Abstract:

The aim of this research study is to find the association between political events in Pakistan and Pakistan's stock exchange. The study considered 10 most big political events in Pakistan in the duration of 2012 to 2017. To calculate the results, the study used moving average method for calculating expected and abnormal returns. Further, t-statistics is used to explore the relationship between political events and behavior of PSX (100). The study has explored in results that political events, on which investor believes some change in Government policies do have impact on PSX. Investors respond positively when government organizations look strong and free from political pressure. The study recommended that government should make strong their organization, rather than alter government policies frequently.  

Key Words:

PSX-100 index, T-statistics, Political Events, Event Study Methodology, Moving Average

Introduction

Many economic indicators like exchange rate, foreign market trends, inflation, interest, and deflation, etc do have impact on stock return. But political events can be considered to affect the performance of many economic activates in less developed or developing countries because of their volatile political circumstances (Bittlingmayer, 1998). Country political stability is an influencing factor in investor decisions. The investor considers political stability of a country for their investment which untimely changes the trend in stock market. Political circumstances in Pakistan are unpredictable since 1947. Sometimes it goes upward which indicates low investment risk while some times it goes down which shows negative indication to investors.

The reliable and predictable situation of a country, where the law and order are fully ensured, and citizens are not at risk is known as political stability. A country gains its political stability when there is in-order work of agencies and no threat for derailing the democracy and policies in the time of bad conditions. Countries with more political stability are more satisfactory and favorable for investor (H Manzoor, 2013).

The research study that investigated the impact of stock in the response of political activities. It is important to consider the impact of political events on stock return in Pakistan. It might be because, in the 70 years, for the first time only two democratic government setups were able to complete their tenure democratically and made the history for the first time (Dawan News, 13 Nov 2017). Otherwise the political circumstances were imbalanced in previous years. Research studies concluded that all types of information influence stock return of return on investment (ROI). 

The stock market reaction in the response of different events is deeply studied by researchers. (e.g. Schwert, 1989). (Taimur, Muhammad, and Khan, Shahwali, 2013) A study on catastrophic and political news suggests that political activities in the country do have impact on Pakistan Stock Exchange (PSX). They explored in their research study that for no longer than 5 days, political activities effect stock return in short run. In the last few years, Pakistan is facing serious political issues and big menace of terrorism.  

Fama, (1965) “Efficient Market Hypothesis Theory” (EMF) describes that investor’s decisions might be reflected any time and at any point to all publically available information. Further, the study discussed that weak form of Efficient Market Hypothesis showed that is there is no relation between historical prices and current prices and no one can get long-run benefits in return from analysis of previous market data. Semi-Efficient Market Hypothesis shows that investor’s decisions are reflected in new publically available information and an investor can get benefits in return by using that information. The Hypothesis of Strong Efficient Market stated that stock prices are completely reflected in every type of information available to the investors in the market.

This research study contributes to the literature and observes the impact of most crucial political activities on the Pakistan Stock Exchange. Islamabad Sit-in (2014) and Lahore Riwand March were considerable ROI changer events for investors. Before the reshaping of PSX from KSE 100, few research studies were conducted to explore the impact of political events on KSE 100. But prior studies are different in the sense, that this study investigated the most recent and crucial political impact on Pakistan Stock Exchange. 

Literature Review

Mahmood, Irfan, Iqbal, Kamran & Ali Ijaz, (2014) found in their research study that some political news in the market does have an impact on KSE 100 for short run. Because political activities are not much concern to stock market in Pakistan. They investigate in their study that stock returns are negatively related to political events. Mostly political events are likely to bring instability in the government and hence investors lose their interest in stock market which leads to a downturn in the returns. 

The study observed that political events influence stock return in short run. However, it has the capability to recover and become normal in long-tern (Taimur, Muhammad and Khan, Shahwali, 2013). The study confirmed that KSE is positively related to good (Positive) political news and there is a positive impact of good news on KSE 100 that causes to decrease the stock market volatility. Similarly, the negative political activities are negatively related to KSE 100 that leads to increase the volatility of stock market. The study also explored that impact of negative political activities in stock market is almost double as compared to positive political activities. Most sectors are reflective of good and bad news (Suleman, 2012).

Political activities have a short-run impact on the stock exchange, followed by many research studies. The (K Najaf, R Najaf, Iqbal & IH Shah, 2015) study found that in short run, the stock market return is negatively related to political events. In long run, the political events impact is positive and insignificant. Supporting the same argument that political activities are positively related to KSE, (Murtaza, Hamza, and Ali, 2015) investigated in their study that all those political activities which might change the government policies can influence KSE positively. Their study further concluded different results for different nature of events.

Agrawal et al. (1999) research study investigated that different political events cause an increase in stock volatility in different countries. Supporting the above result, Mahmood et al, (2014) checked the results in their research study by using event study methodology that political activities have a significant impact on stocks return. With occurrence of political activity in the country, the stock volatility increase. The study argued that each result indicates negative return which is because of mostly events lead to destabilizing the government. And that’s why investors are more reflected stock market reaction during the occurrence of political activity. Further, they explained that political events have a significant impact on KSE 100 for short run (not more than10-15 days). They explained that it is due to political events are less related to stock market.

The study differs from prior studies based on considered data. The events which are considered in this particular study are the most recent and important events. The behavior of these particular political activates with the stock market has not been analyzed before. 

Hypothesis

H0: No association exist between political activities and stock market

H1: There is an association of political activities and stock market

Methodology

The research study used the event study methodology, owing to find any significant association of political activities in Pakistan and Pakistan stock exchange. The event study methodology is the widely used technique for finding abnormal returns of stock market. This technique shows the behavior of stock market before and after the occurrence of political activity. Brown & Warner (1985) eludes that event study methodology is an appropriate technique to check the short time impact of different political activities, in order to find the abnormal return of stocks after the happening of an event.  

The study considered 10 most important political activities in Pakistan, in the duration of 2011 to 2017, to find any significant association of political events in Pakistan and stock market return. The data were taken from Yahoo finance and the website of the Pakistan Stock Exchange. Next to this, the study set the pre-event and a post-event window for each political activity. 

Table 1.

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S.No width="210"> Political Activates width="90"> Date of Event width="78"> Pre-Event window width="78"> Post-Event window

1>  width="210" valign="top"> Disqualification of Prime Minister Yousaf Raza Gilani width="90" valign="top"> 19/06/2012 width="78" valign="top"> 29/05/2012 width="78" valign="top"> 10/06/2012

2 width="210" valign="top"> General Election width="90" valign="top"> 11/05/2013 width="78" valign="top"> 19/04/2013 width="78" valign="top"> 31/05/2013

3 width="210" valign="top"> CPEC Announcement width="90" valign="top"> 22/04/2014 width="78" valign="top"> 1/04/2015 width="78" valign="top"> 14/05/2015

4 width="210" valign="top"> PTI sit-in (Islamabad) width="90" valign="top"> 14/08/2014 to 17/12/2014 width="78" valign="top"> 16/07/2014 width="78" valign="top"> 15/01/2015

5 width="210" valign="top"> Dismissal of NA Speaker width="90" valign="top"> 21/8/2015 width="78" valign="top"> 31/07/2015 width="78" valign="top"> 11/09/2015

6 width="210" valign="top"> PANAMA Leaks allegations on PM width="90" valign="top"> 04/04/2016 width="78" valign="top"> 04/03/2016 width="78" valign="top"> 25/04/2016

7 width="210" valign="top"> PTI Islamabad lockdown width="90" valign="top"> 02/11/2016 width="78" valign="top"> 12/10/2016 width="78" valign="top"> 23/11/2016

8 width="210" valign="top"> PTI Riwand March width="90" valign="top"> 30/09/2016 width="78" valign="top"> 09/09/2016 width="78" valign="top"> 21/10/2016

9 width="210" valign="top"> JIT Formation width="90" valign="top"> 20/04/2017 width="78" valign="top"> 01/04/2017 width="78" valign="top"> 10/05/2017

10 width="210" valign="top"> Disqualification of PM width="90" valign="top"> 28/07/2017 width="78" valign="top"> 08/07/2017 width="78" valign="top"> 18/08/2017

Most research studies use CAPM in the Event Study Methodology. This particular research study used running moving average method instead of CAPM to find the actual, expected and abnormal return for the events. The expected and abnormal return is calculated through;

Daily Stocks Return (Rt) = Current Price – Previous Price / Previous Price

ER = Expected Return

AR =Abnormal return

AR = Rt – ER

After calculating ER and AR, we calculate t-statistics for the given values to analyze the significance of the events. T statistics helped us to determine the significance of the events.

 

Results and Discussion

To conclude, the relationship of political activities and PSX, the study run T-statistic test on abnormal return for each event. Hence, we can accept the null hypothesis or alternative hypothesis, based on t-test value.

Table 1. SD: 0.008914

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Date width="106" nowrap="" valign="bottom"> Return width="88" nowrap="" valign="bottom"> ER width="88" nowrap="" valign="bottom"> AR width="91" nowrap="" valign="bottom"> T-value

19/06/2012 width="106" nowrap=""> -0.001156157 width="88" nowrap=""> 0.001634 width="88" nowrap=""> -0.00279 width="91" nowrap=""> -0.31297

20/06/2012 width="106" nowrap=""> -0.004883435 width="88" nowrap=""> 0.001448 width="88" nowrap=""> -0.00633 width="91" nowrap=""> -0.71033

21/06/2012 width="106" nowrap=""> 0.009529083 width="88" nowrap=""> 0.001458 width="88" nowrap=""> 0.008071 width="91" nowrap=""> 0.905443

22/06/2012 width="106" nowrap=""> -0.006475736 width="88" nowrap=""> 0.001506 width="88" nowrap=""> -0.00798 width="91" nowrap=""> -0.89546

25/06/2012 width="106" nowrap=""> 0.001014689 width="88" nowrap=""> 0.001459 width="88" nowrap=""> -0.00044 width="91" nowrap=""> -0.04989

26/06/2012 width="106" nowrap=""> 0.010422913 width="88" nowrap=""> 0.001461 width="88" nowrap=""> 0.008962 width="91" nowrap=""> 1.0054

27/06/2012 width="106" nowrap=""> 0.000456432 width="88" nowrap=""> 0.001541 width="88" nowrap=""> -0.00108 width="91" nowrap=""> -0.12168

28/06/2012 width="106" nowrap=""> -0.00029049 width="88" nowrap=""> 0.001516 width="88" nowrap=""> -0.00181 width="91" nowrap=""> -0.20271

29/06/2012 width="106" nowrap=""> 0.024443367 width="88" nowrap=""> 0.001458 width="88" nowrap=""> 0.022986 width="91" nowrap=""> 2.578664

02/07/2012 width="106" nowrap=""> 0.00408346 width="88" nowrap=""> 0.001707 width="88" nowrap=""> 0.002376 width="91" nowrap=""> 0.266604

03/07/2012 width="106" nowrap=""> -0.001599107 width="88" nowrap=""> 0.001784 width="88" nowrap=""> -0.00338 width="91" nowrap=""> -0.37949

04/07/2012 width="106" nowrap=""> -0.00050721 width="88" nowrap=""> 0.001689 width="88" nowrap=""> -0.0022 width="91" nowrap=""> -0.2464

05/07/2012 width="106" nowrap=""> 0.009779867 width="88" nowrap=""> 0.001713 width="88" nowrap=""> 0.008067 width="91" nowrap=""> 0.905034

06/07/2012 width="106" nowrap=""> 0.004835215 width="88" nowrap=""> 0.001908 width="88" nowrap=""> 0.002927 width="91" nowrap=""> 0.328384

09/07/2012 width="106" nowrap=""> -0.000362383 width="88" nowrap=""> 0.001989 width="88" nowrap=""> -0.00235 width="91" nowrap=""> -0.26379

10/07/2012 width="106" nowrap=""> 0.000426355 width="88" nowrap=""> 0.002046 width="88" nowrap=""> -0.00162 width="91" nowrap=""> -0.18166

Table 1 (on the previous page) shows statistical results for the event of dismissal of Prime Minister Sayed Yousaf Raza Gillani. The standard deviation value for the previous window is 0.008914 which shows standard error for pre-event window. No significant value except a single day t value 2.578664 for the ninth post-event day which shows that market is revived. The overall t values demonstrate that there is almost no significant impact of the disqualification of Prime Minister Yousaf Raza Gillani on KSE 100. It may be because of investor predicted no change in government policies

Table 2. SD = 0.007000176

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Date width="90" nowrap="" valign="bottom"> Return width="90" nowrap="" valign="bottom"> ER width="96" nowrap="" valign="bottom"> AR width="100" nowrap="" valign="bottom"> T-test

13/05/2013 width="90" nowrap="" valign="bottom"> 0.01128705 width="90" nowrap="" valign="bottom"> 0.00173 width="96" nowrap="" valign="bottom"> 0.009557289 width="100" nowrap="" valign="bottom"> 1.365292535

14/05/2013 width="90" nowrap="" valign="bottom"> 0.00448672 width="90" nowrap="" valign="bottom"> 0.001768 width="96" nowrap="" valign="bottom"> 0.002719045 width="100" nowrap="" valign="bottom"> 0.388425215

15/05/2013 width="90" nowrap="" valign="bottom"> -0.0073245 width="90" nowrap="" valign="bottom"> 0.001863 width="96" nowrap="" valign="bottom"> -0.009187346 width="100" nowrap="" valign="bottom"> -1.312444915

16/05/2013 width="90" nowrap="" valign="bottom"> 0.00588129 width="90" nowrap="" valign="bottom"> 0.001791 width="96" nowrap="" valign="bottom"> 0.004090221 width="100" nowrap="" valign="bottom"> 0.584302502

17/05/2013 width="90" nowrap="" valign="bottom"> 0.01340303 width="90" nowrap="" valign="bottom"> 0.001863 width="96" nowrap="" valign="bottom"> 0.011539532 width="100" nowrap="" valign="bottom"> 1.648463036

20/05/2013 width="90" nowrap="" valign="bottom"> 0.01685801 width="90" nowrap="" valign="bottom"> 0.001965 width="96" nowrap="" valign="bottom"> 0.014893417 width="100" nowrap="" valign="bottom"> 2.12757739

21/05/2013 width="90" nowrap="" valign="bottom"> 0.01364889 width="90" nowrap="" valign="bottom"> 0.00207 width="96" nowrap="" valign="bottom"> 0.011578846 width="100" nowrap="" valign="bottom"> 1.654079113

22/05/2013 width="90" nowrap="" valign="bottom"> -0.0054321 width="90" nowrap="" valign="bottom"> 0.002114 width="96" nowrap="" valign="bottom"> -0.007546463 width="100" nowrap="" valign="bottom"> -1.078038991

23/05/2013 width="90" nowrap="" valign="bottom"> -0.0027626 width="90" nowrap="" valign="bottom"> 0.002053 width="96" nowrap="" valign="bottom"> -0.00481523 width="100" nowrap="" valign="bottom"> -0.68787266

24/05/2013 width="90" nowrap="" valign="bottom"> -0.0153833 width="90" nowrap="" valign="bottom"> 0.002031 width="96" nowrap="" valign="bottom"> -0.017414612 width="100" nowrap="" valign="bottom"> -2.487738976

27/05/2013 width="90" nowrap="" valign="bottom"> 0.02557153 width="90" nowrap="" valign="bottom"> 0.00192 width="96" nowrap="" valign="bottom"> 0.023651439 width="100" nowrap="" valign="bottom"> 3.378691785

28/05/2013 width="90" nowrap="" valign="bottom"> -0.0028224 width="90" nowrap="" valign="bottom"> 0.002119 width="96" nowrap="" valign="bottom"> -0.004941868 width="100" nowrap="" valign="bottom"> -0.705963282

29/05/2013 width="90" nowrap="" valign="bottom"> 0.00695029 width="90" nowrap="" valign="bottom"> 0.002107 width="96" nowrap="" valign="bottom"> 0.004842989 width="100" nowrap="" valign="bottom"> 0.691838196

30/05/2013 width="90" nowrap="" valign="bottom"> 0.01070597 width="90" nowrap="" valign="bottom"> 0.002106 width="96" nowrap="" valign="bottom"> 0.008600406 width="100" nowrap="" valign="bottom"> 1.228598507

31/05/2013 width="90" nowrap="" valign="bottom"> 0.01174391 width="90" nowrap="" valign="bottom"> 0.002157 width="96" nowrap="" valign="bottom"> 0.009587136 width="100" nowrap="" valign="bottom"> 1.369556301

Table 2 shows T-statistics of General Election 2013. The standard error of pre-event is 0.007000176 which is calculated from the SD of the pre-event window. The “t-statistics for day 6^th, 10^th and 11^th are 2.12757739, -2.487738976 and 3.378691785 respectively. It shows that PSX market moves upward on day 6^th and 11^th. While on the remaining days, PSX didn’t show such a big response to the event.

Table 3

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Date width="111" nowrap="" valign="bottom"> Return width="88" nowrap="" valign="bottom"> ER width="80" nowrap="" valign="bottom"> AR width="97" nowrap="" valign="bottom"> T test

22/04/2015 width="111" nowrap="" valign="bottom"> -0.00101 width="88" nowrap="" valign="bottom"> 0.000794 width="80" nowrap="" valign="bottom"> -0.00181 width="97" nowrap="" valign="bottom"> -0.16767

23/04/2015 width="111" nowrap="" valign="bottom"> 0.009471 width="88" nowrap="" valign="bottom"> 0.000765 width="80" nowrap="" valign="bottom"> 0.008707 width="97" nowrap="" valign="bottom"> 0.80809

24/04/2015 width="111" nowrap="" valign="bottom"> 0.002046 width="88" nowrap="" valign="bottom"> 0.000772 width="80" nowrap="" valign="bottom"> 0.001274 width="97" nowrap="" valign="bottom"> 0.118235

27/04/2015 width="111" nowrap="" valign="bottom"> -0.00796 width="88" nowrap="" valign="bottom"> 0.000691 width="80" nowrap="" valign="bottom"> -0.00865 width="97" nowrap="" valign="bottom"> -0.80294

28/04/2015 width="111" nowrap="" valign="bottom"> -0.00346 width="88" nowrap="" valign="bottom"> 0.000615 width="80" nowrap="" valign="bottom"> -0.00407 width="97" nowrap="" valign="bottom"> -0.37807

29/04/2015 width="111" nowrap="" valign="bottom"> 0.008035 width="88" nowrap="" valign="bottom"> 0.000494 width="80" nowrap="" valign="bottom"> 0.007542 width="97" nowrap="" valign="bottom"> 0.699985

30/04/2015 width="111" nowrap="" valign="bottom"> -0.00019 width="88" nowrap="" valign="bottom"> 0.000616 width="80" nowrap="" valign="bottom"> -0.00081 width="97" nowrap="" valign="bottom"> -0.07489

04/05/2015 width="111" nowrap="" valign="bottom"> -0.00565 width="88" nowrap="" valign="bottom"> 0.000642 width="80" nowrap="" valign="bottom"> -0.00629 width="97" nowrap="" valign="bottom"> -0.58366

05/05/2015 width="111" nowrap="" valign="bottom"> 0.009073 width="88" nowrap="" valign="bottom"> 0.000492 width="80" nowrap="" valign="bottom"> 0.008581 width="97" nowrap="" valign="bottom"> 0.796402

06/05/2015 width="111" nowrap="" valign="bottom"> -0.00285 width="88" nowrap="" valign="bottom"> 0.000488 width="80" nowrap="" valign="bottom"> -0.00334 width="97" nowrap="" valign="bottom"> -0.31012

07/05/2015 width="111" nowrap="" valign="bottom"> -0.00632 width="88" nowrap="" valign="bottom"> 0.000532 width="80" nowrap="" valign="bottom"> -0.00685 width="97" nowrap="" valign="bottom"> -0.63597

08/05/2015 width="111" nowrap="" valign="bottom"> -0.03101 width="88" nowrap="" valign="bottom"> 0.000621 width="80" nowrap="" valign="bottom"> -0.03163 width="97" nowrap="" valign="bottom"> -2.93613

11/05/2015 width="111" nowrap="" valign="bottom"> 0.015793 width="88" nowrap="" valign="bottom"> 0.000277 width="80" nowrap="" valign="bottom"> 0.015515 width="97" nowrap="" valign="bottom"> 1.440048

12/05/2015 width="111" nowrap="" valign="bottom"> -0.00329 width="88" nowrap="" valign="bottom"> 0.000466 width="80" nowrap="" valign="bottom"> -0.00375 width="97" nowrap="" valign="bottom"> -0.34825

13/05/2015 width="111" nowrap="" valign="bottom"> 0.005704 width="88" nowrap="" valign="bottom"> 0.000463 width="80" nowrap="" valign="bottom"> 0.005241 width="97" nowrap="" valign="bottom"> 0.486459

14/05/2015 width="111" nowrap="" valign="bottom"> -0.00195 width="88" nowrap="" valign="bottom"> 0.000448 width="80" nowrap="" valign="bottom"> -0.0024 width="97" nowrap="" valign="bottom"> -0.22273

Table 3 shows the statistical calculations for the event of the announcement of the China Pakistan Economic Corridor in 2015. By calculating standard deviation of pre-event window, the standard error was found 0.010774. It is observed that on 11^th post-event day only t-statistics is significant. Apart from this, there is no significant t-value in post-event window. It shows that stock return didn’t respond to the CPEC announcement. This may be the fact that only news of the project doesn’t have any significant impact on PSX.

Table 4

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Date width="115" nowrap="" valign="bottom"> Return width="86" nowrap="" valign="bottom"> ER width="86" nowrap="" valign="bottom"> AR width="86" nowrap="" valign="bottom"> T-Test

17/12/2014 width="115" nowrap="" valign="bottom"> 0.005214 width="86" nowrap="" valign="bottom"> 0.000718 width="86" nowrap="" valign="bottom"> 0.004496 width="86" nowrap="" valign="bottom"> 0.5159

18/12/2014 width="115" nowrap="" valign="bottom"> 0.005941 width="86" nowrap="" valign="bottom"> 0.000776 width="86" nowrap="" valign="bottom"> 0.005165 width="86" nowrap="" valign="bottom"> 0.592756

19/12/2014 width="115" nowrap="" valign="bottom"> 0.015375 width="86" nowrap="" valign="bottom"> 0.000823 width="86" nowrap="" valign="bottom"> 0.014552 width="86" nowrap="" valign="bottom"> 1.669888

22/12/2014 width="115" nowrap="" valign="bottom"> 0.008183 width="86" nowrap="" valign="bottom"> 0.000941 width="86" nowrap="" valign="bottom"> 0.007242 width="86" nowrap="" valign="bottom"> 0.831111

23/12/2014 width="115" nowrap="" valign="bottom"> 0.007831 width="86" nowrap="" valign="bottom"> 0.000999 width="86" nowrap="" valign="bottom"> 0.006832 width="86" nowrap="" valign="bottom"> 0.784022

24/12/2014 width="115" nowrap="" valign="bottom"> -0.00144 width="86" nowrap="" valign="bottom"> 0.00087 width="86" nowrap="" valign="bottom"> -0.00231 width="86" nowrap="" valign="bottom"> -0.26529

30/12/2014 width="115" nowrap="" valign="bottom"> 0.005536 width="86" nowrap="" valign="bottom"> 0.000738 width="86" nowrap="" valign="bottom"> 0.004798 width="86" nowrap="" valign="bottom"> 0.550568

31/12/2014 width="115" nowrap="" valign="bottom"> 0.018511 width="86" nowrap="" valign="bottom"> 0.000681 width="86" nowrap="" valign="bottom"> 0.01783 width="86" nowrap="" valign="bottom"> 2.046122

02/01/2015 width="115" nowrap="" valign="bottom"> 0.003182 width="86" nowrap="" valign="bottom"> 0.000689 width="86" nowrap="" valign="bottom"> 0.002494 width="86" nowrap="" valign="bottom"> 0.28615

07/01/2015 width="115" nowrap="" valign="bottom"> 0.008537 width="86" nowrap="" valign="bottom"> 0.000923 width="86" nowrap="" valign="bottom"> 0.007614 width="86" nowrap="" valign="bottom"> 0.873791

08/01/2015 width="115" nowrap="" valign="bottom"> 0.006242 width="86" nowrap="" valign="bottom"> 0.000806 width="86" nowrap="" valign="bottom"> 0.005435 width="86" nowrap="" valign="bottom"> 0.623745

09/01/2015 width="115" nowrap="" valign="bottom"> 0.002798 width="86" nowrap="" valign="bottom"> 0.000998 width="86" nowrap="" valign="bottom"> 0.0018 width="86" nowrap="" valign="bottom"> 0.206548

12/01/2015 width="115" nowrap="" valign="bottom"> -0.0014 width="86" nowrap="" valign="bottom"> 0.00098 width="86" nowrap="" valign="bottom"> -0.00238 width="86" nowrap="" valign="bottom"> -0.27365

13/01/2015 width="115" nowrap="" valign="bottom"> 0.006406 width="86" nowrap="" valign="bottom"> 0.000887 width="86" nowrap="" valign="bottom"> 0.005519 width="86" nowrap="" valign="bottom"> 0.633365

14/01/2015 width="115" nowrap="" valign="bottom"> 0.005341 width="86" nowrap="" valign="bottom"> 0.000765 width="86" nowrap="" valign="bottom"> 0.004576 width="86" nowrap="" valign="bottom"> 0.525102

15/01/2015 width="115" nowrap="" valign="bottom"> 0.000617 width="86" nowrap="" valign="bottom"> 0.00077 width="86" nowrap="" valign="bottom"> -0.00015 width="86" nowrap="" valign="bottom"> -0.01756

16/01/2015 width="115" nowrap="" valign="bottom"> 0.00672 width="86" nowrap="" valign="bottom"> 0.000724 width="86" nowrap="" valign="bottom"> 0.005996 width="86" nowrap="" valign="bottom"> 0.688106

Table 4 describes the t-statistics for the event of Pakistan Tehreek-e-Insaf Islamabad sit-in 2015. PTI sit-in was named Azadi March, which was arranged for the purpose of removal of PM due to investigation against rigging in the 2013 election. This 126 days’ sit-in was the longest sit-in in the history of Pakistan. SD of this activity is 0.008714, which is calculated based on SD of the Pre-event window. Observing the above table, there is no significant t-value and no considerable relationship between PTI sit-in and PSX return. 

Table 5. SD = 0.010519187

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Date width="92" nowrap="" valign="bottom"> Return width="92" nowrap="" valign="bottom"> ER width="92" nowrap="" valign="bottom"> AR width="92" nowrap="" valign="bottom"> T-test

21/08/2015 width="92" nowrap="" valign="bottom"> -0.04199 width="92" nowrap="" valign="bottom"> 0.000122 width="92" nowrap="" valign="bottom"> -0.04211 width="92" nowrap="" valign="bottom"> -4.00323

24/08/2015 width="92" nowrap="" valign="bottom"> 0.020882 width="92" nowrap="" valign="bottom"> -0.0002 width="92" nowrap="" valign="bottom"> 0.021077 width="92" nowrap="" valign="bottom"> 2.003717

25/08/2015 width="92" nowrap="" valign="bottom"> -0.00776 width="92" nowrap="" valign="bottom"> -2.300005 width="92" nowrap="" valign="bottom"> -0.00774 width="92" nowrap="" valign="bottom"> -0.73587

26/08/2015 width="92" nowrap="" valign="bottom"> 0.012559 width="92" nowrap="" valign="bottom"> -7.300005 width="92" nowrap="" valign="bottom"> 0.012632 width="92" nowrap="" valign="bottom"> 1.200849

27/08/2015 width="92" nowrap="" valign="bottom"> 0.014214 width="92" nowrap="" valign="bottom"> 4.650005 width="92" nowrap="" valign="bottom"> 0.014168 width="92" nowrap="" valign="bottom"> 1.346842

28/08/2015 width="92" nowrap="" valign="bottom"> 0.008068 width="92" nowrap="" valign="bottom"> 0.00019 width="92" nowrap="" valign="bottom"> 0.007878 width="92" nowrap="" valign="bottom"> 0.748899

31/08/2015 width="92" nowrap="" valign="bottom"> -0.00781 width="92" nowrap="" valign="bottom"> 0.000354 width="92" nowrap="" valign="bottom"> -0.00816 width="92" nowrap="" valign="bottom"> -0.7761

01/09/2015 width="92" nowrap="" valign="bottom"> -0.00426 width="92" nowrap="" valign="bottom"> 0.000297 width="92" nowrap="" valign="bottom"> -0.00456 width="92" nowrap="" valign="bottom"> -0.43355

02/09/2015 width="92" nowrap="" valign="bottom"> 0.002549 width="92" nowrap="" valign="bottom"> 0.000251 width="92" nowrap="" valign="bottom"> 0.002298 width="92" nowrap="" valign="bottom"> 0.218485

03/09/2015 width="92" nowrap="" valign="bottom"> -0.01483 width="92" nowrap="" valign="bottom"> 0.000241 width="92" nowrap="" valign="bottom"> -0.01507 width="92" nowrap="" valign="bottom"> -1.43243

04/09/2015 width="92" nowrap="" valign="bottom"> -0.02833 width="92" nowrap="" valign="bottom"> 0.000148 width="92" nowrap="" valign="bottom"> -0.02848 width="92" nowrap="" valign="bottom"> -2.70709

07/09/2015 width="92" nowrap="" valign="bottom"> 0.01595 width="92" nowrap="" valign="bottom"> -0.00023 width="92" nowrap="" valign="bottom"> 0.016184 width="92" nowrap="" valign="bottom"> 1.538482

08/09/2015 width="92" nowrap="" valign="bottom"> 0.010584 width="92" nowrap="" valign="bottom"> 0.000218 width="92" nowrap="" valign="bottom"> 0.010366 width="92" nowrap="" valign="bottom"> 0.985466

09/09/2015 width="92" nowrap="" valign="bottom"> -0.00112 width="92" nowrap="" valign="bottom"> 0.000309 width="92" nowrap="" valign="bottom"> -0.00143 width="92" nowrap="" valign="bottom"> -0.13616

10/09/2015 width="92" nowrap="" valign="bottom"> -0.00355 width="92" nowrap="" valign="bottom"> 0.000289 width="92" nowrap="" valign="bottom"> -0.00384 width="92" nowrap="" valign="bottom"> -0.36471

11/09/2015 width="92" nowrap="" valign="bottom"> -0.00859 width="92" nowrap="" valign="bottom"> 0.000177 width="92" nowrap="" valign="bottom"> -0.00877 width="92" nowrap="" valign="bottom"> -0.83339

Table 5 shows the t-tests values for the event of the dismissal of National Assembly Speaker. On 21^st August 2015, Lahore High Court ordered for re-election in NA-124 due to miss happening in the General Election 2013. The standard error of this pre-event window is 0.010519187 which is calculated on finding SD of the Pre-event window. On the day when event occurred, “T-statistics” value of the said event is -4.00323. It means that it is negatively significant on the event day. But soon the PSX recovered itself. Except post-event day 12, there was no significant impact find in following days.

Table 6. SD = 0.007622

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Date width="114" nowrap=""> Return width="104" nowrap=""> ER width="79" nowrap=""> AR width="79" nowrap=""> T test

04/04/2016 width="114" nowrap="" valign="bottom"> -0.00124 width="104" nowrap="" valign="bottom"> 0.000246492 width="79" nowrap="" valign="bottom"> -0.00149 width="79" nowrap="" valign="bottom"> -0.19518

05/04/2016 width="114" nowrap="" valign="bottom"> 0.005313 width="104" nowrap="" valign="bottom"> 0.000185108 width="79" nowrap="" valign="bottom"> 0.005128 width="79" nowrap="" valign="bottom"> 0.672819

06/04/2016 width="114" nowrap="" valign="bottom"> -0.00148 width="104" nowrap="" valign="bottom"> 0.000269866 width="79" nowrap="" valign="bottom"> -0.00175 width="79" nowrap="" valign="bottom"> -0.23012

07/04/2016 width="114" nowrap="" valign="bottom"> 0.002107 width="104" nowrap="" valign="bottom"> 0.000160167 width="79" nowrap="" valign="bottom"> 0.001947 width="79" nowrap="" valign="bottom"> 0.255486

08/04/2016 width="114" nowrap="" valign="bottom"> -0.01189 width="104" nowrap="" valign="bottom"> 0.000136142 width="79" nowrap="" valign="bottom"> -0.01202 width="79" nowrap="" valign="bottom"> -1.57725

11/04/2016 width="114" nowrap="" valign="bottom"> 0.001509 width="104" nowrap="" valign="bottom"> 2.24503E-05 width="79" nowrap="" valign="bottom"> 0.001486 width="79" nowrap="" valign="bottom"> 0.195003

12/04/2016 width="114" nowrap="" valign="bottom"> 0.000868 width="104" nowrap="" valign="bottom"> -5.20048E-05 width="79" nowrap="" valign="bottom"> 0.00092 width="79" nowrap="" valign="bottom"> 0.120752

13/04/2016 width="114" nowrap="" valign="bottom"> 0.003604 width="104" nowrap="" valign="bottom"> -0.000122916 width="79" nowrap="" valign="bottom"> 0.003727 width="79" nowrap="" valign="bottom"> 0.489023

14/04/2016 width="114" nowrap="" valign="bottom"> -1.4E-05 width="104" nowrap="" valign="bottom"> -6.04137E-05 width="79" nowrap="" valign="bottom"> 4.65E-05 width="79" nowrap="" valign="bottom"> 0.006105

15/04/2016 width="114" nowrap="" valign="bottom"> -0.00021 width="104" nowrap="" valign="bottom"> -4.75427E-05 width="79" nowrap="" valign="bottom"> -0.00016 width="79" nowrap="" valign="bottom"> -0.02156

18/04/2016 width="114" nowrap="" valign="bottom"> -0.0009 width="104" nowrap="" valign="bottom"> -3.71076E-05 width="79" nowrap="" valign="bottom"> -0.00086 width="79" nowrap="" valign="bottom"> -0.11313

19/04/2016 width="114" nowrap="" valign="bottom"> -0.00326 width="104" nowrap="" valign="bottom"> -5.16211E-05 width="79" nowrap="" valign="bottom"> -0.00321 width="79" nowrap="" valign="bottom"> -0.42096

20/04/2016 width="114" nowrap="" valign="bottom"> -0.00142 width="104" nowrap="" valign="bottom"> -7.95794E-05 width="79" nowrap="" valign="bottom"> -0.00134 width="79" nowrap="" valign="bottom"> -0.17589

21/04/2016 width="114" nowrap="" valign="bottom"> 0.004974 width="104" nowrap="" valign="bottom"> -7.76651E-05 width="79" nowrap="" valign="bottom"> 0.005052 width="79" nowrap="" valign="bottom"> 0.662826

22/04/2016 width="114" nowrap="" valign="bottom"> -0.00163 width="104" nowrap="" valign="bottom"> -4.67751E-05 width="79" nowrap="" valign="bottom"> -0.00158 width="79" nowrap="" valign="bottom"> -0.20784

25/04/2016 width="114" nowrap="" valign="bottom"> 0.004833 width="104" nowrap="" valign="bottom"> -5.97789E-05 width="79" nowrap="" valign="bottom"> 0.004892 width="79" nowrap="" valign="bottom"> 0.641909

The table 6 (on previous page) shows the t-statistics of the market return after the event of Panama leaks. Panama papers revealed the world's most important personality’s secrets news of holding assets outside the countries. These assets were hidden for the sake of inquiry and tax exemption by Prominent Politicians. Local studies concluded that unveiling of those hidden assets of such Political Personalities shocked different stock markets.

Table 7. SD = 0.007523

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Date width="90" nowrap="" valign="bottom"> Return width="90" nowrap="" valign="bottom"> ER width="90" nowrap="" valign="bottom"> AR width="90" nowrap="" valign="bottom"> t-value

02/11/2016 width="90" nowrap="" valign="bottom"> 0.005536 width="90" nowrap="" valign="bottom"> 0.001462 width="90" nowrap="" valign="bottom"> 0.004073 width="90" nowrap="" valign="bottom"> 0.541435

03/11/2016 width="90" nowrap="" valign="bottom"> -0.00317 width="90" nowrap="" valign="bottom"> 0.001431 width="90" nowrap="" valign="bottom"> -0.0046 width="90" nowrap="" valign="bottom"> -0.61178

04/11/2016 width="90" nowrap="" valign="bottom"> 0.006954 width="90" nowrap="" valign="bottom"> 0.001314 width="90" nowrap="" valign="bottom"> 0.00564 width="90" nowrap="" valign="bottom"> 0.74965

07/11/2016 width="90" nowrap="" valign="bottom"> -0.00047 width="90" nowrap="" valign="bottom"> 0.001232 width="90" nowrap="" valign="bottom"> -0.0017 width="90" nowrap="" valign="bottom"> -0.22572

08/11/2016 width="90" nowrap="" valign="bottom"> 0.002128 width="90" nowrap="" valign="bottom"> 0.001222 width="90" nowrap="" valign="bottom"> 0.000906 width="90" nowrap="" valign="bottom"> 0.120487

09/11/2016 width="90" nowrap="" valign="bottom"> 0.011786 width="90" nowrap="" valign="bottom"> 0.001182 width="90" nowrap="" valign="bottom"> 0.010604 width="90" nowrap="" valign="bottom"> 1.409466

10/11/2016 width="90" nowrap="" valign="bottom"> 0.003393 width="90" nowrap="" valign="bottom"> 0.001267 width="90" nowrap="" valign="bottom"> 0.002126 width="90" nowrap="" valign="bottom"> 0.282565

11/11/2016 width="90" nowrap="" valign="bottom"> -0.00757 width="90" nowrap="" valign="bottom"> 0.001293 width="90" nowrap="" valign="bottom"> -0.00886 width="90" nowrap="" valign="bottom"> -1.1782

14/11/2016 width="90" nowrap="" valign="bottom"> -0.0055 width="90" nowrap="" valign="bottom"> 0.001281 width="90" nowrap="" valign="bottom"> -0.00678 width="90" nowrap="" valign="bottom"> -0.90133

15/11/2016 width="90" nowrap="" valign="bottom"> 0.00264 width="90" nowrap="" valign="bottom"> 0.001222 width="90" nowrap="" valign="bottom"> 0.001417 width="90" nowrap="" valign="bottom"> 0.188404

16/11/2016 width="90" nowrap="" valign="bottom"> 0.000173 width="90" nowrap="" valign="bottom"> 0.001242 width="90" nowrap="" valign="bottom"> -0.00107 width="90" nowrap="" valign="bottom"> -0.14214

17/11/2016 width="90" nowrap="" valign="bottom"> -0.00205 width="90" nowrap="" valign="bottom"> 0.001241 width="90" nowrap="" valign="bottom"> -0.00329 width="90" nowrap="" valign="bottom"> -0.43746

18/11/2016 width="90" nowrap="" valign="bottom"> 0.002692 width="90" nowrap="" valign="bottom"> 0.001186 width="90" nowrap="" valign="bottom"> 0.001506 width="90" nowrap="" valign="bottom"> 0.200125

21/11/2016 width="90" nowrap="" valign="bottom"> 0.004527 width="90" nowrap="" valign="bottom"> 0.001129 width="90" nowrap="" valign="bottom"> 0.003397 width="90" nowrap="" valign="bottom"> 0.451537

22/11/2016 width="90" nowrap="" valign="bottom"> 0.0063 width="90" nowrap="" valign="bottom"> 0.001163 width="90" nowrap="" valign="bottom"> 0.005138 width="90" nowrap="" valign="bottom"> 0.682874

23/11/2016 width="90" nowrap="" valign="bottom"> 0.001135 width="90" nowrap="" valign="bottom"> 0.001205 width="90" nowrap="" valign="bottom"> -7.1E-05 width="90" nowrap="" valign="bottom"> -0.00937

On 1^st November 2016, the opposition party Pakistan Tehreek-e-Insaf had tried to lock down Islamabad in the protest owing to put pressure on the government of Pakistan for investigation against the Panama case. The Supreme Court of Pakistan offered to form a commission and to begin Panama case investigation. In Table 7, the statistical calculations for the event showed that there is no significant t-test value which means that there is no impact of Islamabad “Lockdown” on PSX. Moreover, the standard deviation for the event is calculated on same method.

Table 8

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Date width="90" nowrap="" valign="bottom"> Return width="90" nowrap="" valign="bottom"> ER width="90" nowrap="" valign="bottom"> AR width="90" nowrap="" valign="bottom"> T-test

30/09/2016 width="90" nowrap="" valign="bottom"> 0.010904 width="90" nowrap="" valign="bottom"> 0.001514 width="90" nowrap="" valign="bottom"> 0.00939 width="90" nowrap="" valign="bottom"> 1.397594

03/10/2016 width="90" nowrap="" valign="bottom"> 0.003353 width="90" nowrap="" valign="bottom"> 0.001516 width="90" nowrap="" valign="bottom"> 0.001837 width="90" nowrap="" valign="bottom"> 0.273424

04/10/2016 width="90" nowrap="" valign="bottom"> 0.003144 width="90" nowrap="" valign="bottom"> 0.001552 width="90" nowrap="" valign="bottom"> 0.001592 width="90" nowrap="" valign="bottom"> 0.236906

05/10/2016 width="90" nowrap="" valign="bottom"> -3.1E-05 width="90" nowrap="" valign="bottom"> 0.001535 width="90" nowrap="" valign="bottom"> -0.00157 width="90" nowrap="" valign="bottom"> -0.23312

06/10/2016 width="90" nowrap="" valign="bottom"> -0.00125 width="90" nowrap="" valign="bottom"> 0.001547 width="90" nowrap="" valign="bottom"> -0.0028 width="90" nowrap="" valign="bottom"> -0.41679

07/10/2016 width="90" nowrap="" valign="bottom"> 0.004927 width="90" nowrap="" valign="bottom"> 0.00152 width="90" nowrap="" valign="bottom"> 0.003407 width="90" nowrap="" valign="bottom"> 0.50705

10/10/2016 width="90" nowrap="" valign="bottom"> 0 width="90" nowrap="" valign="bottom"> 0.001652 width="90" nowrap="" valign="bottom"> -0.00165 width="90" nowrap="" valign="bottom"> -0.24594

11/10/2016 width="90" nowrap="" valign="bottom"> 0 width="90" nowrap="" valign="bottom"> 0.001641 width="90" nowrap="" valign="bottom"> -0.00164 width="90" nowrap="" valign="bottom"> -0.24417

12/10/2016 width="90" nowrap="" valign="bottom"> 0.000195 width="90" nowrap="" valign="bottom"> 0.001634 width="90" nowrap="" valign="bottom"> -0.00144 width="90" nowrap="" valign="bottom"> -0.21414

13/10/2016 width="90" nowrap="" valign="bottom"> 0.001261 width="90" nowrap="" valign="bottom"> 0.001607 width="90" nowrap="" valign="bottom"> -0.00035 width="90" nowrap="" valign="bottom"> -0.05142

14/10/2016 width="90" nowrap="" valign="bottom"> -0.00439 width="90" nowrap="" valign="bottom"> 0.001617 width="90" nowrap="" valign="bottom"> -0.00601 width="90" nowrap="" valign="bottom"> -0.8939

17/10/2016 width="90" nowrap="" valign="bottom"> -0.00799 width="90" nowrap="" valign="bottom"> 0.001584 width="90" nowrap="" valign="bottom"> -0.00957 width="90" nowrap="" valign="bottom"> -1.42482

18/10/2016 width="90" nowrap="" valign="bottom"> -0.00073 width="90" nowrap="" valign="bottom"> 0.001528 width="90" nowrap="" valign="bottom"> -0.00226 width="90" nowrap="" valign="bottom"> -0.3368

19/10/2016 width="90" nowrap="" valign="bottom"> 0.01508 width="90" nowrap="" valign="bottom"> 0.001548 width="90" nowrap="" valign="bottom"> 0.013532 width="90" nowrap="" valign="bottom"> 2.014016

20/10/2016 width="90" nowrap="" valign="bottom"> -0.00615 width="90" nowrap="" valign="bottom"> 0.001678 width="90" nowrap="" valign="bottom"> -0.00782 width="90" nowrap="" valign="bottom"> -1.16435

21/10/2016 width="90" nowrap="" valign="bottom"> -0.0107 width="90" nowrap="" valign="bottom"> 0.00159 width="90" nowrap="" valign="bottom"> -0.01229 width="90" nowrap="" valign="bottom"> -1.82902

On 30^th September 2016, PTI registered a protest against PM over the Panama case in Riwand Lahore. PTI argued that PM should leave the office until the Supreme Court of Pakistan declares the decision of the Panama case. Table 8 describes all the statistics results for the event. The standard deviation for the event was calculated on the same methodology. All values of “t-test” of this activity are insignificant which means that there is no impact of the event on PSX. Only on day 14, the “t-statistics” value for the event is 2.014016, which declares that market restored form the effect of the news.

Table 9. SD: 0.006831

> > > > > > > > > > > > > > > >

Date width="91" nowrap="" valign="bottom"> Return width="91" nowrap="" valign="bottom"> ER width="91" nowrap="" valign="bottom"> AR width="91" nowrap="" valign="bottom"> t-test

20/04/2017 width="91" nowrap="" valign="bottom"> 0.019606 width="91" nowrap="" valign="bottom"> 0.001292 width="91" nowrap="" valign="bottom"> 0.018314 width="91" nowrap="" valign="bottom"> 2.68101

21/04/2017 width="91" nowrap="" valign="bottom"> 0.008075 width="91" nowrap="" valign="bottom"> 0.001409 width="91" nowrap="" valign="bottom"> 0.006666 width="91" nowrap="" valign="bottom"> 0.975812

24/04/2017 width="91" nowrap="" valign="bottom"> -0.00654 width="91" nowrap="" valign="bottom"> 0.001503 width="91" nowrap="" valign="bottom"> -0.00804 width="91" nowrap="" valign="bottom"> -1.17697

25/04/2017 width="91" nowrap="" valign="bottom"> 0.00085 width="91" nowrap="" valign="bottom"> 0.001391 width="91" nowrap="" valign="bottom"> -0.00054 width="91" nowrap="" valign="bottom"> -0.07913

26/04/2017 width="91" nowrap="" valign="bottom"> -0.00696 width="91" nowrap="" valign="bottom"> 0.001402 width="91" nowrap="" valign="bottom"> -0.00837 width="91" nowrap="" valign="bottom"> -1.22472

27/04/2017 width="91" nowrap="" valign="bottom"> -0.00366 width="91" nowrap="" valign="bottom"> 0.001326 width="91" nowrap="" valign="bottom"> -0.00499 width="91" nowrap="" valign="bottom"> -0.72997

28/04/2017 width="91" nowrap="" valign="bottom"> -0.01248 width="91" nowrap="" valign="bottom"> 0.001197 width="91" nowrap="" valign="bottom"> -0.01368 width="91" nowrap="" valign="bottom"> -2.0023

02/05/2017 width="91" nowrap="" valign="bottom"> -0.00173 width="91" nowrap="" valign="bottom"> 0.001065 width="91" nowrap="" valign="bottom"> -0.0028 width="91" nowrap="" valign="bottom"> -0.4095

03/05/2017 width="91" nowrap="" valign="bottom"> 0.013863 width="91" nowrap="" valign="bottom"> 0.001113 width="91" nowrap="" valign="bottom"> 0.012749 width="91" nowrap="" valign="bottom"> 1.866425

04/05/2017 width="91" nowrap="" valign="bottom"> 0.011449 width="91" nowrap="" valign="bottom"> 0.001275 width="91" nowrap="" valign="bottom"> 0.010174 width="91" nowrap="" valign="bottom"> 1.489419

05/05/2017 width="91" nowrap="" valign="bottom"> 0.021527 width="91" nowrap="" valign="bottom"> 0.001348 width="91" nowrap="" valign="bottom"> 0.020179 width="91" nowrap="" valign="bottom"> 2.954032

08/05/2017 width="91" nowrap="" valign="bottom"> 0.002691 width="91" nowrap="" valign="bottom"> 0.001526 width="91" nowrap="" valign="bottom"> 0.001164 width="91" nowrap="" valign="bottom"> 0.170456

09/05/2017 width="91" nowrap="" valign="bottom"> 0.000595 width="91" nowrap="" valign="bottom"> 0.001566 width="91" nowrap="" valign="bottom"> -0.00097 width="91" nowrap="" valign="bottom"> -0.14212

10/05/2017 width="91" nowrap="" valign="bottom"> 0.006291 width="91" nowrap="" valign="bottom"> 0.001548 width="91" nowrap="" valign="bottom"> 0.004742 width="91" nowrap="" valign="bottom"> 0.694239

11/05/2017 width="91" nowrap="" valign="bottom"> 0.006298 width="91" nowrap="" valign="bottom"> 0.001563 width="91" nowrap="" valign="bottom"> 0.004735 width="91" nowrap="" valign="bottom"> 0.69317

12/05/2017 width="91" nowrap="" valign="bottom"> 0.012233 width="91" nowrap="" valign="bottom"> 0.001563 width="91" nowrap="" valign="bottom"> 0.01067 width="91" nowrap="" valign="bottom"> 1.562039

On 4^th April 2017, the Supreme Court of Pakistan ordered to form a Joint investigation team for Panama case against PM Nawaz Sharif. The Joint Investigation Team was consisting of 6 members from different departments of Pakistan, including military forces. Table 9 shows the statistical calculations for the event. The standard deviation for the event, which is calculated on basis on pre-event window, is 0.006831.  On the 1^st and 11^th day of JIT announcement, “t-statistics” values for the event were 2.68101 and 2.954032 which are significant. It shows that investor confidence in JIT and Supreme Court decisions were increased.

Table 10. SD: 0.012322

> > > > > > > > > > > > > > > >

Date width="93" nowrap="" valign="bottom"> Return width="84" nowrap="" valign="bottom"> ER width="93" nowrap="" valign="bottom"> AR width="93" nowrap="" valign="bottom"> t-test

28/07/2017 width="93" nowrap="" valign="bottom"> 0.002141 width="84" nowrap="" valign="bottom"> -0.00065 width="93" nowrap="" valign="bottom"> 0.002796 width="93" nowrap="" valign="bottom"> 0.226928

31/07/2017 width="93" nowrap="" valign="bottom"> 0.011302 width="84" nowrap="" valign="bottom"> -0.00062 width="93" nowrap="" valign="bottom"> 0.011921 width="93" nowrap="" valign="bottom"> 0.967439

01/08/2017 width="93" nowrap="" valign="bottom"> 0.008892 width="84" nowrap="" valign="bottom"> -0.00054 width="93" nowrap="" valign="bottom"> 0.009429 width="93" nowrap="" valign="bottom"> 0.765171

02/08/2017 width="93" nowrap="" valign="bottom"> 0.002878 width="84" nowrap="" valign="bottom"> -0.0005 width="93" nowrap="" valign="bottom"> 0.003379 width="93" nowrap="" valign="bottom"> 0.274213

03/08/2017 width="93" nowrap="" valign="bottom"> -0.00441 width="84" nowrap="" valign="bottom"> -0.00048 width="93" nowrap="" valign="bottom"> -0.00393 width="93" nowrap="" valign="bottom"> -0.31858

04/08/2017 width="93" nowrap="" valign="bottom"> -0.00882 width="84" nowrap="" valign="bottom"> -0.00052 width="93" nowrap="" valign="bottom"> -0.0083 width="93" nowrap="" valign="bottom"> -0.67353

07/08/2017 width="93" nowrap="" valign="bottom"> -0.01051 width="84" nowrap="" valign="bottom"> -0.0006 width="93" nowrap="" valign="bottom"> -0.00992 width="93" nowrap="" valign="bottom"> -0.80465

08/08/2017 width="93" nowrap="" valign="bottom"> 0.000416 width="84" nowrap="" valign="bottom"> -0.00069 width="93" nowrap="" valign="bottom"> 0.001108 width="93" nowrap="" valign="bottom"> 0.089944

09/08/2017 width="93" nowrap="" valign="bottom"> -0.00796 width="84" nowrap="" valign="bottom"> -0.00066 width="93" nowrap="" valign="bottom"> -0.00731 width="93" nowrap="" valign="bottom"> -0.59289

10/08/2017 width="93" nowrap="" valign="bottom"> -0.0076 width="84" nowrap="" valign="bottom"> -0.00063 width="93" nowrap="" valign="bottom"> -0.00697 width="93" nowrap="" valign="bottom"> -0.56574

11/08/2017 width="93" nowrap="" valign="bottom"> -0.03115 width="84" nowrap="" valign="bottom"> -0.00076 width="93" nowrap="" valign="bottom"> -0.0304 width="93" nowrap="" valign="bottom"> -2.46669

15/08/2017 width="93" nowrap="" valign="bottom"> 0.006528 width="84" nowrap="" valign="bottom"> -0.00098 width="93" nowrap="" valign="bottom"> 0.007508 width="93" nowrap="" valign="bottom"> 0.609272

16/08/2017 width="93" nowrap="" valign="bottom"> -0.02407 width="84" nowrap="" valign="bottom"> -0.00085 width="93" nowrap="" valign="bottom"> -0.02322 width="93" nowrap="" valign="bottom"> -1.88447

17/08/2017 width="93" nowrap="" valign="bottom"> -0.00134 width="84" nowrap="" valign="bottom"> -0.00106 width="93" nowrap="" valign="bottom"> -0.00027 width="93" nowrap="" valign="bottom"> -0.02218

18/08/2017 width="93" nowrap="" valign="bottom"> -0.02171 width="84" nowrap="" valign="bottom"> -0.00107 width="93" nowrap="" valign="bottom"> -0.02064 width="93" nowrap="" valign="bottom"> -1.67471

21/08/2017 width="93" nowrap="" valign="bottom"> -0.00405 width="84" nowrap="" valign="bottom"> -0.00126 width="93" nowrap="" valign="bottom"> -0.00278 width="93" nowrap="" valign="bottom"> -0.22573

On 28^th July 2017, the Supreme Court announced the decision of Panama case and disqualified Prime Minister over hiding illegal assets and uncollected income for tax evasion. Table 10 demonstrates the statistical results for the event. The SD for the event is 0.012322 which was calculated on the same methodology. Although, event was a shock for the market the event didn’t show any impact on PSX. Only on day 11, the “t-statistics” value for the event was -2.46669 which is significant. It may be because of different views on appointing new Finance Minister. 

Conclusion

In every country, stability in the political system plays a vital role in the financial markets of the country. It increases investor confidence. While on the other hand political instability creates uncertainty in the country which reflects the investment graph. Mostly investor believes that political instability cause changes in the country policies.  The aim of the study is to find the association of political variation with stock market. This research study considered 10 major political activities in Pakistan, in order to test the significant relationship between political variation and behavior of PSX.

The result of this research study demonstrates that some political activities are significantly related to the stock market, while some activities don’t have the potential to significantly fluctuate the graph of stock market return. It depends on the intensity of political activity. Investor responds positively when government organization looks confident for implementing rule of law. Three out of ten events were significant which affect the performance of PSX 100. It means that political events which cause movement in government organization for implementing rule of law against the ruling government do have impact on PSX 100. Further, the study found that small political events are not statistically significant and not able to influence volatility in returns. 

The study is applicable for investors to make the decision for their investment in the consideration of political events. By taking international political events, the study could be extended.  

Recommendations

The political situation of Pakistan is the most important factor to be considered. Political stability in a country not only important for the social development of a country but should also be considered to boost the economic growth of a country. Greater political stability and strong rule of law and order are vital factors for economic growth. The deadly wave of terrorism already hit the economy for Pakistan. Political instability could increase terrorist acts in the country. To control terrorism and improve economic and social development in the country, government of Pakistan should make short and long term free-Political strategies.    

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