to avoid risk situation. Macro-
economical variables that affects on stock prices in Vietnam include industrial
production and interest rate (Hussainey and Ngoc 2009). Vietnamese stocks are
11. 10
influenced by US macro-economical variables. Interest rates didn’t affect stock price
but industrial production influences positively and significantly on stock prices.
Similarly US macro-economic indicators also have a significant impact on
Vietnamese stock prices. Also, there is a significant impact of US stock market news
on Vietnamese stock market and its prices (Nguyen 2011). Although in Thailand case,
Johansen co integration test suggests a strong and positive effect of industrial
production on stock prices, money supply poses positive effect, inflation rate reflects
negative and exchange rate is responsible to influence positively and significantly on
stock market index (Jiranyakul 2009). However, evidences from Lahore stock
exchange (LSE, Pakistan) macro-economic indicators i.e. industrial production index,
real effective exchange rate, money supply, interest rate and consumer price index
(assume for inflation rate) effect on stock prices (Sohail and Hussain 2009). The
effects of industrial production index and interest rate on stock price are in a similar
way that in Vietnamese stock prices (mentioned previously). Hence, there is a positive
and significant influence of industrial production index, real effective exchange rate
and money supply on stock prices. While, inflation rate (CPI) causes a negative
impact on stock returns. Moreover, in case of Karachi stock exchange (KSE) in
Pakistan the macro-economic variables effects the stock prices along with company
fundamentals which includes company size, previous year earnings per share and
behavior of stock. Company fundamentals are most important factors to influence
stock prices (Nisa and Nishat 2012). There is strong and positive relationship between
stock prices and previous year stock prices. Whereas, other macro-economic variables
i.e. GDP and interest rate has significant relationship with stock prices. However,
there is a minor and negative influence of inflation rate on stock prices. Although in
UAE index researchers found relationships between stock prices and macro-
12. 11
economical variables. Hence consumer price index (Inflation rate) and interest rate
influence significantly and negatively on stock prices. Whereas, the impact of
earnings per share is strong on stock prices (Tamimi et al 2011). While studying
South African stock market JSE (Johannesburg stock exchange) with an application
of Nelson, 1991 GARCH model macro-economic variables like real GDP, interest
rate, inflation rate, stock prices in US and yield on US Govt. bonds influencing
significantly on stock prices (Yu Hsing 2011). Real GDP and stock prices in US are
bounded positively and significantly to the stock prices, on the other hand stock prices
are negatively affected by interest rate, inflation rate and US Govt. bonds. Followed
by BVAR technique of analysis of South African stock market, consumer price index
influences significantly on stock returns. While, monetary policy shocks represent a
negative but consistent relationship with stock market returns (Gupta and Reid 2013).
CPI and monetary policy showed significance at both sector and aggregate levels.
However, monthly data based BVAR model expresses significant influence of CPI,
also at aggregate of stock returns for smaller intervals right after CPI and monetary
policy shocks. In Istanbul stock exchange (ISE) Macro-economic variables such as
interest rate, inflation, risk premium, money supply and exchange rate with an
addition of unemployment rate effects stock return (Rjoub et al 2009). This effect can
be termed as a pricing relationship but due to weak ability to explain these
relationships it is affirmed that other than these factors there are some macro-
economical variables that are strongly influencing stock market and so as stock prices.
Although in the scenario of New York stock exchange there are four most crucial
macro-economic variables that influence stock prices are; inflation, money supply,
GDP and unemployment. The most influencing variable among all four variables is
money supply and it affects stock prices positively (Shiblee 2009). The second most
13. 12
influencing component on stock prices is inflation but its direction is uncertain for
different companies. According to a proxy hypothesis, the real economic activity has
a negative relationship with inflation while, stock returns also respond negatively to
inflation rate. Ultimately, real economic variables impact positively on stock prices
(Fama, 1981). Therefore, after some empirical tests it is affirmed that stock returns are
related negatively with employment growth and positively with GDP growth (Merikas
and Merika 2006). High employment leads people to spend more money which is a
major reason for inflation. So, there is a negative relationship between stock returns
and inflation rate. It also supports stock prices will be reduced due to high economic
activity in a boom while it will increase in a declining economy (Mcqueen & Roley,
1993). Right after financial slump of 2008, in an impact study of macro-economic
indicators on stock prices, it is founded that when macro-economical indicators
decline this will lead a serious decline in performance of stock market. By applying
SVAR model, it is stated that macro-economic indicator and stock prices are directly
related to each other (Velinov and Chen 2015). Along with other macro-economic
variables political situation of countries is also need to be analyzed and its effect on
stock prices. The effects of political uncertainty on stock markets with a sample frame
of different developing and developed countries resulted in different conclusions.
Majorly, there is no significance between political uncertainty and stock price. In
addition to that, political factors may impact on stock prices if it crosses a certain
point or level (Chang et al 2015). Like political uncertainty oil price is a vital macro-
economic variable. Like other macro-economic variables it has also impact on stock
prices. Previously, researchers which were examining oil prices shocks and their
effects on stock market returns were not so attracted towards oil exporting countries.
The main focus was largely at oil importing countries. In case of Nigeria there is an
14. 13
insignificant and positive effect of oil prices shocks on stock market returns
(Babatunde and Adenikinju 2013). Also, when oil prices are excessive volatile it
shows negative impact on stock market volatility. However, interest rates in Nigeria
express a stronger effect of oil prices shocks rather than stock returns. Because,
Nigeria is oil exporting country and its economy is very much dependent upon
proceeds from oil exports. That is why when oil prices become volatile internationally
it affects stock prices and volatility scenarios of oil prices leads to increased volatility
of stock prices in Nigeria. Whereas, in Ghana where the economy is based upon cocoa
and gold exports, Cocoa price volatility increases volatility in stock prices and
volatility in gold prices leads to decrease in stock price volatility(Adjasi 2009). In an
analysis EGARCH model and taking macro-economic variables i.e. inflation rate
(CPI), interest rate, oil prices, exchange rate and money supply, the relationship of
volatility between interest rate and stock prices is positive while money supply and oil
prices volatility impact negatively on stock prices. Smaller economies reflect a higher
impact of their basic commodities and their volatility. Although in above mentioned
citations of Nigeria (in certain situations) and Ghana oil price poses significant
relationship with stock market returns and its prices. However, in India the situation is
almost turned around, there is a positive and long term relationship between
movement of market indices and oil prices, though there is no significant causal
impact of oil prices on Indian stock market and its prices (Bandopadhyay and Mondal
2014). Examining cross correlations of stock returns in South Asian Association for
Regional Co-operation (SAARC) countries and their volatility founds positive returns
on all stock markets (Singhania and Prakash 2014), also stock markets in stock
countries generate returns highly in comparison to other global stock markets.
15. 14
2.1.Identification of Variables
2.1.1. Oil Prices (OP)
Oil market is the most volatile market, because its response to the minor change
results a major fluctuation in stock prices. Oil price poses insignificant effect on stock
market returns and its prices (Babatunde and Adenikinju 2013). In this research,
quarterly-based data of Oil Prices for 13 years (1995-2008) was analyzed and Oil
Prices were extracted by taking Log of these values. However, in India there is no
significant causal impact of oil prices on Indian stock market and its prices
(Bandopadhyay and Mondal 2014). Data was from 2001 to 2013 daily-based and
taken from Bombay Stock Exchange, Ministry of Petroleum and National Stock
Exchange.
2.1.2. Exchange Rate (E)
Exchange Rate refers to the rate of a currency in another currency. These currencies
are traded in Foreign Exchange Market (also known as FOREX) where different
institutional buyers and sellers undergo the process of sale and purchase of currencies.
Exchange Rate is responsible to influence positively and significantly on stock market
index (Jiranyakul 2009). There is an absence of any long term relationship between
stock prices and exchange rate (Bahmani et al. 1992) (Nieh & Lee, 2001), but there is
short term influence of exchange rates on stock prices (Kutty 2010). This data was
from 1989 to 2006 based on DOW-Jones news and weekly closing data from Mexico
Equity Index. The Exchange Rate data was obtained from International Monetary
Market of 849 points. However, in a comparison of twelve countries nexus of
Exchange Rate and Stock Prices, seven countries result long term linkage between
stock prices and exchange rate (Chen and Chen 2012). However, other countries
16. 15
exchange rate and stock prices represents a short term linear and non-linear
relationship.
2.1.3. Interest Rate (R)
Interest rate is actually an expense to use funds by borrower and it is an income for
the lender. Logically, when interest rate is on its peak will lead an investor towards
term deposits in banks. On the other hand, if interest rate declines this forces the
investors to try their luck by investing in stock market. Although, there is an empirical
relationship between stock prices and interest rate (Alam and Uddin 2009). They
analyzed data from 1988 to 2003 monthly. As sample of interest rate they took Bank
Deposit Rate (offered to general Public) and Stock Returns are calculated by taking
Log of monthly averages of stock prices.
2.1.4. Inflation Rate (I)
Inflation rate influences the whole economy on a large scale. Increased Inflation
results downfall in value of money and increases its supply. In South African stock
market, Inflation influences significantly on stock returns (Gupta and Reid 2013).
One of most influencing components on stock prices is inflation but its direction is
uncertain for different companies. According to a proxy hypothesis, stock returns
respond negatively to inflation rate (Shiblee 2009).. They used two Indexes to
measure the Inflation Rate which are Consumer Price Index (CPI) and Producer Price
Index (PPI). Data was gathered from New York Stock Exchange (NYSE) and US
Federal Reserve Bank for 1990 to 2007 time period.
17. 16
3. Methodology
3.1.Description of Tests
For analysis of our variables unit root, Co-integration and Vector Error Correction
tests are applied. The description of these tests is discussed below.
3.1.1. Unit Root
This study is constituted of some macro-economic variables and their effect on Stock
Prices. Economic data is often non-stationary or unit root due to variation in values
between different periods. Unit root is a problem in a time series data which shows a
variant picture of that data and also causes hurdles in statistical inference. In order to
nullify the effect of unit root we run Augmented Dickey-Fuller (ADF) test which
enhanced the data and cancelled the unit root effect on First(1) Difference Level.
3.1.2. Stationarity
i. E(Yt ) = Constant = µ
ii. VAR(Yt ) = δ2
iii. COV(Yt ,Yt-j) = δj
3.1.3. Co-Integration
Co-integration is defined when the error term in the regression modeling is stationary.
It has become an important property in contemporary time series analysis. Co-
integration actually finds relevant data points which can be used to check causality
relation of one variable with other one. Co-integration analysis provides a framework
for estimation, inference, and interpretation when the variables are not covariance
stationary. Instead of being covariance stationary, many economic time series appear
to be “first-difference stationary”. This means that the level of a time series is not
stationary but its first difference is First difference stationary processes are also
18. 17
known as integrated processes of order 1, or I (1) processes. Covariance-stationary
processes are I(0).
3.1.4. Vector error correction
A vector error correction model (VECM) can lead to a better understanding of the
nature of any non-stationarity among the different component series and can also
improve longer term forecasting over an unconstrained model.
3.2.Description of variables
3.2.1. Exchange rate (E)
Exchange Rate refers to the rate of a currency in another currency. These currencies
are traded in Foreign Exchange Market (also known as FOREX) where different
institutional buyers and sellers undergo the process of sale and purchase of currencies.
In this study the exchange rate is used in comparison of Pakistani Rupees (PKR) with
US Dollar (USD). The sample of data is of last 6 year record, jan 2009-Dec 2014 with
total 72 observations on monthly basis. The data was collected from forex.com.
3.2.2. Inflation rate (I)
Inflation rate influences the whole economy on a large scale. Increased inflation
results downfall in value of money and increases its supply. Some researcher also
writes inflation as increase in general price level. Inflation of last 6 years is taken as a
sample. The data collected was on monthly basis from jan 2009-Dec 2014 with total
72 observations. The data based on inflation in Pakistan was collected from trading
economics.com.
19. 18
3.2.3. Oil price (OP)
Oil Price Index is also an important variable that influences on stock market. Oil
market is the most volatile market, because its response to the minor change results a
major fluctuation in oil prices. International oil prices (US Dollar per barrel) was
collected of last 6 years on daily basis. Averages are calculated to convert daily data
to monthly data. After converting data to monthly basis the total number of
observation become 72.
3.2.4. Interest rate (R)
Interest rate is an important variable which may influence the stock prices and returns.
Interest rate is actually an expense to use funds by borrower and it is an income for
the lender. Logically, when interest rate is on its peak will lead an investor towards
term deposits in banks. On the other hand, if interest rate declines this forces the
investors to try their luck by investing in stock market. In Pakistan the basic interest
rate is Karachi Inter-Bank Offer Rate (KIBOR). But in our research we take interest
rate issued by State bank of Pakistan and offered to general public with some banking
spread. Monthly data was collected of last 6 years with same observation like other
variables.
3.2.5. Stock price (SP)
Stock market is place where buyers and sellers meets in order to purchase or sale
securities like shares. Stock market is an essential component to represent state of an
economy. It attracts investors and allows companies to be listed and raise wealth of
their owners. As a sample data of stock prices we take KSE 100 index as our sample.
The data was collected on monthly basis of last 6 years. KSE 100 index is a market
indicator which represents top 100 capitalized companies trading. KSE 100 index also
20. 19
indicates the growth and decline in stock markets. The data of KSE 100 index was
taken from yahoofinance.com.
4. Results and discussion
In this study we have analyzed the time series data with Johansen’s Co-integration,
Granger Causality test and Vector Error Correction Model (VECM). The analyses and
results are discussed as under.
4.1.Historical Trends of Variables
Figure: 1
The Figure 1 defines graphically the historical trends of Stock Price (SP), Exchange
Rate (E), Interest Rate (R), Inflation Rate (I) and Oil Price (OP). The data shows
stochastic trends of variables. Stock prices and Exchange rate in graphs represents
increasing trends. Whereas, rate of Interest and inflation shows decline. Oil prices
shows high variation and on declining now a days.
5000
10000
15000
20000
25000
30000
35000
5 10 15 20 25 30 35 40 45 50 55 60 65 70
S P
76
80
84
88
92
96
100
104
108
5 10 15 20 25 30 35 40 45 50 55 60 65 70
E
8
9
10
11
12
13
14
15
16
5 10 15 20 25 30 35 40 45 50 55 60 65 70
R
0
4
8
12
16
20
24
5 10 15 20 25 30 35 40 45 50 55 60 65 70
I
30
40
50
60
70
80
90
100
110
120
5 10 15 20 25 30 35 40 45 50 55 60 65 70
O P
21. 20
4.2.Granger causality
The Granger causality test is a statistical hypothesis test for determining whether one
time series is useful in forecasting another. Time series X is said to Granger-cause Y if
it can be shown, usually through a series of t-tests and F-tests on lagged values of X
(and with lagged values of Y also included), that those X values provide statistically
significant information about future values of Y.
Granger defined the causality relationship based on two principles:
1. The cause happens prior to its effect.
2. The cause has unique information about the future values of its effect.
4.2.1. Pairwise Granger Causality Test Graphs
Figure: 2
The above graph represents three co-integrating vectors and their mean points. All the
vectors have high variation around their mean in time series.
22. 21
4.2.2. Pairwise Granger Causality Test Results
Table: 1
Pairwise Granger Causality Test Results
Null Hypothesis: Obs F-Statistic Probability
E does not Granger Cause SP 68 3.5913 0.01094
SP does not Granger Cause E 0.68027 0.60838
R does not Granger Cause SP 68 2.13998 0.08707
SP does not Granger Cause R 1.07036 0.37932
I does not Granger Cause SP 68 0.69958 0.59533
SP does not Granger Cause I 2.29093 0.0702
OP does not Granger Cause SP 68 1.1618 0.3369
SP does not Granger Cause OP 0.79321 0.53432
According to our study we have selected Four (4) Independent Variables and One (1)
Dependent Variable which resulted in creation Four (4) Null Hypotheses. After a test
of Pairwise Granger Causality (at significance level of 10%) it is affirmed that
Exchange Rate (E) has significant effect on Stock Price (SP) and Interest Rate (R)
also causes significantly on Stock Price (SP). Inversely, Inflation Rate (I) and Oil
Price (OP) cause Stock Price (SP) insignificantly. Two of Null Hypotheses were
rejected and others were accepted following Pairwise Granger Causality test.
23. 22
4.3. Co-Integration
4.3.1. Co-Integration Equations
Table: 2
Vector Error Correction Estimates
Cointegrating Eq: CointEq1 CointEq2 CointEq3
SP(-1) 1.000000 0.000000 0.000000
R(-1) 0.000000 1.000000 0.000000
E(-1) 0.000000 0.000000 1.000000
I(-1) 4019.458 -0.61547 2.336311
(660.357) (0.10824) (0.54360)
[ 6.08680] [-5.68609] [ 4.29786]
OP(-1) -43.4435 -0.00344 -0.21223
(140.162) (0.02297) (0.11538)
[-0.30995] [-0.14960] [-1.83937]
C -60358.6 -5.56856 -95.4751
(18503.8) (3.03302) (15.2321)
[-3.26196] [-1.83598] [-6.26800]
In this model we have normalized variables to stock prices (SP) as stock prices is our
variable of interest. This table shows values of Co-efficient, Standard Error and t-
statistics. The Co-integration equation 1 represents a positive and significant
relationship between inflation rate (I) and stock prices (SP), while an insignificant
positive impact of oil prices on stock prices.
24. 23
4.3.2. Johansen’s Co-Integration Test
Table: 3
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic
Critical
Value Prob.**
None * 0.476449 111.9034 76.97277 0.0000
At most 1 * 0.384762 67.89916 54.07904 0.0018
At most 2 0.269647 34.86840 35.19275 0.0542
At most 3 0.134005 13.50093 20.26184 0.3252
At most 4 0.053200 3.717361 9.164546 0.4558
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic
Critical
Value Prob.**
None * 0.476449 44.00422 34.80587 0.0031
At most 1 * 0.384762 33.03076 28.58808 0.0126
At most 2 0.269647 21.36747 22.29962 0.0671
At most 3 0.134005 9.783569 15.89210 0.3545
At most 4 0.053200 3.717361 9.164546 0.4558
Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Co-integration Rank Tests (Trace and Maximum Eigenvalue) results
show that both the tests are supporting each other. At the difference level of two (2)
both tests show three (3) Co-Integrating Vectors as Probability (Pt) value
approximately less than 5%.
25. 24
4.3.3. LikeliHood Co-Integration Test
Table: 4
The above mentioned table represents the effect of oil prices, interest rate, exchange
rate and inflation on stock prices by using t-statistics. If -2<t<2 it means that there is
insignificant relationship between two variables. The value smaller than -2 and greater
than 2 means there is long-term significant relationship between variables. If we see
the above table the value -5 is lesser than -2 which show a significantly negative
relationship between Exchange rate (E) and Stock prices (SP). Similarly, rate of
Interest (R) also have value which is smaller than -2 represents the highly significant
and negative relationship with stock price (SP). Inflation (I) and Oil prices (OP) both
have values greater than which affirms significant and positive relationship of these
two variables with Stock price. If we see the short-term relationship we analyzed an
insignificant negative relationship of Oil Prices and Inflation, whereas insignificant
positive relationship of Exchange rate (E) and Interest rate (R) with Stock Prices.
1 Cointegrating
Equation(s):
Log
likelihood -922.47
Normalized cointegrating coefficients (standard error in
parentheses)
SP E R I OP C
1 -1865.4 -5339.74 2627.68 353.0893 154333.4
-324.95 -1593.26 -551.48 -93.4616 -38602.6
t -5.7405 -3.35145 4.7648 3.7779 3.998
D(SP) D(E) D(R) D(I) D(OP)
-0.1014 -0.0246
8.80E-
05 -3.10E-05
9.80E-
06 -1.00E-05 -8.73E-05 -3.50E-05 -0.0002 -0.0002
27. 26
[ 0.65541] [-2.27869] [ 0.86225] [-0.51491] [ 0.40173]
D(R(-2)) -168.8229 -0.130978 0.231111 0.269496 -0.345197
(285.340) (0.34901) (0.39678) (0.11109) (2.25723)
[-0.59166] [-0.37529] [ 0.58247] [ 2.42603] [-0.15293]
D(OP(-1)) 3.660480 -0.032542 0.028375 -0.010926 0.250726
(20.0526) (0.02453) (0.02788) (0.00781) (0.15863)
[ 0.18254] [-1.32677] [ 1.01762] [-1.39963] [ 1.58057]
D(OP(-2)) -11.76788 0.017784 0.025670 -0.001950 0.018877
(20.1422) (0.02464) (0.02801) (0.00784) (0.15934)
[-0.58424] [ 0.72186] [ 0.91649] [-0.24867] [ 0.11847]
C 549.9665 -0.106117 0.191306 -0.082446 0.376903
(146.169) (0.17878) (0.20325) (0.05690) (1.15629)
[ 3.76255] [-0.59355] [ 0.94121] [-1.44884] [ 0.32596]
R-squared 0.184009 0.381623 0.176558 0.345161 0.073044
Adj. R-squared 0.026537 0.262287 0.017648 0.218789 -0.105843
Sum sq. resids 34377221 51.43016 66.47289 5.210239 2151.290
S.E. equation 776.6010 0.949886 1.079903 0.302337 6.143446
F-statistic 1.168521 3.197887 1.111059 2.731303 0.408324
Log likelihood -550.5053 -87.76784 -96.61948 -8.776667 -216.5770
Akaike AIC 16.30450 2.891821 3.148391 0.602222 6.625420
Schwarz SC 16.69304 3.280362 3.536931 0.990762 7.013960
Mean dependent 366.2472 0.298911 -0.214058 -0.065217 0.164513
S.D. dependent 787.1152 1.105930 1.089561 0.342064 5.842051
Determinant resid covariance (dof adj.) 1517573.
Determinant resid covariance 583819.2
Log likelihood -947.6023
Akaike information criterion 29.35079
Schwarz criterion 31.45538
VECM model shows both Long-Term and Short-Term relationships of Independent
Variables with Dependent Variable (SP). In this equation variables with Difference
Levels suggest Short-term relationships while other variables without Difference
Levels indicate Long-term relationships. If we see the above statistics for long run
relationship, Exchange Rate and Interest rate represents significantly negative,
however Oil Prices and inflation represents significant positive relationship with
Stock Prices. This model shows insignificantly negative relationship of Oil Prices and
Inflation, whereas positively insignificant relationship of Exchange rate and Interest
rate with stock prices in short-term.
28. 27
4.4.2. Vector Auto-regression Equation
D(SP) = α(1,1)*(β(1,1)*SP(-1) + β (1,2)*E(-1) + β (1,3)*I(-1) + β (1,4)*R(-1) + β (1,5)*OP(-1) + β
(1,6)) + C(1,1)*D(SP(-1)) + C(1,2)*D(SP(-2)) + C(1,3)*D(E(-1)) + C(1,4)*D(E(-2)) + C(1,5)*D(I(-
1)) + C(1,6)*D(I(-2)) + C(1,7)*D(R(-1)) + C(1,8)*D(R(-2)) + C(1,9)*D(OP(-1)) + C(1,10)*D(OP(-
2)) + C(1,11)
The obtained data was Unit Root so that we ran Augmented Dickey-Fuller test to
bring stationarity at difference level of 1. Further we analyzed the data in Pairwise
Granger Causality test to find causality between two variables which results that
Interest Rate (R) and Exchange Rate (E) causes Stock Prices. However, Inflation Rate
(I) and Oil Prices (OP) do not cause Stock Prices. Johansen’s Co-integration test
resulted that there are Three Co-integrating Vectors having Probability Value (Pt) less
than 5%. T-statistic under Likelihood Co-integration resulted that there is a long-term
significant negative relationship of Exchange rate and Interest rate with Stock Prices,
whereas significantly positive relationship of Inflation and Oil Prices with Stock
Prices. In short-term relationship we analyzed an insignificant negative relationship of
Oil Prices and Inflation, whereas insignificant positive relationship of Exchange rate
(E) and Interest rate (R) with Stock Prices. VECM model shows both Long-Term and
Short-Term relationships of Independent Variables with Dependent Variable (SP). In
this equation variables with Difference Levels suggest long-term relationships while
other variables without Difference Levels indicate short-term relationships.The
statistics for long run relationship, Exchange Rate and Interest rate represents
significantly negative, however Oil Prices and inflation represents significant positive
relationship with Stock Prices. This model shows insignificant relationship between
Stock Price (SP) and Macro-economical Variables in short-term. Co-integration test
29. 28
and Vector Error Correction test (VECM) represents the same results and supports
each other statistics.
5. Conclusions and Limitations
In this study we find impact of Macro-economical Variables at Stock Prices (SP).
There were four (4) Macro-Economical Variables which include Oil Prices (OP),
Interest Rate (R), Inflation Rate (I) and Exchange Rate (E). However, Stock Price
(SP) is a Dependent variable. Monthly data was gathered to analyze the above
mentioned variables from secondary sources. The data was from last Six (6) years
having 72 observations (monthly). After studying a wide range of empirical evidences
it is affirmed that there is an absence of any long term relationship between stock
prices and exchange rate (Bahmani et al. 1992) (Nieh & Lee, 2001), but there is short
term influence of exchange rates on stock prices(Kutty 2010).Johansen co-integration
model and vector error correction model (VECM) analyses that Stock prices are
negatively influenced by inflation rate (CPI). Short term interest rate and exchange
rate are insignificantly related with stock prices(Naik and Puja 2012).In case of
Nigeria there is an insignificant and positive effect of oil prices shocks on stock
market returns (Babatunde and Adenikinju 2013). (Marshal, 1992) stated that
increase in inflation leads to increase circulation of money in market and investors
have more money to save in banks. When banks have more deposits they will eager to
pay less rate of interest, so investors start to withdraw their money and invest into
stock market to maximize the profits.
According to previous studies for more than thirty years the overall correlation
between Oil prices and stock prices is strongly positive (Carlson, 2014). On the other
hand, when Oil prices start to fall the investor will start investing in other stocks and
30. 29
withdraw their money from oil industry which will eventually increase stock prices.
The above argument is not a final word due to sensitivity of Oil prices, it may move in
same direction with stock prices or in an opposite side.
Interest rate has also some implications that if it increases the investors are more
likely to withdraw investments from stocks and invest into term deposits in banks to
get higher interest rates and profits. However, increase in Exchange rate means the
value of US dollar is appreciating which leads to decrease in confidence level of
investor. Thus, investors start to put their money out from stock market.
So we concluded on base of Co-integration test and VECM that Exchange rate and
Interest rate impacts significant negative relation in long term but insignificantly
positive relation in short term with Stock Prices. However, Oil prices and inflation
shows significantly positive relationship in long-term but insignificantly negative
relationship in short-term with Stock Prices. This study has also some limitations,
results can be more accurate and clear if other macro-economic variables like
Industrial production index, Gold Prices, Money Supply and Political Uncertainty also
be included in the study. This study is specifically for Pakistan, and which is an Oil
importing country, there is no comparative analysis has done to check the influences
of these variables on other oil importing countries with similar economies. There
should be also a comparative study between oil and gold importing and exporting
countries and volatility in their stock prices.
