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Is Pakistan Stock Exchange (PSX) Informational 1

Efficient? 2

3

This paper aims to investigate the informational efficiency of Pakistan Stock Exchange 4

both in short-run and long-run as well as beyond the sample period and examines 5

eight macroeconomics variables to evaluate their impact on stock returns. The study 6

employs three unit-roots tests to examine the integration level of the variables. Vector 7

error correction model and Johansen’s cointegration tests are performed to 8

investigate cointegration relationship. The study employs Granger causality test and 9

variance decomposition analysis to find the direction of causality within and beyond 10

the sample period. Furthermore, impulse response function is applied to graphically 11

evaluate the shocks of exogenous macroeconomic variables on stock prices. The 12

empirical results indicate that PSX is price inefficient. Investors can take advantage 13

from PSX by keeping an eye on the macroeconomic variables trends. This is the prior 14

study to investigate the informational efficiency of PSX using eight macroeconomic 15

variables for 27 years data set. 16

17

Keywords: Efficiency, PSX, GDP, Stock Returns, Macroeconomic Variables. 18

19

20

Introduction 21

22

Systematic patterns in the stock market have attracted the attention of the 23

researchers in the last few years because they usually become a cause of earning 24

unexpected profits by the investors. Efficiency of stock market may affect due to these 25

systematic patterns. One can easily anticipate stock prices by scrutinizing the stock 26

market patterns. The Efficient Market Hypothesis (EMH) stressed that stock market 27

prices are not predictable so, investors cannot predict the stock market prices for their 28

unexpected gains. Cowles (1933) and Bachelier (1900) were the first to give the 29

notion of efficient market hypothesis through their inceptive empirical and theoretical 30

research respectively. Samuelson (1965) modernized the literature and research work 31

on efficient market hypothesis with his empirical research contribution. He 32

emphasized that well and properly forecasted stock prices seldom change and are not 33

predictable. The stock valuation model stated that current price of an equity share is 34

equal to net present value of that equity’s future cash flows; expected future cash 35

flows and required rate of return of a share fluctuate due to the random changes in 36

macroeconomic environment so, equity’s current price also changes. Furthermore, 37

Fama (1965) stated that semi-strong form of efficient market hypothesis stresses that 38

stock prices are not predictable from information available to the general public. 39

The informational efficiency of the stock market is examined by many studies. 40

Darrat (1987) investigated a straggled relationship between stock prices and money 41

growth in two countries i.e. U.K and West Germany. Ayadi (1994) concluded a strong 42

relationship between aggregate stock returns and their underlying stock values in 43

Nigeria. Chowdhury (1995) found Dhaka stock exchange informational inefficient. 44

Bondt and Thaler (1985) evaluated the effect of dramatic and unexpected news events 45

on stock returns and observed the New York Stock Exchange (NYSE) weak-form 46

efficient. Sarkar and Mukhopadhyay (2005) and Ahmed (2002) observed that Dhaka 47

stock exchange is not price efficient. Brown and Zhang (1997) evaluated the 48

relationship between supply of dealer services in a dealer market and stock returns 49

efficiency through their review paper. Laopodis (2003) observed that before the 50

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liberalization in the capital markets of Greece, these markets were informational 1

efficient in weak-form. Adelegan (2004) observed no evidence for the existence of 2

weak-form efficiency in Nigerian stock market. Habibullah, Makmur, Saini, Radam 3

and Ong (2005) found informational inefficiency in Kuala Lumpur stock market with 4

respect to operating efficiency of the banks. Maghyereh (2005) observed that after the 5

introduction of electronic trading in Amman stock exchange (ASE), it became more 6

informational efficient. Al-Khazali (2011) observed that after removing inactive and 7

infrequent trading factors, six stock exchanges of Gulf Corporation Council Countries 8

(GCCC) became more informational efficient. Dow and Gorton (1997) concluded that 9

for economic efficiency, efficiency of stock indices is not sufficient in general. 10

Hotchkiss and Ronen (2002) examined National Association of Security Dealers 11

(NASD) after the introduction of electronic system named as Fixed Income Pricing 12

System (FIPS) and investigated the similarity between market efficiency of stock 13

prices and corporate bond prices. Rashid (2008) and Wickremasinghe (2011) found 14

cointegration relationship between macroeconomic variables and stock returns in 15

Pakistan and Sri Lanka respectively. 16

In line with this historical background, the purpose of this study is to investigate 17

that can investors get advantage if Pakistan stock market is predictable? The aim of 18

present study is to evaluate whether short and long run relationship exist between 19

stock prices and eight macroeconomic indicators i.e. GDP (Gross Domestic Product), 20

CPI (Consumer Price Index), FDI (Foreign Direct Investment), M1 (Money Supply 21

includes coins, currency, travelers cheques, negotiable order of withdrawal accounts, 22

demand deposits etc. As it contains assests and currency readily convertible to cash 23

therefore, it measures most liquid portion of money supply), exchange rate (EXCH), 24

Industrial production (IND), market capitalization (MKT) and stock traded turnover 25

(STK) in context of PSX? The study also attempts to locate the causality direction of 26

macroeconomic variables and stock returns within and beyond the sample period. 27

28

29

Theoretical Framework 30

31 Stock market affects due to macroeconomic variables. These macroeconomic 32

indicators have also an impact on the efficiency of the stock market. Theoretical 33

framework of the study based on comprehensive literature is as follows: 34

35

Implications for Stock Market Efficiency 36

37 Two notions are related with the efficiency of the stock market: One is that at any 38

unit time, uniform spread of stock returns is not a desired condition for the market 39

equilibrium (Hess, 1981). The other notion is that uniform distribution of stock returns 40

is a desired condition for the market equilibrium, also called efficient market 41

hypothesis. Fama (1970) summarized this view by saying that stock market is called 42

efficient if stock prices reflect all the information known by the general public. Many 43

theories have been hypothesized to elaborate the idea of efficient stock market as: 44

45

Overreaction Hypothesis 46

47

This hypothesis suggests that efficiency of stock market effects due to unexpected 48

and dramatic news and events. Bondt and Thaler (1985) examined the impact of 49

unexpected and dramatic news events on stock market efficiency. 50

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Stock Market Efficiency Hypothesis 1

2

This hypothesis emphasizes that investors cannot earn unexpected profits from 3

stock market returns because stock prices reflect all the information available to the 4

general public. Darrat (1987) found a strong relationship of stock returns and money 5

growth in U.K and West Germany and concluded these markets as price inefficient. 6

Ahmed (2002) found Dhaka stock exchange informational inefficient. 7

Wickremasinghe (2011) found Sri Lankan stock market informational inefficient. 8

9

Some Other Efficiency Effects 10

11

Dow and Gorton (1997) in their review research work stated that economic 12

efficiency and investment efficiency have an impact on stock market efficiency. 13

Brown and Zhang (1997) evaluated the impact of supply of dealers in a dealer market 14

on stock market efficiency in general. Habibullah et al. (2005) examined the effect of 15

operating efficiency of banks on the efficiency of bank stock prices in Malaysia. 16

Laopodis (2003) studied a relationship between stock market liberalization and stock 17

market efficiency for the capital markets of Greece. 18

19

20

Review of the Literature 21

22 There exists comprehensive amount of literature that insight the impact of 23

macroeconomic variables on stock market returns and informational market 24

efficiency. The study discusses it as: 25

Bondt and Thaler (1985) examined the impact of unexpected and dramatic news 26

events on stock market prices of New York Stock Exchange (NYSE) using CAPM 27

model. The results concluded that dramatic news events have an impact on efficient 28

market hypothesis. Darrat (1987) evaluated the impact of monetary policy related 29

news on the efficiency of the stock markets of West Germany and U.K and found a 30

strong relationship between stock market returns and money growth by employing 31

trivariate regression analysis and atheoretical statistical procedure proposed by 32

Mishkin (1982). Ayadi (1994) found fluctuations in stock returns from their 33

fundamental underlying values in Nigeria by using vector autoregressive (VAR) 34

model and declared the Nigerian stock market informational inefficient. Ahmed 35

(2002) followed Ayadi (1994) and examined Dhaka stock exchange efficiency by 36

using LB statistic test. He found the Dhaka stock exchange weak-form efficient. Ntim, 37

Opong, Danbolt and Dewotor (2011) examined the market efficiency of eighteen (18) 38

individual national African stock markets and twenty- four (24) continents based 39

African stock markets. The findings revealed that weak-form market efficiency exists 40

in eight individual national African stock markets using variance-ratio tests. The study 41

followed Ayadi (1994). Chowdhury (1995) examined the relationship between stock 42

market prices and money supply both M1 and M2 in Dhaka stock market by 43

performing Johansen (1991) cointegration and Granger causality tests by considering 44

bivariate VAR model. The results concluded the Dhaka stock exchange informational 45

inefficient. 46

Dow and Gorton (1997) found through their review research that stock market 47

efficiency is not sufficient for investment efficiency and economic efficiency. Brown 48

and Zhang (1997) found a relationship between supply of dealer services in a dealer 49

market and the efficiency of the underlying stock in general. Hotchkiss and Ronen 50

(2002) examined National Association of Security Dealers (NASD) after the 51

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introduction of electronic system named as Fixed Income Pricing System (FIPS) using 1

vector autoregressive (VAR) approach and concluded that stock market is less 2

informationally efficient than the bond market. Laopodis (2003) found that Greece 3

stock market, namely Athens Stock Exchange (ASE) was weak-form efficient before 4

the declaration of financial market liberalization by employing random walk test as 5

KPSS and structural changes tests as Wald and Chow tests. Adelegan (2004) did not 6

reject the efficient market hypothesis for Nigerian Stock Exchange (NSE) nor found 7

that NSE is weak-form efficient by performing traditional random walk approach. The 8

study observed that there is still a need for improved research in NSE for vivid 9

conclusions. Sarkar and Mukhopadhyay (2005) observed after performing a number of 10

tests as automatic variance ratio test, Andrew’s tests and BDS test; that Indian stock 11

market is inefficient. Habibullah et al. (2005) investigated efficiency of Kuala Lumpur 12

Stock Exchange (KLSE) with respect to operating efficiency of Malaysian banks. The 13

study used data envelopment analysis and observed that Malaysian banks are price 14

inefficient. Maghyereh (2005) employed a multifactor model consisted of time 15

varying coefficients and found that electronic trading system and automation date 16

have no significant impact on Amman Stock Exchange (ASE) efficiency. Al-Zoubi 17

and Al-Zu’bi (2007) investigated Amman stock exchange using Box-Jenkins 18

technique and EGARCH model. The study suggested that good news has more impact 19

on ASE than bad news and ASE is informationally inefficient. Rashid (2008) 20

examined the impact of four macroeconomic variables i.e. consumer prices, industrial 21

production, market rate of interest and exchange rate on KSE by utilizing 22

cointegration and Granger causality tests. The results found short-run relationship 23

between interest rate and stock prices and long-run relationship of four 24

macroeconomic variables and stock prices. Maxfield (2009) investigated role of stock 25

markets in emerging economies and found the public stake in the stock markets of 26

emerging economies. Wickremasinghe (2011) evaluated the impact of six 27

macroeconomic variables i.e. CPI, three months FDR (Fixed Deposit Receipts), M1, 28

GDP, USD exchange rate and US share price Index on Sri Lankan stock market by 29

using cointegration error correction model, impulse responses, variance decomposition 30

analysis and unit-root tests. The study suggested that Sri Lanka stock market can be 31

predicted. It 32

Al-Khazali (2011) examined efficiency of six Gulf Corporation Council (GCC) 33

capital markets by employing LOMAC single variance ratio test, sign based tests and 34

Wright’s rank test. The findings suggested that six capital markets of GCC countries 35

were price inefficient of weak-form. Kumar and Pandey (2013) investigated four 36

agricultural and seven non-agricultural commodity future markets of India for market 37

efficiency using Johansen cointegration, ECM model and ECM-GARCH-in-mean 38

model. The findings revealed that some level of inefficiency exists in the short run for 39

all the commodities. Nwachukwu and Shitta (2015) examined efficiency of 24 40

emerging and 9 industrial stock market returns around the world by using parametric 41

and non-parametric techniques. The findings concluded that future prices can be 42

predictable in two-thirds of emerging markets and one-third of industrial economies. 43

Seth and Sharma (2015) examined some selected U.S and Asian stock markets to 44

investigate the informational market efficiency employing unit-root test, Johansen’s 45

cointegration, GARCH, Granger causality and Pearson correlation coefficient tests 46

and found these markets price inefficient in weak-form. Chow, Hui, Vieito and Zhu 47

(2016) found that financial liberalization failed to improve the efficiency of Latin 48

America stock markets by using various approaches including Wright variance ratio 49

test, stochastic dominance test, Chow-Denning multiple variance ratio test and 50

martingale hypothesis test. Huszar, Tan and Zhang (2016) investigated the market 51

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efficiency of offshore and onshore Renminbi (RMB) forward markets and observed 1

that onshore markets are less efficient than offshore markets. Ho and Lyke (2017) 2

classified the determinants of stock market development into two categories i.e. 3

macroeconomic factors and industrial factors in a review paper. Jiang (2017) provided 4

evidence that informational market efficiency fluctuates greatly across market stock 5

exchanges and individual stock in U.S stock markets using Dimson beta model, partial 6

adjustment model and variance ratio test. 7

8

Data and Methodology 9

10 The data set and different techniques used by this study to investigate the 11

market efficiency of Pakistan Stock Exchange (PSX) are as follows: 12

13

Data 14

The data set of the study comprises of monthly returns of PSX and eight 15

macroeconomic variables (GDP, M1, CPI, FDI, Industrial production, Exchange rate, 16

Market capitalization and stock traded turnover), covers the period from January 1, 17

1992 to March 30, 2019. The data about PSX and macroeconomic variables is 18

collected from official website of PSX; and sources of WDI and IFS. 19

20

Modeling Framework 21

22

The study employs three unit-root tests i.e. Augmented Dickey-Fuller (ADF), 23

Phillips-Perron (PP) and Ng-Perron to find the level of integration of the variables so 24

that, appropriate cointegration test can be used. Significance of macroeconomic 25

variables and stock returns is checked using Least Squares, ARCH and EGARCH 26

methods. Short and long run relationship between macroeconomic variables and stock 27

prices are evaluated using vector error correction model and Johansen’s cointegration 28

test respectively. This study examines the direction of causality among variables 29

within and beyond the sample period using Granger causality test and variance 30

decomposition analysis respectively. Furthermore, impulse response function is used 31

to graphically evaluate the shocks of exogenous macroeconomic variables and 32

reaction of stock market prices. The analytical tools used by the study are briefly 33

explained below: 34

35

Unit-root tests 36

37

Stationarity of stock returns and macroeconomic variables can be checked using a 38

number of statistical techniques proposed by Perron (1990). This study adopted ADF, 39

PP and Ng-Perron unit-root tests. 40

41

Augmented Dickey-Fuller (ADF) unit-root test 42

43

ADF test is the augmented form of Dickey-Fuller and is usually used for complex 44

data sets of time-series. The ADF statistic is a negative number; the more its negative, 45

the strongly is the rejection of null hypothesis that there exists a unit root in the time-46

series data set. The ADF testing equation is: 47

48

ΔZt = λ + ψt + αZt-1 + β1ΔZt-1 +…+ βρ-1ΔZt-ρ+1 + ϕt -----------------------------------(1) 49

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Here λ is a constant term, ψ is a coefficient used for time trend and ρ is 1

autoregressive process lag order. Lag order and lag length must be determined while 2

employing ADF test for higher order autoregressive processes. The lag length can be 3

determined by testing down from high order lag to evaluate the t-values coefficients. 4

The unit root is calculated under null hypothesis (H0) as α=0 against alternative 5

hypothesis (HA) as α<0. Test statistic of unit root under ADF is: 6

7

DFT = ----------------------------------------------------------------------------(2) 8

9

This test statistic is compared with critical values of DF test. Null hypothesis 10

would be rejected if DF larger negative critical value is more than test statistic and it 11

would mean that there is no unit root in time-series. 12

13

Phillips-Perron (PP) unit-root test 14

15 PP unit root test is used in time-series when there is higher order of 16

autocorrelation in the process generating data. It is improved form of Dickey-Fuller 17

test. The only difference between ADF test and PP test is that PP makes a non-18

parametric correction to the test statistic while ADF introduces lags in the test statistic. 19

PP test is robust with respect to heteroscedasticity and autocorrelation in test equation 20

disturbance process. PP does not perform well in case of finite samples of time-series 21

data set. Testing equation of PP test is as follows: 22

23

ΔZt = ρZy-1 + ϕt -----------------------------------------------------------------------------(3) 24

25

Here ΔZt is the regressor in the test equation, null hypothesis (H0) is ρ=0 which 26

shows that time series has a unit root, Δ represents the difference operator and ϕt is the 27

white noise. Null hypothesis is compared with PP tabular values. If ρ<0 then null 28

hypothesis would be rejected. 29

30

Ng-Perron unit-root test 31

32

Ng and Perron (2001) stated that ADF test faces the low power problem when the 33

moving average polynomial series has large negative unit-root. Ng-Perron unit root 34

test has augmented merits of small sample and better power than ADF and PP tests. 35

The testing equation under Ng-Perron test for de-trended data is as follows: 36

37

M = ( − T1 ( )

2) / g0 if Zt = {1} 38

( + (1- ) T1 ( )

2) / g0 if Zt = {1, t}. --------(4) 39

40

41

Here g0 is spectrum of zero frequency and is de-trended value of GLS 42

(Generalized Least Square). MSBd, M and M are the other three statistics used 43

when series residuals are negatively correlated. 44

4.2.2 Johansen’s Cointegration Test 45

Engle and Granger (1987) stressed that if the variables under observation have 46

same level of integration, then long-run cointegration can be evaluated using 47

Johansen’s cointegration test otherwise Autoregressive Distributed Lag (ARDL) 48

model is used. The present study evaluates the level of integration of variables using 49

ADF, Ng-Perron and PP unit root tests and after observing the same level of 50

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integration, Johansen’s cointegration test is employed. Following is the test equation 1

under Johansen’s cointegration test: 2

Zt = B1Zt-1 +…+ BpZt-p + Cyt + ϕt ----------------------------(5) 3

4

This equation can be written as: 5

6

ΔZt = ωZt-1 + ΔZt-iβyt + ϕt ----------------------------(6) 7

8

Where ω = −1 and = 9

10

Here yt represents deterministic variables vector and Zt represents vector of non-11

stationary variables. Engle and Granger (1987) declared that there are k*r matrices 12

where r represents the number of long-run relationships. Two statistics i.e. a trace 13

statistic and a maximal eigenvalue statistic are calculated under Johansen’s 14

cointegration test as follows: 15

16

ωtrace = −T ) r = 0, 1, 2,…, n−1 and -----------(7) 17

ωmax = −T log (1 − ) r = 0, 1, 2,…, n−2, n−1 ----------(8) 18

19

These test statistics are then compared with Osterwald-Lenum (1992) critical 20

values for empirical inferences. 21

22

Vector Error Correction Model 23

24

Engle and Granger (1987) stressed that there exists an error correction model in 25

time-series data set if there exists a long-run cointegration relationship. Following is 26

the test equation of vector error correction model: 27

28

ΔZt = b1 + c1ωtt-1 + ΔZt-i + ΔZt-i + ϕ1t. --------------------------(9) 29

ΔSt = b2 + c2 ωtt-1 + ΔSt-i + ΔSt-i + ϕ2t --------------------------(10) 30

31

Here Zt and St represent cointegrated variables, p is the lag length, Δ represents 32

difference operator, ωtt-1 is cointegration equation residual and ϕt is the white noise. 33

34

Granger Causality Test 35

36

Granger causality test evaluates the direction of causality of the time-series 37

variables only for the sample period. It examines the uni-directional and bi-directional 38

causal relationship of the under observation time-series variables. 39

40

Variance Decomposition Analysis 41

42

To overcome the demerits of Granger causality test and to examine the causality 43

of the time-series data set beyond the sample period, variance decomposition analysis 44

is performed. Sims (1982) defined that a variable effects due to its own shocks and 45

due to the shocks of other variables. 46

47

48

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Empirical Estimations and Discussion 1

2 Regression Analysis 3

4 The study performed regression analysis using Least Squares, ARCH and 5

EGARCH methods to examine the significance of the macroeconomic variables and 6

stock returns. Table 1, 1(a) and 1 (b) show the results of regression. The findings 7

indicate that all the variables of the study are significant at 1 per cent level of 8

significance while fit of model is 96% approximately. 9

10

Table 1. Regression Analysis (Least Squares) 11

Variable Coefficient t-Statistic Prob.

CPI -15.86146 -14.38279 0.0000***

EXCH -122.8417 -21.34230 0.0000***

FDI -322.4932 -6.464657 0 .0000***

GDP 74.28112 3.837423 0.0000***

IND 205.4615 18.61581 0.0000***

M1 6.34E-10 19.28373 0.0000***

MKT 53.83375 13.02188 0.0000***

STK 6.444067 24.77429 0.0000***

C 13234.81 18.40473 0.0000***

R-Squared 0.958619 Note: *** Significance at 1 per cent, ** Significance at 5 per cent, * Significance at 10 per cent 12

13 Table 1(a). Regression Analysis (ARCH) 14

Variable Coefficient z-Statistic Prob.

CPI -15.86146 -6.984428 0.0000***

EXCH -122.8417 -11.23135 0.0000***

FDI -322.4932 -2.445132 0.0145***

GDP 74.28112 1.595062 0.0007***

IND 205.4615 9.836414 0.0000***

M1 6.18E-10 6.805697 0.0000***

MKT 53.83375 5.670524 0.0000***

STK 6.444067 10.63025 0.0000***

C 13234.81 9.809621 0.0000***

R-Squared 0.958469 Note: *** Significance at 1 per cent, ** Significance at 5 per cent, * Significance at 10 per cent 15 16

Table 1(b). Regression Analysis (EGARCH) 17

Variable Coefficient z-Statistic Prob.

CPI -15.86146 -10.75926 0.0000***

EXCH -122.8417 -19.00534 0.0000***

FDI -322.4932 -3.657567 0.0003***

GDP 74.28112 2.438012 0.0148***

IND 205.4615 17.33257 0.0000***

M1 6.32E-10 10.45835 0.0000***

MKT 53.83375 8.628546 0.0000***

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STK 6.444067 15.55061 0.0000***

C 13234.81 16.84084 0.0000***

R-Squared 0.958618 Note: *** Significance at 1 per cent, ** Significance at 5 per cent, * Significance at 10 per cent. 1 2

3 Unit-Root Tests 4

5

An appropriate cointegration test can be applied only when integration level of 6

variables is calculated. The study found the integration level of observed variables 7

using ADF, PP and Ng-Perron unit root tests. Tables 2(a) and 2(b) represent the results 8

of unit root tests. 9

10

Table 2(a). Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) Unit Root 11

Tests 12

Variable ADF Test PP Test

t-Statistic t-Statistic

CLOSE -1.415207 -1.419009

CPI -2.377736 -2.428443

EXCH -0.291082 0.062684

FDI -1.995554 -1.385288

GDP -3.449573 -3.189982

IND -3.031707 -3.188884

M1 -1.571674 -1.588776

MKT -2.679928 -2.417662

STK -1.663539 -1.980986 Note: Critical values are -3.989045, -3.424926 and -3.135554 at 1 per cent, 5 per cent and 10 per cent 13 level of significance respectively using intercept and trend as deterministic components in the test 14 equation. 15 16

Table 2(b). Ng-Perron Unit Root Test 17

Variables MZα MZt MSB MPT

CLOSE -4.64343 -1.45010 0.31229 19.1370

CPI -11.4468 -2.36121 0.20628 8.12875

EXCH -0.97614 -0.40132 0.41112 40.4887

FDI -9.09317 -2.07819 0.22854 10.2433

GDP -20.4769 -3.14803 0.15374 4.77095

IND -15.9989 -2.82792 0.17676 5.69823

M1 -5.69670 -1.58437 0.27812 15.8130

MKT -15.6863 -2.77530 0.17693 5.96448

STK -4.14657 -1.41915 0.34225 21.7609 Critical Values: 18

Significance Level MZα MZt MSB MPT

1% -23.8000 -3.42000 0.14300 4.03000

5% -17.3000 -2.91000 0.16800 5.48000

10% -14.2000 -2.62000 0.18500 6.67000

19

These three unit root tests evaluate the null hypothesis (H0) that there exists a unit 20

root in the time series. The results show that as t-statistic is larger than the critical 21

values so null hypothesis can be rejected at 1 per cent level of significance for all three 22

unit root tests with the conclusion that all the variables of the study have same level of 23

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integration and there exists a long run cointegration relationship between 1

macroeconomic variables and stock returns so, the study preferred to employ 2

Johansen’s cointegration test as compared to ARDL test. 3

4

Johansen’s Cointegration Test 5

6

The Johansen’s cointegration test results indicate the rejection of null hypothesis 7

at 5 per cent level of significance. Table 3 shows the empirical results. The rejection 8

of null hypothesis would mean that there exists a long-run cointegration relationship 9

between stock market prices and macroeconomic variables. 10

11

Table 3. Cointegration Rank Test 12 Null

Hypotheses

Eigenvalue Trace

Statistic

0.05 Critical

Value

Max-Eigen

Statistic

0.05 Critical

Value

r = 0 0.237416 220.5579* 215.1232 43.45564* 61.80550

r ≤ 1 0.098162 116.3487 139.2753 32.13278 49.58633

r ≤ 2 0.076008 84.21589 107.3466 24.58502 43.41977

r ≤ 3 0.055752 59.63087 79.34145 17.84106 37.16359

r ≤ 4 0.052014 41.78981 55.24578 16.61210 30.81507

r ≤ 5 0.047530 25.17772 35.01090 15.14479 24.25202

r ≤ 6 0.035467 23.47486 34.48648 12.57839 22.57499 Note: Trace test indicates 1 cointegration equation at the 0.05 level. * denotes rejection of the null 13 hypothesis at the 0.05 level. 14 15 Vector Error Correction Model 16

17

The study evaluated the short-run relationship between stock prices and 18

macroeconomic variables using vector error correction model. Table 4 shows the 19

results. The results indicate that only M1 has short-run cointegration relationship with 20

the stock market prices. 21

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Table 4. Vector Error Correction Model 1 Error Correction D(CLOSE) D(CPI) D(EXCH) D(FDI) D(GDP) D(IND) D(M1) D(MKT) D(STK)

D(CLOSE(-1))

Coefficient p-values

Std.error

-0.000976 0.09305*

(-0.01049)

5.63E-05 0.00152***

(0.03694)

-7.43E-08 0.00025***

(-0.00029)

7.68E-06 2.7E-05***

(0.27940)

1.30E-06 0.00014***

(0.00929)

4.08E-05 0.00031***

(0.13059)

6668744 8.6E+07

(0.07794)

1.69E-05 0.00063***

(0.02665)

-0.000252 0.00770***

(-0.03272)

D(CPI(-1))

Coefficient

p-values

Std.error

2.383064

12.3154

(0.19350)

0.041162

0.20170

(0.20407)

-0.012989

0.03368**

(-0.38559)

0.007035

0.00364***

(1.93377)

-0.005943

0.01851***

(-.32114)

0.017859

0.04139**

(0.43150)

6.52E+09

1.1E+10

(0.57590)

0.00063

0.08374*

(0.07204)

-0.082135

1.01858

(-0.08064)

D(EXCH(-1))

Coefficient

p-values

Std.error

6.791589

24.5833

(0.27627)

0.090775

0.40263

(0.22546)

-0.46769

0.06724*

(-0.69555)

0.004932

0.00726***

(0.67917)

0.003717

0.03694**

(0.10062)

0.006119

0.08261*

(0.07407)

6.42E+09

2.3E+10

(0.28393)

-0.004229

0.16716

(-0.02530)

0.246385

2.03323

(0.12118)

D(FDI(-1)) Coefficient

p-values

Std.error

-64.26954

209.102

(-0.30736)

-2.319302

3.42473

(-0.67722)

0.376850

0.57193

(0.65891)

-0.153144

0.06177**

(-2.47933)

0.147270

0.31422

(0.46869)

-0.294586

0.70271

(-0.41921)

-1.47E+11

1.9E+11

(-0.76664)

-0.332664

1.42188

(-0.23396)

-0.070262

17.2944

(-0.00406)

D(GDP(-1))

Coefficient

p-values

Std.error

-0.622820

40.4152

(0.01541)

0.036536

0.66193

(0.05520)

0.006483

0.11054

(0.05864)

0.003205

0.01194***

(0.26849)

-0.006360

0.06073**

(-0.10473)

0.015980

0.13582

(0.11765)

2.39E+09

3.7E+10

(0.06431)

0.012234

0.27482

(0.04452)

-0.104943

3.34265

(-0.03140)

D(IND(-1))

Coefficient

p-values

Std.error

1.981485

20.5164

(0.09658)

0.054570

0.33602

(0.16240)

-0.007620

0.05612**

(-0.13579)

0.003613

0.00606***

(0.59617)

-0.008354

0.03083**

(-0.27097)

-0.002253

0.06895**

(-0.03268)

3.33E+09

1.9E+10

(0.17663)

0.008067

0.13951

(0.05782)

0.023383

1.69687

(0.01378)

D(M1(-1)) Coefficient

p-values

Std.error

-5.05E-11

2.2E-10***

(-0.23012

-8.17E-13

3.6E-12***

(-0.22738)

2.89E-13

6.0E-13***

(0.48087)

-1.27E-13

6.5E-14***

(-1.95946)

9.63E-14

3.3E-13***

(0.29206)

-3.10E-13

7.4E-13***

(-0.42063)

-0.120406

0.20183

(-0.59657)

-9.41E-14

1.5E-12***

(-0.06305)

1.05E-12

1.8E-11***

(0.05807)

D(MKT(-1))

Coefficient

p-values

Std.error

0.434631

10.1245

(0.04293)

-0.019065

0.16582

(-0.11498)

-0.002117

0.02769**

(-0.07643)

3.24E-05

0.00299***

(0.01084)

9.78E-05

0.01521***

(0.00643)

-0.001041

0.03402**

(-0.03058)

98060421

9.3E+09

(0.01053)

-0.004491

0.06885**

(-0.06524)

0.003106

0.83737

(0.00371)

D(STK(-1))

Coefficient

p-values

Std.error

-0.149305

0.94082 (-0.15870)

-0.002778

0.01541*** (-0.18029)

0.000989

0.00257*** (0.38447)

-0.000209

0.00028*** (-0.75116)

4.28E-05

0.00141*** (0.03025)

-0.000447

0.00316*** (-0.14124)

-2.24E+08

8.7E+08 (-0.25885)

-0.000144

0.00640*** (-0.02257)

-0.002766

0.07781** (-0.03555)

C

Coefficient

p-values

Std.error

43.98988

38.5579

(1.14088)

0.586093

0.63151

(0.92808)

-0.308673

0.10546

(-2.92687)

0.013714

0.01139***

(-1.20408)

0.040616

0.05794**

(0.70099)

-0.021227

0.12958

(-0.16382)

2.65E+10

3.5E+10

(0.74793)

-0.045388

0.26219

(-0.17311)

2.200808

3.18903

(0.69012)

Note: *** Significance at 1 per cent, ** Significance at 5 per cent, * Significance at 10 per cent 2

2019-3159-AJBE - FOR REVIEW ONLY

12

Granger Causality Test 1

2

Granger causality test results define that there exists only one bi-directional 3

(feedback) causal relationship between M1 and stock returns. Table 5 explains the 4

causality relationship of the variables. 5

6

Table 5. Granger Causality Test 7 Causality Prob. Direction of

Causality

Nature of

Causality From to

CPI CLOSE 0.0566** Unidirectional Causality

CLOSE CPI 0.4306 No causality

EXCH CLOSE 0.5557 Unidirectional No causality

CLOSE EXCH 0.0858* Causality

FDI CLOSE 0.5959 Unidirectional No causality

CLOSE FDI 0.0286*** Causality

GDP CLOSE 0.2483 No causality

CLOSE GDP 0.1774 No causality

IND CLOSE 0.7246 Unidirectional No causality

CLOSE IND 0.0503** Causality

M1 CLOSE 0.02033*** Bidirectional Causality

CLOSE M1 0.0339** Causality

MKT CLOSE 0.0344** Unidirectional Causality

CLOSE MKT 0.4881 No causality

STK CLOSE 0.0192*** Unidirectional Causality

CLOSE STK 0.3245 No causality Note: *** Significance at 1 per cent, ** Significance at 5 per cent, * Significance at 10 per cent 8

9

The results indicate that there are three unidirectional causal relationships from 10

macroeconomic variables to stock market prices i.e. from CPI, Market capitalization 11

and stock traded turnover to stock returns. From stock prices to macroeconomic 12

variables there exist three unidirectional causal relationships i.e. from stock prices to 13

Exchange rate, FDI and Industrial production. 14

15

Variance Decomposition Analysis 16

17 This study employed variance decomposition analysis to examine the causality 18

relationships of the variables beyond the sample period and in the longer time 19

horizons. Table 6 shows the results. 20

21

Table 6. Variance Decomposition Analysis of Stock Returns 22 Perio

d CLOSE CPI EXCH FDI GDP IND M1 MKT STK

1 100.0000

0.000000

0.000000

0.000000

0..000000

0.000000

0.000000

0.000000

0.000000

2 99.96357

0.000366

0.012933

0.017094

0.000138

0.001442

0.000505

0.000109

0.003842

3 99.95334

0.003913

0.016300

0.019786

9.96E-05 0.001053

0.001311

7.38E-05

0.004126

4 99.94040

0.009228

0.018753

0.022918

7.96E-05 0.000791

0.003483

9.43E-05

0.004253

5 99.92481

0.016450

0.020618

0.025573

9.81E-05 0.000721

0.007313

0.000186

0.004228

6 99.90648

0.025210

0.022213

0.028099

0.000157

0.000849

0.012494

0.000344

0.004152

7 99.88570

0.035272

0.023635

0.030520

0.000253

0.001165

0.018847

0.000561

0.004048

8 99.8628 0.04640 0.02494 0.03286 0.00038 0.00164 0.02618 0.00082 0.00393

2019-3159-AJBE

13

1 7 0 7 3 6 3 8 4

9 99.83817

0.058416

0.026159

0.035150

0.000541

0.002271

0.034337

0.001139

0.003815

10 99.81211

0.071127

0.027308

0.037375

0.000723

0.003019

0.043159

0.001485

0.003696

Note: .This tabe shows casual relationship between macroeconomic variables and stock prices beyond the 1 sample period. 2

3

The results indicate that in long time horizons, the vital macroeconomic variable 4

which disturbs the stock returns is CPI. Other macroeconomic variables whose shocks 5

create a disturbance in stock prices are M1, FDI and Exchange rate respectively. These 6

results are consistent with Darrat (1987), Wickremasinghe (2011), Seth and Sharma 7

(2015), Rashid (2008) and Ahmed (2002). 8

9

Impulse Responses 10

11

Figure 1 represents the shocks in exogenous macroeconomic variables i.e. CPI, 12

exchange rate, FDI, GDP, industrial production, M1, market capitalization and stock 13

traded turnover and reaction of endogenous variable i.e. stock returns at the time of 14

shocks and in successive points in time. 15

16

Figure 1. Impulse Responses - Graphical representation of variance decomposition 17

analysis 18 19

-100

0

100

200

300

400

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of CLOSE to Cholesky

One S.D. Innovations

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of CPI to Cholesky

One S.D. Innovations

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of EXCH to Cholesky

One S.D. Innovations

-.10

-.05

.00

.05

.10

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of FDI to Cholesky

One S.D. Innovations

-.1

.0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of GDP to Cholesky

One S.D. Innovations

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of IND to Cholesky

One S.D. Innovations

-1E+11

0E+00

1E+11

2E+11

3E+11

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of M1 to Cholesky

One S.D. Innovations

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of MKT to Cholesky

One S.D. Innovations

-10

-5

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

CLOSE CPI EXCH

FDI GDP IND

M1 MKT STK

Response of STK to Cholesky

One S.D. Innovations

20 21

2019-3159-AJBE

14

Conclusion and Implications 1

2 The basic purpose of the study is to investigate that can investors get benefit if 3

Pakistan stock market is predictable? This study evaluated the informational market 4

efficiency of Pakistan Stock Exchange (PSX) by investigating short as well as long 5

run cointegration relationships between eight (8) macroeconomic variables (CPI, 6

Industrial production, M1, GDP, FDI, Exchange rate, Market capitalization and Stock 7

traded turnover) and stock market prices. 8

The empirical findings of the unit root tests show that all the variables employed 9

by the study have same integration level. Johansen’s cointegration test provides the 10

evidence for the long run cointegration relationship between all eight (8) 11

macroeconomic variables and stock returns. Vector error correction model indicates 12

that only M1 has a short run relationship with stock prices. Granger causality results 13

show that from stock market returns to macroeconomic variables there are three 14

unidirectional causal relationships as from stock returns to Exchange rate, FDI and 15

Industrial production. From macroeconomic variables to stock prices, there are three 16

causal relationships as from CPI, Market capitalization and Stock traded turnover to 17

stock returns. Furthermore there is mere one feedback relationship between M1 and 18

stock returns. The findings of variance decomposition analysis indicate the effect of 19

macroeconomic variables on stock prices beyond the sample period. CPI, M1, FDI 20

and Exchange rate affect the stock returns in long time horizons respectively. 21

Furthermore, the results of impulse response function indicate the shocks in 22

macroeconomic variables and relative response of stock returns. 23

The overall findings provide the evidence that Pakistan Stock Exchange (PSX) is 24

informational price inefficient as this market is predictable using the trends of 25

macroeconomic indicators. By predicting the trends of these macroeconomic 26

variables, investors can easily grasp a huge benefit from Pakistani stock market. 27

In future research, Overreaction hypothesis and effects of agricultural and 28

political variables can be examined on PSX. 29

This study suggests the following policy implications: 1. If causality moves from 30

macroeconomic variables to stock prices, then policy makers can make suitable 31

policies at the time of financial crisis by controlling fluctuations in macroeconomic 32

variables. 2. If causality runs from sock returns to macroeconomic variables, then 33

policy makers can take appropriate steps to establish an efficient stock market by 34

electronically automating it and with the use of better technologies. 35

36

37

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34