Revisiting Day of the Week Effect in Indian Stock Market

In recent twelvemonths the examineing of commercialize place anomalies in seam strikes has snuff it an active field of research in empirical finance and has been receiving attention not only from academic journals but in like manner from the financial press as well. Among the more well-known anomalies ar the coat effect, the January effect and the day-of-the calendar workweek effect. According to this phenomenon, the average daily drive out of the commercialise is not the same for all geezerhood of the week, as we would expect on the basis of the efficient market theory. The objective of this paper is to examine the world of day of week effect in Indian subscriber line market.Daily mop up outlays of S&P CNX slap-up business leader eat up been analyzed over fifteen years stay commencing from January 1994 to declination 2008. A coif of parametric and non parametric tests has been use to test the equality of pie-eyed returns and step losss of the returns. The tight tax returns on Monday and Tuesday atomic number 18 nix fleck on Wednesday these ar extremely positive. Also, the impact of introduction of trilled colonisation on the birth returns is observed. The results visualise that before rolling resolution came in 2001, Tuesday was showing highly contradict returns and Wednesday highly positive. yet after the introduction of rolling law of closure, the seasonality in the diffusion of the rigorous returns crosswise divergent eld of the week ceased to appear. therefore the markets acquire become more efficient over a period of time. KEY rowing Market Efficiency, calendar Anomalies and Day-of-the-Week Effect INTRODUCTION A farm animal telephvirtuoso exchange is a common platform where buyers and sellers come together to transact in securities. It may be a physical entity where brokers trade on a physical work floor via an open outcry system or a virtual environment.The Stock Exchange, Mumbai (bovine spongifor m encephalitis) and the National Stock Exchange (NSE) are the Indias two leading stock exchanges. Indian security market is one of the oldest markets in Asia. It has come a long way from earlier years of floor trading to the present(a) day screen and net base trading. This study is an attempt to demand a deeper insight in to the behaviour and patterns of stock equipment casualty distribution in the Indian stock market. The bell of a security should vib mark around its intrinsic worth in any efficient market.In finance, the efficient-market venture (EMH) asserts that financial markets are informationally efficient, or that costs on traded assets, e. g. , stocks, bonds, or property, already reflect all known information. The efficient-market opening states that it is impossible to consistently outperform the market by using any information that the market already knows, except through luck. Therefore, the past expenditure movements can in no way help in speculating the pr ices in prox. The price of each day is independent. It may be unchanged, high or first-class honours degreeer from he previous price, but depends upon new pieces of information being received each day. So seasonalities cannot be used to formulate trading strategies to earn ab recipe returns according to efficient market hypothesis theory. Calendar anomalies are cyclical anomalies in returns, where the cycle is establish on the calendar. It describes the temperament of stocks to perform antithetically at distinguishable times. For example, a number of researchers check documented that historically, returns tend to be higher in January compared to former(a) months (especially February).There are three types of efficiencies as explained in efficient market hypothesis. So calendar anomalies mainly explain weak form of efficiency which says that previous price changes or changes in return are useless in predicting future price or return changes. Some of the calendar anomalies are Month-of-the year effect, Month-of-the quarter effect, Week-of-the month effect, Day-of-the-week effect or Weekend effect, Monday effect, Hour-of-the-day effect or the End of the-day effect, holiday effect and turn of the month effect etcetera Among them the day-of-the-week effect is well-nigh widely documented crosswise the countries and markets.In context to stock market the majority of research keyings, indicates that the stock returns remain low or banish on Monday. This paper examines the day-of-the-week effect in Indian stock market, using SP CNX corking data of last fifteen years from January 1994 to celestial latitude 2008. REVIEW OF LITERATURE There is an extensive publications on the day-of-the-week effect in the stock returns. This section examines a a couple of(prenominal) research works on the day of the week effect in Indian and international stock markets. Ziemba (1993) investigated the weekend hypothesis for the Japanese market using daily data from 1949 to 19 88.Tuesday recorded negative returns following a one day weekend and Mon long time declined after two age weekends. Balaban (1994) name day of the week effect in an emerging stock market ISECI of a developing country Turkey for the period 1988 to 1994. Highest returns on Friday and last-place returns on Tuesday were observed. Mishra (1999) studied day of the week effect in Indian stock market using Sensex and Natex for the period 1986 to 1998 indicating the movement of day of the week effect in Indian stock market. Friday returns were launch highest and significantly incompatible from the pixilated returns of other days. Hence thither exists a Friday effect.Berument and Halil Kiymaz (2001) tested the presence of the day of the week effect on stock market exci deferness by using the SP 500 market index during the period of January 1973 and October 1997. The findings showed that the day of the week effect is present in some(prenominal) unpredictability and return equations. W hile the highest and net returns were observed on Wednesday and Monday, the highest and the final volatility were observed on Friday and Wednesday, respectively. move on investigation of sub-periods reinforced findings that the volatility pattern across the days of the week was statistically different.Sarma (2004) examined seasonality across the days of week in Indian stock market using BSE indices- SENSEX, NATEX and BSE 200. Highest variance on Monday was found and weekend effect was substantiate by this study. Nath and Dalvi (2004) examined the day of the week anomaly in Indian stock market for the period from 1999 to 2003 using index SP CNX bang-up data. The study found that before introduction of rolling settlement in January 2002, Monday and Friday were significant days. However after the introduction of the rolling settlement, Friday became significant. Mondays were found to have higher standard deviations followed by Fridays.Davidsson (2006) found evidence of day of week effect in SP 500 index. Davidsson found Wednesday was the weekday with highest rate of return and Monday was weekday with low rate of return. Also Monday was the only day with negative rate of return. Wednesdays returns were found approximately four times of Mondays returns. Badhani (2008) examined the presence of day-of-the-week effect on stock returns, trading intensity level and price volatility at the NSE during the period of 10 years from 1995-2005. Wednesday effect was found during earlier periodical settlement regime which now disappeared.Monday and Tuesday returns were consistently low but during recent sub period these were not significantly different from other days of week. Also on Monday the average trading volume was significantly low and price volatility was high consistently across the entire sample period. Mangala (2008) examined day-of-the-week effect in sub periods in Indian stock market using SP CNX groovy data. Highest returns on Wednesday and lowest on Tues day were observed. Also findings showed that seasonality in return distribution across weekdays was confined to pre rolling settlement time period thereafter seasonality vanished.DATA AND methodological analysis This study covers a sample period of fifteen years from January 1, 1994 to December 31, 2008 comprising a total of 3695 observations(days). The stock prices are contained by SP CNX Nifty index. The closing evaluates of this index have been obtained from the formal website of National Stock Exchange (www. nseindia. com). There was trading on trusted weekly closing days (i. e. 18 Saturdays and 3 Sundays) these days have been excluded from the sample. During the above sample period of fifteen years many geomorphologic changes also took place in the market.For example rolling settlement was introduced in place of weekly settlement system. Therefore, the behaviour of stock prices has been studied on an yearly basis so as to gauge the impact of these changes on the stock price s. Measuring the Daily Returns Daily pct return on the index for a give day of the week has been calculated by subtracting the closing price of the previous trading day from closing price of that day, then dividing the resulting no. by closing price as on the previous trading day and multiplying by 100. Rt = Pt-Pt-1 * 100 Pt-1 Rt is daily return on the share price index for day tPt is the closing grade of index for the dayt and Pt-1 is the closing value of the index for the preliminary day. Hypothesis and Testing Procedure The null hypothesis is that there are no differences in the mean daily returns across the weekdays. The non parametric Kruskall- Wallis (H) test has been applied to test seasonality in returns across weekdays to test the hypothesis. Null hypothesis is Ho 1= 2= 3= 4= 5 Here, 1, 25 represent mean returns of different trading days of week. It pith that mean returns across all the five days of week are equal. Alternative hypothesis is H1 1? 2? 3? 4? 5It implies that there is significant difference in mean returns across the trading days in a week. Different statistical tools have been used to find the results like mean, standard deviation, range, skewness kurtosis etc. Then the most scientific and logical non-parametric Kruskall-Wallis (H) test has been applied to check the hypothesis. The Kruskall Wallis test requires the entire set of observations being ranked higher the value, higher is the rank and vice-versa- then consistent into nj ? 5 hyaloplasm where nj represents the rank of the return and tugboats represent the day of the week (Monday through Friday).The value of H is calculated by formula H = 12 ( pic (Rj)2 ) 3(N+1) pic pic N(N+1) nj Where Rj= sum of ranks in the jth column nj = number of cases in the jth column N = sum of observations in all the columns The calculated H value has been compared with the table value of the chi-square(? 2) distribution with (k-1) degree of freedom, where k stands for the number of trading days in a week.Hence H0 is rejected if Hgt ? 2 H0 is accepted if Hlt ? 2 The value of H in our study is taken as the critical value at 1% as well as 5% level of significance. Further Dunns ten-fold pair simile test based on rank matrix built in K-W test has been used to find seasonality by a pair wise multiple relation use. It identifies whether particular day of the week differs from other days of the week. The test procedure relies on Kruskall-Wallis rank sum Rj. The data in the rank-day matrix brisk for H test is used for this purpose. For a given level of ? fall ? ? ? if Ru-Rv ? Z ? /k(k-1) N(N+1)/121/2 1/n + 1/nv1/2 Where, = 1, 2k-1 v= +1,. k k = 5 N = total number of observations n = corresponding number of observations in the uth column nv = corresponding number of observations in the vth column Ru = Average K-W rank sums in the uth columns of the rank matrix Rv = Average K-W rank sums in vth columns of the rank matrix Z? /k(k-1) = the veloc ity percentage point of the unit normal distribution for a given significance level for 99 percent confidence level is 2. 575 Further the returns have been analyzed for two sub-periods i. e.Sub period-1 before rolling settlement (weekly settlement period) sub period-2 after the rolling settlement was introduced. In weekly settlement time period, Tuesday used to be as the settlement day on NSE. In 2001, rolling settlement was introduced which shifted settlement cycle from a located day of the week to fixed settlement lag. Tuesday settlement might be the possible reason for the observed seasonality in stock returns. DATA compend Here the day of the week pattern of the SP CNX Nifty data from January 1994 to December 2008 has been tested, results of which have been depicted in Table 1.It is observed from the table that the mean returns on Monday i. e. -0. 08563 percent are minimum followed by Tuesday. Mean returns on Wednesday, Thursday and Friday are positive out of which Wednesdays return with 0. 303 percent is utmost across all the days of the week. The mean return on Wednesday is about 8 times the overall mean return. The variation in mean returns measured in terms of standard deviation is found maximum on Monday (1. 870303 percent) followed by Friday (1. 740897 percent). It shows that trading on week bulge and week end is more volatile than other days of week.Skewness is positive only on Wednesday while other days of week have negatively skewed distributions. Kurtosis tells us the extent to which a distribution is peaked or flat topped when compared with a normal curve. The return distribution on Monday, Tuesday and Friday is leptokurtic while on Wednesday and Thursday are platykurtic. Through table it is also observed that range on Monday is highest which is also a measure of Dispersion. There is a significant difference in mean returns across different the different days of the week as evident by K-W (H) statistics (21. 78) which is highly significant at 1 percent level of significance. Therefore the null hypothesis of equality of mean returns across various days of the week stands rejected. Table 1. Summary Statistics of Daily Stock Returns of SP CNX Nifty(Jan 1994-Dec. 2008) Monday Tuesday Wednesday Thursday Friday All Days Mean -0. 08563 -0. 07615 0. 30300 0. 1895 0. 03221 0. 03838 meter Deviation 1. 87030 1. 50858 1. 62655 1. 55153 1. 74090 1. 66944 Skewness -0. 71612 -0. 15909 0. 40400 -0. 05609 -0. 35999 -0. 24662 Kurtosis 4. 29741 4. 47636 1. 79652 1. 53957 5. 66062 3. 98682 Range 7. 54838 8. 29523 7. 9590 6. 30507 7. 83089 20. 53297 No. of Observations 741 742 740 744 728 3695 K W(H) Statistics 21. 278* * authoritative at 1 percent level for 5-1 degrees of freedom Table 2 represents authentic and expected multiple comparison values as per Dunns multiple pair comparison test to study pair wise comparison among different days of the week. This test is based on rank matrix built in Kruskall Wallis Test.The calc ulation of actual and expected values is shown in table 3 while the deviation of actual from expected ranks is shown in table 3. So it is observed from the table 3 that there is variation in Monday Wednesday, Tuesday Wednesday, Wednesday Thursday and Wednesday Friday pairs as these are showing positive deviation of absolute rank sum values from the corresponding Z value or expected value. It means these pairs are showing more discrimination in returns than expected and Tuesday Wednesday is showing highest positive deviation. Also it is observed from the table that Wednesday appears in all above pairs.It means Wednesday returns are significantly different from the other days of week. Wednesday is showing highly different mean returns from ministration of the days. So a trading outline of buying on Tuesday and merchandising on Wednesday may help an investor to earn abnormal returns. Table 2. actual and Expected Multiple Comparison Values essential Expected RU ?Rv Z N( N+1)/121/2 (1/nu+1/nv)1/2 ZN(N+1)/121/2 (1/nu+1/nv)1/2 Monday-Tuesday 40. 64 2. 575 1066. 799 0. 0519 142. 6521 Monday-Wednesday 197. 07 2. 575 1066. 799 0. 0520 142. 7620 Monday-Thursday 30. 38 2. 575 1066. 799 0. 0519 142. 5697 Monday-Friday 50. 24 2. 75 1066. 799 0. 0522 143. 3388 Tuesday-Wednesday 237. 71 2. 575 1066. 799 0. 0520 142. 7070 Tuesday-Thursday 71. 02 2. 575 1066. 799 0. 0519 142. 5147 Tuesday-Friday 90. 88 2. 575 1066. 799 0. 0522 143. 3114 Wednesday-Thursday 166. 69 2. 575 1066. 99 0. 0519 142. 6246 Wednesday-Friday 146. 83 2. 575 1066. 799 0. 0522 143. 3938 Thursday-Friday 19. 86 2. 575 1066. 799 0. 0521 143. 2015 Table 3. Deviation of Actual from Expected Rank Differences Monday-Tuesday -102. 12 Monday-Wednesday 54. 308 Monday-Thursday -112. 190 Monday-Friday -93. 099 Tuesday-Wednesday 95. 03 Tuesday-Thursday -71. 495 Tuesday-Friday -52. 431 Wednesday-Thursday 24. 065 Wednesday-Friday 3. 436 Thursday-Friday -123. 41 Table 4 represents the yearly distribution of mean returns on SP CNX Nifty for different days of the week from 1994 to 2008. Also to test whether these differences in the mean returns on different days are statistically significant or not, the non parametric H statistics has been used. The table value of the chi-square (? 2) distribution at 1 percent level of significance is 13. 277 and at 5 percent level of significance is 9. 488. If we look at year wise KW statistics, up to year 1999 H statistics is highly significant and after 1999 it is insignificant. Table 4.Yearly dispersal of Mean Returns on SP CNX Nifty by Day-of-the-Week (January 1994 December 2008) Year/Day Monday Tuesday Wednesday Thursday Friday KW Statistics 1994 0. 47012 -0. 16573 -0. 36687 0. 01075 0. 32745 9. 945** 1995 -0. 51580 -0. 33583 0. 25709 -0. 6627 0. 11756 11. 145** 1996 -0. 35599 -0. 35342 0. 53600 0. 18662 0. 07796 10. 114** 1997 -0. 46253 -0. 14396 1. 04706 -0. 16222 -0. 06761 19. 917* 1998 -0. 1 2914 -0. 52606 0. 78280 -0. 15417 -0. 22507 13. 245** 1999 -0. 00553 0. 07532 0. 98097 0. 10327 -0. 00305 14. 48* 2000 -0. 16997 -0. 28629 0. 49777 -0. 10239 -0. 16992 4. 989 2001 -0. 21325 0. 11775 0. 30553 0. 08010 -0. 60214 4. 987 2002 0. 00508 -0. 15830 -0. 05939 0. 07054 0. 22584 4. 226 2003 0. 15214 0. 13598 0. 26208 0. 13987 0. 38014 2. 323 2004 -0. 4126 0. 26824 0. 04482 0. 02138 0. 07889 1. 236 2005 0. 29696 0. 04875 0. 02291 0. 08195 0. 18711 1. 806 2006 -0. 09098 0. 01140 0. 22203 0. 22753 0. 33653 1. 198 2007 0. 24310 0. 32425 0. 02874 0. 30801 0. 02442 2. 139 2008 -0. 36369 -0. 13064 -0. 04547 -0. 5441 -0. 24632 1. 46 All Years -0. 08563 -0. 07615 0. 30300 0. 01895 0. 03221 21. 278* * hearty at 1% level **Significant at 5% level Further entire study period has been divided into two sub periods Period 1 (January 1994 to Decemeber 2001) and period 2 (January 2002 to December 2008). Period 1 represents the time when weekly settlement was operativ e and during this time frame NSE had fixed settlement day Tuesday. Period 2 represents the time period when rolling settlement was introduced in place of weekly settlement cycle. Table 5.Mean Daily returns on SP CNX Nifty by Day of the Week for Sub-Periods Monday Tuesday Wednesday Thursday Friday KW Statistics Subperiod-1 -0. 17276 -0. 20228 0. 50504 -0. 01304 -0. 06810 42. 752* Subperiod-2 0. 00197 0. 05294 0. 09734 0. 03923 0. 12735 2. 84 *Significant at 1% level It is analyzed from the above table that in sub period 1 (1994 to 2001) all days except Wednesday gives negative rate of return. This is clearly the impact of Tuesday settlement that returns on Tuesday are lowest and on Wednesday it is highest positive. It means beginning of settlement cycle ives maximum returns while last day of settlement cycle called settlement day gives lowest returns. Also a very high value of KW statistics i. e. 42. 752 represents a high degree of seasonality in sub period 1 (before rolling settlement time period). To bring more frequency in the minutes and to bring Indian markets at par with the international markets rolling settlement on T+5 basis was introduced in December 2001. So in sub period 2 when rolling settlement was introduced, returns on all the days have become positive and Friday is giving maximum returns and Monday is giving lowest returns.This hints towards the presence of some sort of weekend seasonality. But the value of H statistics is very low i. e. 2. 684. From this it can be inferred that the return distributions are not significantly different across the week days and the null hypothesis stands rejected in the sub period 2. Thus it may be concluded that with the introduction of rolling settlement on NSE the stock markets have become more efficient. CONCLUSION During the period 1994 to 2008, SP CNX Nifty index recorded highest positive returns on Wednesday and most negative returns on Monday with highest volatility on Monday a nd Friday.It means week start and week end tend to be more volatile in Indian stock market. Also it has been analyzed that Wednesday is giving significantly higher returns than other days of the week which points towards the existence of Wednesday effect in Indian stock market. There was presence of day of the week effect in pre-rolling settlement period which gradually phased away with the introduction of the rolling settlement. Markets have become efficient after rolling settlement has been introduced.So in present scenario we cant rely on a trading strategy formulated on the basis of historical return movements on different days to earn abnormal returns as seasonality has disappeared in the recent years of the study period.