Are Equity markets Efficient? Evidence from Emerging ... - SSRN papers

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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

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Are Equity markets Efficient? Evidence from Emerging Economy Syed Kashif Saeed(Corresponding Author), Shahid Mehmood Sargana, Usman Ayub COMSATS Institute of Information Technology, Islamabad, Pakistan.

Abstract This study investigates that whether Karachi Stock Exchange is efficient or otherwise over the period from July 1997 to April 2010. To examine the efficiency, various stock market anomalies (i.e. Days of week Effect, With-in month Effect, Turn of Month Effect) have been estimated. Results show no evidence for presence of Days of week Effect whereas the presence of other two effects cannot be rejected. JEL Code:

C10, G12, G15

Keywords: Efficient Market Hypothesis, Stock Market Anomalies, day of the week (DoW) effect, Within Month (WiM) effect, turn of the month (ToM) effect.

Introduction A market anomaly (or inefficiency) is a price or return distortion in a financial markets which can be caused by lack of stock market transparency or due to other reasons, so these anomalies are in contradiction of common belief in finance that financial markets are efficient. This important belief in financial markets that security prices reflect, fully and quickly, all available information is known as Efficient market Hypothesis (EMH). Initially, the concept of EMH was applied to the stock market only whereas later on it was taken as a general notion to all financial assets (Fama 1970; Fama 1991). So when market uses all available information prudently and nothing is ignored provided the avoidance of systematic errors, the results that prices are always at the levels corroborating with its fundamentals . Although, theoretically very well received but when tested empirically then various deviation from EMH were found known as market Anomalies. Some famous anomalies are Monday effect, weekend effect, January effect etc. Mehdian and Perry (2001) argue that once identified anomalies began to wipe out. This study intends to analyze whether these market anomalies exist in equity markets of Pakistan in one way or another. We have done so first by taking the whole sample and then by breaking the sample into various sub periods in order to examine the time varying impact also.

Literature Review When various market anomalies were identified then researchers tried to provide the theoretical justification for these. For example, as EMH assumes the availability of full information free of cost therefore if information was not costless then financial incentive exists for obtaining it. But again if the information was already fully reflected in security prices then financial incentive became impossible to obtain (Grossman and Stiglitz 1980). Jensen (1978) argued that prices reflect information up to the point where the expected profits to be made by acting on the information do not exceed the marginal costs of obtaining it. It is also observed that

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anomalies began to disappear once they are identified (Mehdian and Perry, 2001). In this section, we will look at various types of empirically founded market anomalies. The January effect is generally defined as that the expected returns of stocks will be relatively higher in first month of the year in comparison of the rest of the month. This effect was initially registered by Rozeff and Kinney (1976). Later on it is also documented, in same US markets, that this January Effect is restricted to first few days of month of January only (Keim, 1983). Gultekin and Gultekin (1983) investigate the 17 countries (including Australia, Belgium, Germany, Japan, Norway, Spain, and Switzerland etc) for January effect. They argue that significance of January effect cannot be rejected in these countries. The January Effect, i.e. higher mean daily return in the month of January, is also found significant in Singapore market by various researchers9. The day-of-the-week effect allows average stock returns to be different across various days of the week. One specific variant of DoW effect is Monday Effect according to which, the mean return on Monday is negative and generally the lowest while the mean return on Friday is positive and generally the highest . Cross (1973) and Board and Sutcliffe (1988) identified these effect and later on various researcher10 augmented the research in other markets as well. Extensive research have been conducted to investigate the presence of the DoW effect across the globe [French (1980), Gibbons and Hess (1981), Keim and Stambaugh (1984), Wong and Ho (1986), Condoyanni et al (1987), Lakonishok and Smidt (1988), Aggarwal and Rivoli (1989), Wong et al (1992), Abraham and Ikenberry (1994), Wang et al (1997) and Mehdian and Perry (2001)]. This extensive research also concludes that in near past various anomalies11 has significantly declined. (Mehdian and Perry, 2001; Wong et al, 2006) Another phenomenon that the average stock return is higher on the trading day immediately before holiday in comparison with rest of trading days is referred as Holiday Effect in financial literature. Pettengill (1989) and Ariel (1990) are among those who initially investigated Holiday Effect in US markets and found significant, i.e. the average stock returns are significantly higher on holidays than the remaining trading days. Kim and Park (1994) later on corroborated their research findings and argue that null hypothesis for Holiday Effect cannot be accepted. In contrast to US markets, results for Holiday Effects are mixed across other stock markets of the world, for example Holiday Effect cannot be documented in France, Italy, Switzerland, UK and West Germany but cannot be rejected in Australia, Canada, Hong Kong, Japan, Singapore market (Cadsby and Ratner, 1992; Tan and Wong, 1996). Within month effect (WiM) or Time of Month (ToM) Effect refers to change in average return as month progresses. Ariel (1987) documented that average returns are relatively higher in the start of month whereas lower for rest of the month. Later on various researchers have identified the start and remains of month in literature in different ways (e.g. Peterson, 1990, Kohers and 9

See Agarwal and Rivoli (1989); Wong and Ho (1986); Lee (1992) and Chan et al (1996) See Chang et al. 1993, Coutts and Hayes, 1999, Al-Loughani and Chappell, 2001) 11 Monday Effect and January Effects etc 10

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Patel, 1999). Some divide the month into two parts, distinguishing between the first half and second half of the month (Ariel, 1987) whereas some researchers distribute month into three equal parts that is each third of the month (Kohers and Patel, 1999). Chandra (2009) argues in favor of presence of Within month effect in Indian stock markets. Another variant of WiM Effect is the ToM effect. ToM effect refers to the phenomenon that at the turn of the month, unusual higher stock returns are evidenced. In literature, ToM effect is defined as the period from the last trading day of the previous month to the first three trading days of the current month . The empirical evidences regarding the presence of ToM effect are not conclusive among various markets across the world. Lakonishok and Smidt (1988) investigate and find significant ToM effect, using the Dow Jones index for the period of 1897-1986. Cadsby and Ratner (1992) argue that null hypothesis for the turn-of-the-month effects cannot be rejected in France, Hong Kong, Italy or Japan but can be rejected in Australia, Canada, UK and West Germany. Tan and Wong (1996) investigate and find a significant turn-of-the-month effect in Singapore markets over the period 1975-1994. Chandra (2009) argues that Turn-of-the-Month effect cannot be rejected in Indian stock markets.

Data: The purpose of the present study was to investigate the presence of various stock market anomalies in Karachi stock exchange. Our sample period consist of July 01, 1997 to April 30, 2010. The price has been taken from index of Karachi stock exchange called KSE-100. As time period is relatively long, therefore we have decided to further divide the sample into sub sample of almost three years each. The division of sample period into further sub periods will help in enhancing understanding regarding the evolution process in stock market, if any. Whole sample period: Sub- sample period: First period Second period Third period Fourth period

July 01, 1997 to April 30, 2010 July 01, 1997 to June 30, 2000 July 01, 2000 to June 30, 2003 July 01, 2003 to June 30, 2006 July 01, 2006 to April 30, 2010

Methodology In order to test various market anomalies, daily return of stock prices i.e. continuously compounded rate of change is calculated using the following formula: Rt

ln(

Pt

Pt

) *100 1

Where Pt and Pt-1 are the prices index of Karachi Stock Exchange at time t and at t-1 respectively. COPY RIGHT © 2011 Institute of Interdisciplinary Business Research

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The basic regression equation for week day effect is as follows: Rt

1

D1

2

D2

3

D4

4

D4

5

D5

(1)

We have not included here intercept to avoid dummy variable trap. We can avoid that by not including intercept or by reducing one dummy e.g. D3 for Wednesday. One problem indicated by econometricians that the error term of above regression can have autocorrelation problem. To avoid this problem we have added the lagged value of returns as well. n

Rt

1 D1

2 D2

3 D4

4 D4

5 D5

Rt

(2)

i

i 1

Where: D1 = 1 if there is Monday; 0 otherwise. D2 = 1 if there is Tuesday; 0 otherwise. D3 = 1 if there is Wednesday; 0 otherwise. D4 = 1 if there is Thursday; 0 otherwise. D5 = 1 if there is FRIDAY; 0 otherwise. Rt-i = Lagged values of returns For analyzing Within Month Effect, following regression equation will be estimated: Rt

7

D7

8

D8

9

D9

(3)

Where: D7= 1 if the trading days are from 1st to 10th of the month; 0 otherwise. D8 = 1 if the trading days are from 11th to 20th of the month; 0 otherwise. D9 = 1 if the trading days are from 21st to end of the month; 0 otherwise. For analyzing Turn of the Month (ToM) Effect, Hansel and Ziemba (1996) has suggested the event window of five days i.e. last two trading days of month and first three trading days of following month (-2, +3). So according to same intuition following regression equation will be estimated: Rt

TOM

0

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(4)

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Where: TOM= 1 if trading days are last two trading days and first three trading days adjacent months; 0 otherwise.

Findings Table 1 shows the descriptive statistics of the daily returns of the Karachi stock Exchange index 100 for the entire period from July 1997 to April 2010. The first column reports statistics for the entire sample period. The average (mean) daily return was negative at 0.06 percent (almost 21.6% annualized). The null hypothesis for normality is rejected at 1% level of significance, as shown by majority of the studies that financial time series are not normal. The second column reports statistics for the sub-sample period from July 1997 to June 2000. The average (mean) daily return was at 0.03 percent (almost 10.8% annualized). Here also, the null hypothesis for normality is rejected at 1% level of significance, as shown by majority of the studies that financial time series are not normal. The third column reports statistics for the subsample period from July 2000 to June 2003. The average (mean) daily return was negative at 0.10 percent (almost 30.7% annualized). The fourth column reports statistics for the sub-sample period from July 2003 to June 2006. The fifth column reports statistics for the sub-sample period from July 2006 to April 2009.

Table 1:

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

Descriptive Statistics of the daily returns of the Karachi Stock Exchange Index (KSE100) Full Sample Sub-Sample Sub-Sample Sub-Sample Sub-Sample 1997-2010 1997-2000 2000-2003 2003-2006 2006-2010 -0.000557 0.000225 -0.001038 -0.001771 0.000161 -0.000095 0.000000 -0.001500 -0.001571 0.000000 0.132133 0.052784 0.060418 0.077414 0.132133 -0.127622 -0.082547 -0.057967 -0.085071 -0.127622 0.017139 0.015468 0.015427 0.014866 0.020869 0.364332 0.217971 0.477042 0.118838 0.370527 8.408492 5.600692 4.705473 7.200085 8.893271

Jarque-Bera Probability

4154.692 0.000000

226.8626 0.000000

124.2738 0.000000

578.1086 0.000000

1469.992 0.000000

Observations

3348

783

781

784

1000

Table 2, 3, 4, 5 and 6 shows the results of regression model in equation 2 for estimating Days of Week (DoW) effect. Table 2 consists of regression results for the entire sample i.e. from 1997 to


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2010. The coefficients of all dummy variables are insignificant that we cannot reject null hypothesis for days-of-week effects. Table 2: Regression Model (2); July 1997 to April 2010 Sample (adjusted): 7/03/1997 4/30/2010 Coefficien Variable t Std. Error t-Statistic Prob. 0.000486 0.000661 Mon -0.735597 0.4620 Tue 0.001001 0.000660 -1.516555 0.1295 Wed 0.000596 0.000660 -0.903807 0.3662 Thu 0.000391 0.000660 -0.592412 0.5536 Fri 0.000058 0.000660 -0.087895 0.9300 RPKP(-1) 0.091187 0.017228 5.292810 0.0000 The non-presence of days-of-week effects can be the indication of informationally efficiency of markets. It can also be said that participants in this market are aware of various anomalies documented by researchers around the globe which is being taken away through the process of arbitrage. Table 3, 4, 5 and 6 consists of regression results for the sub-sample from 1997 to 2000, 2000-2003, 2003-2006 and 2006-2010 respectively. The above regression model was estimated for sub sample for robustness of results and also to see that whether this effect has been time varying across sample period. Largely, leaving 2000-2003 periods, result for days-of-week effects are same. Therefore we can safely accept the null hypothesis for absence of days-of-week effects.

Table 3: Regression Model (2); July 1997 to June 2000 Sample (adjusted): 7/03/1997 6/30/2000 Coefficien Variable t Std. Error t-Statistic Prob. Mon -0.000387 0.001209 -0.320251 0.7489 Tue -0.000468 0.001206 -0.387961 0.6982 Wed 0.001411 0.001206 1.169856 0.2424 Thu -0.001755 0.001211 -1.448756 0.1478 Fri 0.002055 0.001209 1.699626 0.0896 RPKP(-1) 0.220555 0.035012 6.299345 0.0000


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Table 4: Regression Model (2); July 2000 to June 2003 Sample: 7/03/2000 6/30/2003 Coefficien Variable t Std. Error t-Statistic Prob. Mon -0.002239 0.001228 -1.822859 0.0687 Tue -0.001793 0.001231 -1.456064 0.1458 Wed -0.001353 0.001231 -1.098926 0.2721 Thu -0.001008 0.001231 -0.818153 0.4135 Fri 0.001632 0.001233 1.323439 0.1861 RPKP(-1) 0.081662 0.035801 2.280972 0.0228

Table 5: Regression Model (2); July 2003 to June 2006 Sample: 7/01/2003 6/30/2006 Variable Coefficient Std. Error t-Statistic Prob. Mon 0.000238 0.001193 0.199485 0.8419 Tue -0.002360 0.001192 -1.979653 0.0481 Wed -0.003329 0.001189 -2.800258 0.0052 Thu -0.002250 0.001187 -1.895489 0.0584 Fri -0.001235 0.001186 -1.041293 0.2981 RPKP(-1) -0.010506 0.035850 -0.293058 0.7696

Table 6: Regression Model (2); July 2006 to April 2010 Sample: 7/03/2006 4/30/2010 Coefficien Variable t Std. Error t-Statistic Prob. Mon 0.000071 0.001472 0.047845 0.9618 Tue -0.000115 0.001472 -0.078246 0.9376 Wed 0.000534 0.001473 0.362330 0.7172 Thu 0.002303 0.001473 1.563603 0.1182 Fri -0.002053 0.001472 -1.395186 0.1633 RPKP(-1) 0.079871 0.031617 2.526216 0.0117

Table 7, 8, 9, 10, and 11 shows the results of regression model in equation 3 for estimating Within-month effect. Table 7 consists of regression results for the entire sample i.e. from 1997 to 2010.


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Table 7: Regression Model (3); July 1997 to April 2010 Sample (adjusted): 7/02/1997 4/30/2010 Coefficien Variable t Std. Error t-Statistic Prob. D7 -0.001924 0.000515 -3.734197 0.0002 D8 -0.000877 0.000543 -1.614151 0.1066 D9 0.000897 0.000483 1.858290 0.0632

Table 8, 9, 10, and 11 consists of regression results for the sub-sample from 1997 to 2000, 20002003, 2003-2006 and 2006-2010 respectively. The above regression model was estimated for sub sample for robustness of results and also to see that whether this effect has been time varying across sample period. Table 8: Regression Model (3); July 1997 to June 2000 Sample (adjusted): 7/02/1997 6/30/2000 Coefficien Variable t Std. Error t-Statistic Prob. D7 -0.002134 0.000958 -2.226906 0.0262 D8 0.000660 0.001008 0.654211 0.5132 D9 0.001961 0.000901 2.177742 0.0297 The results for with-in-month Effect largely reject the null hypothesis of market anomaly. Leaving sub sample of 2006-2010, the coefficients for D7 (first 10 days of month) are significant for entire sample period and also for sub sample periods, whereas both D7 (first 10 days of month) and D9 (last 10 days of month) are significant at 1% for full sample period.

Table 9: Regression Model (3); July 2000 to June 2003 Sample: 7/03/2000 6/30/2003 Coefficien Variable t Std. Error t-Statistic Prob. D7 -0.002550 0.000960 -2.655051 0.0081 D8 -0.001269 0.001017 -1.247742 0.2125 D9 0.000457 0.000896 0.510267 0.6100

Table 10: Regression Model (3); July 2003 to June 2006 Sample: 7/01/2003 6/30/2006 Coefficien Variable t Std. Error t-Statistic Prob. D7 -0.002685 0.000929 -2.888911 0.0040 D8 -0.001398 0.000972 -1.438209 0.1508 D9 -0.001273 0.000867 -1.467352 0.1427


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Table 11: Regression Model (3); July 2006 to April 2010 Sample: 7/03/2006 4/30/2010 Variable Coefficient Std. Error t-Statistic Prob. D7 -0.000686 0.001145 -0.598661 0.5495 D8 -0.001372 0.001213 -1.131486 0.2581 D9 0.002119 0.001077 1.966818 0.0495 Table 12, 13, 14, 15, and 16 shows the results of regression model in equation 4 for estimating turn of month (TOM effect. Table 12 consists of regression results for the entire sample i.e. from 1997 to 2010 whereas Table 13, 14, 15, and 16 consists of regression results for the sub-sample from 1997 to 2000, 2000-2003, 2003-2006 and 2006-2010 respectively. The TOM effect is significant when tested for entire sample period at 5% level of significance. When same effect is tested for each sub sample, the coefficients of TOM is not significance in all, except in 2000-2003, sub sample period.

Table 12: Regression Model (4); July 1997 to April 2010 Sample (adjusted): 7/02/1997 4/30/2010 Variable Coefficient Std. Error t-Statistic Prob. C -0.000207 0.000337 -0.613178 0.5398 TOM -0.001522 0.000704 -2.163079 0.0306

Table 13: Regression Model (4); July 1997 to June 2000 Sample (adjusted): 7/02/1997 6/30/2000 Variable Coefficient Std. Error t-Statistic Prob. C 0.000489 0.000629 0.777162 0.4373 TOM -0.001157 0.001317 -0.878984 0.3797

Table 14: Regression Model (4); July 2000 to June 2003 Sample: 7/03/2000 6/30/2003 Variable Coefficient Std. Error t-Statistic Prob. C -0.000555 0.000628 -0.884358 0.3768 TOM -0.002107 0.001112 -1.894787 0.0907

Table 15: Regression Model (4); July 2003 to June 2006 Sample: 7/01/2003 6/30/2006 Coefficien Variable t Std. Error t-Statistic Prob. C -0.001465 0.000605 -2.420302 0.0157 TOM -0.001326 0.001260 -1.052740 0.2928


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Table 16: Regression Model (4); July 2006 to April 2010 Sample: 7/03/2006 4/30/2010 Variable Coefficient Std. Error t-Statistic Prob. C 0.000505 0.000752 0.671702 0.5019 TOM -0.001498 0.001568 -0.955229 0.3397

Conclusion: This paper provides a detailed examination of the daily stock returns of the KSE100 index in Pakistan. KSE 100 index is an index based on Market capitalization of 100 companies in Karachi stock exchange in Pakistan, which make it a good representative of the equity market in Pakistan. According to my review, this is the first paper to examine, Days of week Effect, Within Month effect, Turn of Month Effect in on paper for Karachi stock Exchange. We have used data for the period July 1997 to April 2010, and further divide the sample period into four sub sample to analyze any effect which may be time varying as well. To our surprise, in majority of sample period, except for July 2000 to June 2003, we find no evidence for Days of week effect. This result is in line with Mehdian and Perry (2001) and Wong et al (2006) arguing that anomalies began to disappear once identified. The results for WiM Effect show significant difference for the trading days in three parts of the month, suggesting the presence of WiM Effect. Whereas the results for Turn-of-Month (TOM) effect show significant return when month ends and new month start, suggesting the presence of TOM Effect. It is important to note that that TOM effect is very much evident in full sample but when estimated for sub sample than TOM effect is not present, may be due to short of data in sub sample.


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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS References

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