A Study on Movement of Share Price on Day Basis and Market Efficiency of Indian Stock Exchange

A Study on Movement of Share Price on Day Basis and Market Efficiency of Indian Stock Exchange
Author: Nidheesh K B | Posted: 21-09-2007 | Comments: 0 | Views: 4 | Ads by Google

Free Investment Advice.
Your Expert Guide, Educator, Personal Trainer And Facilitator.
ShareKhan.com/InvestmentGuidance

Value Investors Club
Top investors exchange well researched investment ideas!
www.valueinvestorsclub.com

Double Your Money
Learn All About Investments At India's Leading News Website
www.hindustantimes.com

SBI Cash Back Plus Offer
Use Your State Bank Debit Card & Get Upto 5% Cash Back & More
www.SBI.co.in/Cash_Back_Offer

HDFC Bank 3 in 1 Account
Get The Benefits of Demat, Online Trading & Savings in One A/c. Apply
HDFCBank.co.in/Savings3-in-1

INTRODUCTION

The basic argument on which all most all theories of modern finance have been built is that of the assumption of efficiency of capital market. In an informationally efficient capital market, there is no scope for earning abnormal return by investors as the material information get quickly processed, assimilated, and reflected in the market price. The investor cannot take the advantage of the knowledge of a new piece of information; there by making abnormal return is the crux of Efficient Market Hypothesis (EMH). Further, EMH also imply that if the market is efficient, trading strategies do not work to the advantage of investor as the returns are equal for all days of the week. However in reality financial researchers have discovered many market imperfections, called market anomalies and there by suggested different trading strategies based on such anomalies. Most of the modern finance theories are formulated based on the assumption of efficient capital market. An efficient market is understood to be a market, where the price of the security reflects the markets best estimate of their expected return on risk, taking into account all the information material to their pricing. Hence the securities are supposed to be fairly priced at any point of time. Thus there does not exist any overvalued or undervalued securities offering higher or lower return than the expected return. However, if the market is not efficient a well planned investment strategy can help the investor get abnormal return, by identifying undervalued and over valued securities and formulating the strategy accordingly. Thus market efficiency has an influence on the investment strategy of an investor because if security markets are efficient trying to pick winners will be a waste of time, if otherwise excess return can be made by currently pricing winners. The term market efficiency is used to explain the relationship between information and share prices movement in the stock market. An efficient capital market is a market that is efficient in processing information. In this market information is evaluated as it arrives and prices instantaneously adjust to a new level and the security prices equal their intrinsic value at all times. Therefore, an investor cannot consistently earn excess returns by undertaking fundamental analysis or technical analysis.The security prices in an efficient capital market fully reflect their investment value. The market has the capability to instantaneously impound the given set of information into the pricing process. It is impossible to consistently make abnormal returns using a trading strategy based on a given set of information when the markets are efficient. This postulate is based on the premises that

(1) all investors have cost-less access to currently available

information about the future;

(2) they are good analysts; and

(3) they pay close attention to the market process and adjust their

holding appropriately.

Till the late seventies, empirical studies supported the view that capital markets are informationally efficient. Many models related to security valuation have been based on this concept of ‘informational efficiency of capital market. However, the late seventies and eighties brought in evidences questioning the validity and highlighting various anomalies related to the capital market efficiency. There are many focused studies that demonstrated the possible trading strategies yielding abnormal rates of returns using the historical data and publicly available information ruling out the efficiency of market. The empirical studies evidencing the inefficiency are broadly related to;

 the low P/E effect,

 low priced stocks,

 Insignificant firm effects,

 market overreaction,

 the January effect,

 holiday effect,

 persistence of technical analysts, and

 the day of the week effect.

As stated earlier, in an efficient market stock returns are identical for all days of the week. However, the financial researches have observed that the stock returns are not identical across the time periods. More specifically, the researchers have found that the Monday return is significantly negative and Friday experiences a high positive return. This observation is generally referred to as ‘day of the week effect’ or ‘the weekend effect.’ The most satisfactory explanation that has been given for the negative returns on Mondays is that usually the most unfavorable news appears during the weekends. These unfavorable news influence the majority of the investors negatively, causing them to sell on the following Monday. Other possible reasons behind this anomaly identified by financial researchers includes; settlement effects, measurement error, specialist related biases and trading pattern of individual and institutional investors Thus if an investor has the ability to compound the price swings in earning extra normal returns, he counters the principle of market efficiency. In addition, any systematic pattern of price changes across days of the week may also suggest some trading strategy to earn abnormal returns. This study attempts to focus on stock return variability across days of the week and try to find out the existence of the day of the week effect in stock return in the Indian stock market. based on regularity or otherwise of returns across the days in a week.

Theoretical Reviews

Stock market efficiency is an important concept, for understanding the workingof the capital markets particularly in emerging stock market such as India. The efficiency of the emerging markets assumes greater importance as the trend of investments is accelerating in these markets as a result of regulatory reforms and removal of other barriers for the international equity investments. There is enough evidence on market efficiency and day of the week effect in the developed markets, however, the same is not true for the emerging stock markets.. stcok market efficency is an important concept, in terms of anunderstanding of the working of the capital markets. The efficiency of the emergingmarkets assumes greater importance as the trend of investments is accelerating in these markets as a result of regulatory reforms and removal of other barriers for the international equity investments. The term market efficiency is used to explain the relationship between information and share prices in the capital market literature. Fama (1970 and 1991) provides the formal definition of “Market Efficiency”. He classifies market efficiency into three categories namely, weakform, semi strongform and strongform. In its weak form, market efficiency hypothesis (EMH) states that the stock returns are serially un-correlated and have a constant mean. In other words, a market is considered weak form efficient if current prices fully reflect all information contained in historical prices, which implies that no investor can devise a trading rule based solely on past price patterns to earn abnormal returns. A market is semi strong efficient if stock prices instantaneously reflect any new publicly available information and Strong form efficient ifprices reflect all types of information whether available publicly or privately.

Market Efficiency has an influence on the investment strategy of an investor because if securities markets are efficient trying to pick winners will be a waste of time. Since in an efficient market, the prices of securities will reflect the market’s best estimate of their expected return and risk, taking into account all that is known about them. Therefore, there will be no undervalued securities offering higher than deserved expected returns, given their risk. So, in an efficient market, an investment strategy concentrating simply on the overall risk and return characteristics of the portfolio will be more sensible. If however, markets are not efficient, and excess returns can be made by correctly picking winners, then it will pay investors to spend time finding these undervalued securities (Rutterford, 1983 pp. 282). The day of the week effect refers to the existence of a pattern on the part of stock returns, whereby these returns are linked to the particular day of the

week. Such relationship has been verified mainly in the USA. The last trading days of the week, particularly Friday, are characterised by the positive and substantially positive returns, while Monday, the first trading day of the week, differs from other days, even producing negative returns (Cross, 1973; Lakonishok and Levi, 1982; Rogalski 1984; Keim and Stambaugh, 1984; Harris, 1986). Once again the day of the week effect in emerging stockmarket have not been extensively researched. The presence of such an effect would mean that equity returns are not independent of the day of the week, an evidence against random walk theory.Studies on testing of market efficiency of Asian emerging stock marketsare also surprisingly few. Chan, Gup, and Pan (1992), show that there is no evidence that the stock prices in major Asian Markets and U.S. markets are weak form efficient individually and collectively in the long run. Dickinson and Muragu (1994) provide evidence of market efficiency in Nairobi Stock Exchange. They conclude that small market such as Nairobi Stock Exchange provides empirical results consistent with weak-form efficiency. Cheung, Wong and Ho (1993) report inefficiency of stock markets of Korea and Taiwan on the basis of weak theoretical form of Capital Asset Pricing Model in both the markets. Groenewold and Kang (1993) have conducted weak and semi-strong efficiency tests of Australian stock market by using aggregate share price indexes and find the data consistent with the weak form efficiency. Ho, Richard and Cheung (1994) study the seasonal pattern in volatility of Asian Stock Markets. Using Levene (1960) test, they report that there exist day-of-the-week variations in volatility in most of the emerging Asian markets. Barnes (1986) tests the weak form market efficiency of the Kuala Lumpur Stock Exchange and concludes that the stock exchange exhibited a surprisingly high degree efficiency, inspite of thinness of the market.

Efficient Markets Theory

The efficient market hypothesis is inextricably related to the random walk theory. The idea that security prices might follow random was put forward by Bachelier1 in 1900. The random walk is used to refer to successive price changes which are independent of each other. In other words, tomorrow’s price change (and therefore, tomorrow’s price) cannot be predicted by looking at today’s price change, Pt+1 - Pt is independent of pt- pt-1. There should be no trends in price changes. Proofs of the random walk theory can take several forms. As with all tests of theories involving future expected prices or returns, past actual prices or returns are used for the tests. So for the random walk theory, sets of share price changes are tested for serial independence. Random walk theory for share prices reflects a securities market where new information is rapidly incorporated into prices and where abnormal returns or’excess’ returns cannot be made from spotting trends or from trading on new information. That share prices appear to follow a random walk is an interesting result and proving it or attempting to disprove it occupied significant proportion of research in 1970’s. But what remained to be shown was why share prices followed a random walk. There was plenty of evidence, but a formal theory was missing. What was needed was a model of share price behaviour to explain the random walk. The gap was filled by more general model based on the concept of efficiency of the markets in which shares are traded – the efficient market hypothesis (EMH). According to EMH, the ability of investor to pick winners and make excess returns using new information is directly related to the speed and efficiency of a market at absorbing that information. So, efficiency is considered in terms of the ‘fair game’ concept. A market is regarded as efficient with respect to a particular set of information if investors using that information are faced with fair game, that is, they receive on average the return expected for the risk involved and make no consistent abnormal returns. This can be expressed in the following way. If t is defined to be a particular set of information concerning security j available at time t, then any abnormal return achieved at time t+1 on security j can be written j,t+l = where j, t+1= (E(Rj,t+1)/ t). The equation shows that the excess returns will be the difference between the return actually achieved and the return expected given the risk. The solidus/simply means that the returns are achieved or expected knowing information t at time t. The EMH does not say that investors will never beat the market and will never make large profits. In other words, j,t+l can be large and positive and sometimes negative, with the result that the sum of the excess returns over a number of periods of time will average zero j,t+1 = 0. 1. Bachelier, L., 1900, Th’eorie de la sp’eculation, Gauthiers-Villars 608 Finance India The fair game for investors is an outcome of a market being efficient. If a market is efficient, then investing is a fair game. This fair game concepts is useful in that it allows the different levels of the EMH to be tested.

Review Of Literature

The studies on the behavior of stock returns to find out the day of the week effect by using different methodologies have grown substantially over the years. French (1980), and Gibbons and Hess (1981) document a weekend effect with a low or negative return on Mondays in the US stock market. Smirlok and Starks (1983), Rogalski (1984) found that most of the negative returns on Friday closing price to the Monday closing price take place when the market is closed over weekends rather than during the trading day on Monday. Maurice (1988), Dyl and Holland (1990) opine that individual investor trades odd-lots more than the institutional investors and this specific event may cause day of the week effect is NYSE. Ziemba (1993) in his study reported that Japanese market experiences significant Tuesday returns. Fishe and Laiser (1993) confirmed negative Monday return due to negative weekend effect. Abraham and Ikenberry (1994) observed that the Monday return following negative Friday return was significantly negative nearly 80 percent of time. Similarly, Athanassakos and Robinson (1994) also observed that 72 percent of Monday return following negative Friday supported negative return. Sias and Starks (1995) in their study argued that the trading behavior of institutional investors is the main reason behind the Monday effect. According to Chow, Hsiao and Salt (1997), it is possible to exploit the day of the week effect to generate positive returns by following the sorting strategy prescribed by them. Kamara (1997) found a significant decline in Monday seasonal due to increase in ratio of institutional trading volume and evidence that small-cap returns exhibited a significant Monday seasonal throughout the study period. Wang and Erickson (1997) in their study also reported similar results. Kiymaz,Halil and Berument,Haken (2002) in their study investigate day of the week effect on volatility of major stock market in conditional variance framework. Their study report the presence of this anomaly in Canada, Germany, Japan, US and UK. Berument, H, Inamlik, and Kiymaz, H. (2004) investigate the day of the week effect on return and volatility for Istanbul Stock Exchange and reported a strong presents of day of the week effect in that market. The phenomenon of day of the week effect was not only present in the equity market, but it is also detected in the Treasury – bill market (Flannery and Protopapadakis, 1988), in the commodity and stock futures markets (Carnal, 1985; Dyl and Maberly, 1986), and in the foreign exchange market (Corha and Rad, 1994).

In the Indian stock market, there are a few studies on the day of the week effect. Chaudhuri (1991) supported the presence of weekend effect/day of the week effect in the daily return of BSE SENSEX for the period June 1988 to January 1990 through Kruskal-Wallis test. Broca (1992) presented unequivocal evidence as to the day of the week effect but concluded that the trading strategy based on this evidence is ineffective when compared to a naïve ‘buy and hold’ strategy. In another study Poshakwale (1996) tested the weekend effect by using BSE national index during the period January 1987 to October 1994 by applying first order auto correlation and supported the presence of weekend effect. Arumugan (1999) in his study observed positive Friday return and significant negative Monday return in bear phase. Anshuman and Goswami (1999) in their study reported that Fridays shows above average positive return and Tuesdays shows below average negative returns. Their study rejected the settlement error, size-effect, and settlement effect and badla mechanism as possible factors behind the phenomenon of weekend effect. Amanualla and Thiripalraju (2001) tested whether the carry-forward transactions in different periods have any impact on weekend effect. The results form the sample period strongly supported the weekend effect during the period of ban on badla transactions. The study also shows a reversal in weekend effect, i.e., positive Monday return and negative Friday return in modified carry forward transaction and revised modified carry forward transactions. Further, the study reported that there is a consistent positive return on Wednesday and consistent negative return on Tuesday due to the possible impact of NSE on weekend effect. Sarma (2004) also confirmed the existence of day of the week effect in the Indian stock market. Nath and Dalvi, (2004) reported that before introduction of rolling settlement in January 2002, Monday and Friday were significant days. But after the introduction of rolling settlement, Friday become significant. The study asserts that Indian capital market is not efficient and the market is yet to price the rise appropriately. Bhattacharya et al (2005) examines the stability of the day of the week effect in returns and volatility at the Indian security market, They tries to explain this anomaly with respect to reporting and non-reporting weeks of the banks. Gupta (2006) in his study examined day of the week effect in Indian stock market after the introduction of compulsory rolling settlement system and confirmed the presents of this anomaly during the study period.

Though various studies have largely accounted for all possible factors responsible for the day of the week effect, they differ widely in their findings. Hence it is difficult to give any specific factor(s) responsible for this anomaly. Thus, this day of the week effect, in sharp contrast to the theories of efficient market, was considered as a puzzle and despite different theories and explanations, so far the puzzle has not been satisfactorily resolved. As more and more empirical evidence are obtained form different stock market all over the world, the puzzle seems to have increased.

Further, most of these studies have been based on data of mid-1980s and mid-1990s and have taken the closing values of the respective indices in return compilation process with the implied assumption that trading is done at the closing price. However, there would not be any need to make such an assumption in case an average of high, low, opening and closing values are taken. (Sarma). Thus, it would be pertinent to retest the conclusions drawn by earlier studies in view of the changes in the wider economic scenario in India, widened choice of benchmark portfolios, and methods of measurement techniques. On this background, the present study examines the presence of day of the week effect in stock returns in India.

Data and Period of the Study

The PROWESS database provides information regarding the daily opening, high, low and close values of the SENSEX, and Nifty indices. The study used this data related to the period spanning from January 1st 1997 to June 30th 2004 comprising a total of 2139 observations for each of the indices. Ideally, individual stock prices should be used for such an analysis, since the index data suffers from inherent limitations in the face of non-synchronous trading and omission of dividends. These may lead to statistical problems such as understatement of returns, autocorrelation and distortions in estimated variances. However, French et. al., (1987) have shown that the results are broadly similar when the daily values of the S&P 500 index as well as 30 actively traded shares on the NYSE are taken.

Methodology

The earlier studies had used the closing values for return generating procedure with an implied assumption of trading done at the closing values there would not be any need for such a restrictive trading assumption in case an average of the available opening, high, low and closing values is used. The continuously compounded annual rate of return is a well accepted approach to measuring the daily returns. The natural log of daily relative mean index value is, thus, the measure of daily return used in this study. The formula is stated below:



Where,

Rt = return on day ‘t’

It = index mean value on day ‘t’

It-1 = index mean value on day ‘t-1’

In = natural log

The returns so generated are classified day-wise. from Monday to Friday and their equalities and volatilities are measured For testing whether mean returns are constant across all five days of the week or whether they exhibit statistically significant differences, a non-parametric test method has been employed. This is because of their robustness arising from lack of restrictive assumptions such as population normality and homoscedastic variances. Thus, the usual one-way analysis of variance is replaced by its non-parametric alternative, the Kruskal-Wallis test K-W test is non-parametric tests for testing the null hypothesis that K independent random sample comes from identical populations against the alternative hypothesis that the means of these samples are not all equal. The K-W test requires the entire set of observations to be ranked-higher the value, higher the rank and vice versa then arranged into ni*5 matrix where ni represents the rank of the return and columns represents the day of the week Monday through Friday. The formula for calculating the test statistic ‘H’ is as follows:



Where:

Rj = sum of the ranks in the jth column

nj = number of case sin the jth column

N = sum of observations in all the column

Since the sampling distribution of ‘H’ is asymptotically leptokurtic based on four degrees of freedom, the critical value is 13.28 at one percent level of significance for the given four degrees of freedom. If the computed value of ‘H’ is greater than the critical value, the null hypothesis cannot be accepted. Conversely, if the computed value of ‘H’ is less than the critical value, the alternative hypothesis cannot be accepted.

Hypothesis

For each week day mean daily returns and return volatility have been calculated over the entire period of study and then compared. Accordingly, the hypotheses to be tested are:

H0: There are no differences in the average return on stock indices across the days of the week.

H1: There are differences in the average return on stock indices across the days of the week.

Analysis and Discussion

Testing for Statistical significance: Equality of Returns

Table 1, 2 and 3 presents the descriptive statistics of the day of the week returns for the three selected indices along with that of the comprehensive sample-‘all days’- in addition to the computed ‘H’ statistics. It is clear from the Table 1, 2 and 3 that the daily mean returns are zero or almost zero for al the portfolios-SENSEX, and Nifty, during the study period. The distribution of daily returns tends to be leptokurtic with long tails and many mean centric observations. All the indices give negative returns on Fridays and all the values of the days descriptive statistics are very closely coinciding sending strong evidence as to the ‘weekend effect. The standard deviation of the portfolio increased with the degree of diversification. This is neither surprising nor contrary to the expectations. The portfolio construction is neither random nor based on Markowitz selectivity criterion. Standard deviation of all the indices returns is highest for Mondays. Further, it also reveals that Mondays’ standard deviation of SENSEX and Nifty are more than their respective average of ‘all days’ standard deviation during the study period. For all the indices, Wednesday register the highest positive return. For SENSEX Thursday and Friday register negative returns while for Nifty Friday shows negative returns. However, for SENSEX and Nifty Friday shows the lowest return. All the indices appear to be most attractive on Mondays from the view point of mean returns and standard deviations. Considering the Kurtosis and the range figures SENSEX relatively shows some semblance of normality. Wide variations are observed across the week-days within and among the indices To test whether the differences in the mean returns across the weekdays are statistically significant, ‘H’ statistic is computed. The critical value of ‘H’ is abnormally higher than the critical value of all the indices. Thus, the null hypothesis is rejected. This provides evidence as to the presence of regularity in common stock returns in India during the study period.

Pattern of Deviation

By employing multiple comparison procedure it is possible to find out which pair shows significant deviation form one another and uncover the general pattern of high low tendencies in the data. For a given overall level of significance level of μ decide # ×v, if



Where:

μ = 1, 2.. .K-1

ν = μ+1.. .K

K = 5

N = total number of daily returns

n = number of daily means in the μth and vth column

R = average rank sum of the μth and ith column

= the upper percentage point of the unit normal distribution for a given significance level

whose value for 99 percent confidence level is 2.575.

The required calculations are presented in the table 4 and 5

The Table 5 shows that Monday-Tuesday, Monday-Friday and Wednesday-Friday sets have positive deviations for all the indices. However, Monday-Friday sets for all the indices have the highest positive deviation Tuesday-Wednesday-sets also have positive deviations, although very low, for all the indices. Thus, in general, Indian stock market exhibit regularities in the equity return and have scope for questioning its market efficiency.

Implication for Market Efficiency

Through pair-wise multiple comparison procedure, it is observed that Monday-Tuesday, Monday-Friday, and Wednesday-Friday have positive deviations for all the indices. These observations must consequently lead us to designing a trading strategy exploiting the possibility of making abnormal returns. A comparison of annual rates of return generated by a passive strategy of ‘by and hold’ and various active strategies of ‘buying Monday and selling Thursday’ or ‘buying Monday and selling Friday’ or ‘buying Wednesday and selling Friday’ is presented in Table 6. For all the indices mean returns of the active strategies turned out to be lower than the ‘buy and hold’ strategy for the field. With transaction cost this would further reduce, even whip out, any profit the investor could have earned through the active strategy mentioned above. The active strategy is of little use may be because the study period is characteristic of a highly unsettled economic environment and the fact that the indices underwent frequent shuffling and reshuffling unconnected to the principles of diversification. Broca (1992) also found the return resulting from pursuing a trading strategy based on the observed regularity of return being less than a naive ‘by and hold strategy’ in spite of his strong evidence as to the Wednesday having the lowest returns and Fridays the highest. However this does not means that knowledge of persistent stock market behavior pattern on week days have no value whatsoever. An individual can increase the expected return to his investment by altering the timing of routinely scheduled transactions.

Conclusion

The assumption of efficient Capital Market is the base of most of the modern finance theories. However, whether markets are efficient or not is a matter to be investigated before apply the postulate to finance theories. Many researches have challenged the application of dictums of finance theories on the ground of non-existence efficient capital market. The researchers have discovered many anomalies existing in various capital market theories doubting the existence of efficient market. One of the widely reported market anomaly is Day of the Week Effect. This anomaly suggests that adopting trading strategy will enable the investor to make abnormal return if market is not efficient. In this study an attempt is made to investigate the presence of the day of the week effect or other wise In Indian stock Market. The study is conducted on the log return data of Sensex and Nifty. Since 1, Jan, 1997 to 30, June , 2004. The data source is Prowess data base. A total of 2139 observations of Sensex and Nifty have been taken as the base of the present investigation. The data has been analyzed with the help of a non-parametric tool i.e. Kruskal Wallis Test. The analysis provides evidence as to the presence of day of the week effect in stock returns. It confirms the evidence of earlier studies as to the leptokurtic distribution of equity returns, presence of highest variation on Monday, weekend effect, and regularities of returns across the indices. An examination of daily returns of these indices during this period shows evidence of significant variation according to the day of the week. This contradicts to the random walk hypothesis as a descriptive model for common stock price movements in India. Wednesdays shows consistently highest returns and Fridays shows consistently negative returns for all the indices. Through a pair wise comparison procedure, it is found that Monday-Tuesday, Monday-Friday and Wednesday-Friday have highest positive deviations for all the indices. But a simple trading strategy designed to exploit this empirical regularities could not out perform a naïve ‘by and hold’ policy over the study period. A benefit, however, accruing to investors from knowledge of the variations is that by altering the timing of routinely scheduled transactions they could increase the expected returns on investments.





Table 1

BSE SENSEX Day-wise

Summary Statistics on Daily Returns

Monday Tuesday Wednesday Thursday Friday All Days

Mean 0.0002 -0.0005 0.0011 0.0003 -0.0007 0.00008

Median 0.0009 0.0000 0.0008 0.0005 -0.0002 0.00048

Standard Deviation 0.0909 0.00917 0.00778 0.00566 0.00609 0.02392

Skewness -3.310 -2.192 5.897 -0.325 -0.227 -0.0314

Kurtosis 26.166 74.584 78.330 2.988 1.997 36.813

Range 0.12 0.20 0.13 0.05 0.005 0.11

Number of Observations 429 428 429 428 425 2139

‘H’ value 27.8407

Table 2

S&P CNX Nifty Day-Wise

Summary Statistics on Daily Returns

Monday Tuesday Wednesday Thursday Friday All Days

Mean 0.0011 0.0007 0.0032 0.0012 -0.0001 0.0012

Median -0.0001 -0.0001 0.0012 0.0008 0.0004 0.0004

Standard Deviation 0.03093 0.00534 0.03596 0.03636 0.00557 0.02283

Skewness 19.238 -0.611 20.089 -20.089 -0.279 3.656

Kurtosis 388.796 2.348 409.632 411.513 1.188 242.695

Range 0.68 0.05 0.76 0.76 0.04 0.428

Number of Observations 429 428 429 428 425 21.39

‘H’ value 17.3631

Table. 3

Actual and Expected Multiple

Comparison Values





SENSEX NIFTY Z



Monday-Tuesday 113.664 98.263 2.575 507.662 0.08892 96.817

Monday-Wednesday 7.267 4.336 2.575 507.662 0.08892 96.389

Monday-Thursday 19.773 32.221 2.575 507.662 0.08892 96.719

Monday-Friday 120.831 137.669 2.575 507.662 0.08815 97.293

Tuesday-Wednesday 109.296 108.26 2.575 507.662 0.08832 97.289

Tuesday-Thursday 98.263 86.261 2.575 507.662 0.08871 96.271

Tuesday- Friday 8.412 20.226 2.575 507.662 0.08819 97.289

Wednesday-Thursday 19.263 16.226 2.575 507.662 0.08839 96.279

Wednesday-Friday 123.229 101.226 2.575 507.662 0.08879 97.881

Thursday-Friday 102.669 61.226 2.575 507.662 0.00891 96.718



Tab le 4

Deviation of Actual From Expected

Average Risk Difference



SENSEX

NIFTY

Monday-Friday 16.847 1.446

Monday-Wednesday -89.122 -92.052

Monday-Thursday -77.006 -64.498

Monday-Friday 23.538 40.376

Tuesday-Wednesday 12.007 10.971

Tuesday-Thursday 1.992 -10.01

Tuesday-Friday -88.877 -77.063

Wednesday-Thursday -77.016 -80.053

Wednesday-Friday 16.382 25..348

Thursday-Friday 5.951 -35.492

Table 5

Trading Strategy

Annual Returns Generated



SENSEX

Nifty

Monday--Thursday

2.93

31.77

Monday-Friday

20.63

29.92

Wednesday-Friday

7.91

35.27

Buy and hold

17.26

27.82

References

1 Business and Finance, Rising Asian Capital Markets: Empirical Studies, Vol. 10, Jai Press Inc., London, 213-219.

2 Cross, F., 1973, “The Behaviour of Stock Prices on Fridays and Mondays, Financial Analysts Journal, 29, November/December, 67-69.

3 A Case Study of the Nairobi Dickinson , J., and Muragu, K., 1994, “Market Efficiency in Developing Countries: Stock Exchange”, Journal of Business Finance & Accounting, January, 21(l), 133-150.

4 Fama, E., 1970, “Efficient Capital Markets: A Review of Theory and Empirical Work”, The Journal of Finance, 25, 383-417.

5 Fama, E.F., 1991, “Efficient Capital Markets II”, The Journal of Finance, XLVI, 5, December 1991.

6 Groenewold, N., and Kang,K.C., 1993, “The Semi-strong Efficiency of the Australian Share Market”, Economic Record, Vol. 69, Iss. 207, December, 405- 410.

7 Harris, L., 1986, “A Transaction Data Study of Weekly and Intradaily Patterns in Stock Returns”, Journal of Financial Economics, 16, 99-117.

8 Ho, Y.K., Richard, and Cheung, Y.L., 1994, “Seasonal Pattern in Volatility in Asian Stock Markets”, Applied Financial Economics, Vol. 4, Iss. 1, February, 61-67.

9 Jacobs, B.J., and Levy, K.N., 1988, “Calendar Anomalies: Abnormal Returns at Calendar Turning Points, Financial Analysts Journal, November/December, 28- 39.

10 Keim, D., and Stambaugh, R., 1984, “A Further Investigation of the week-end Effect in Stock Returns”, Journal of Finance, 30, 819-835.

11 Lakonishok, H. J., and Levy, M., 1982, “Week-end Effects on Stock Returns: A Note”, Journal of Finance, 37, 883-889.

12 Rogalski, R., 1984, “New Findings Regarding Day of the Week Returns over Trading and Non-trading Periods”, Journal of Finance, 39, 1603 -14.

13 Rutterford, J., 1993, Introduction to Stock Exchange Investment, Second Edition, 281-308, The Macmillan Press Ltd., London.

14 Solnik, B. and Bousquet, L., 1990, “Day of the Week Effect on the Paris Bourse”, Journal of Banking and Finance, 14, 461-68.

15 Abraham, Abraham and Ikenberry, L. David (1994), “The Individual Investor and the Week end Effect”, Journal of Financial and Quantitative Analysis, Vol.29, No.2, pp.263-277.

16 Amanulla S. and Thiripalraju, M (2001), “Week-end Effect: New Evidence from the Indian Stock Market ”, Vikalpa, Vol.26, No.2, pp.33-50.

17 Anshuman,R and Goswami,R (1999 ), “Day of the Week Effect on Bombay Stock Exchabge,’’ A Paper Presented at the Third Capital Market Conference, Indian Institute of Capital Market, Mumbai.

18 Arumugan (1998 ), “Day of the Week Effect in Stock Returns An Empirical Evidence from the Indian Stock Market,’’ Prajan, Vol.27, No.2, pp.171-191.

19 Barument, Hakan and Kiymaz, Halil (2001), ‘The Day of the Week and Stock Market Volatality”, Journal of Economics and Finance, Vol.25, No.2, pp.181-192.

20 Berument, Hakan, Kiymaz, Halil (2004) “The Day of the Week Effect and Stock Market Volatality: Evidence From Developed Market”, Journal of Economics nm and Finance Vol.7 No.2, pp. 60-67.

21 Bhattacharya, K, Sarkar, N and Mukhopadhyay (2003), “Stability of the Day of the Week Effect in Return and in Volatility at the Indian Capital Market: A GARCH Approach with Proper Mean Specification,” Applied Financial Economics, Vol. 13, No. 5, pp. 553-563.

22 Broca, Dilbagh (1992),“Day of the Week Patterns in the Indian Stock Market”, Decision, Vol.19, No.2, pp. 57-64.

23 Canbell,J.Y and Y,Hanao (1992),“Predictable Stock Returns in the United States and Japan : A Study of Long-term Capital Market Integration,’’ Journal of Finance, Vol. 43, pp.43-69

24 Carnal,B (1985), “The weekly Patterns in Stock Returns Cash Verses Futers: A Note,’’ Journal of Finance, Vol.40,pp.583-588.

25 Chan,K.C, A. Karolyi and R.M. Stulz (1991), “Global Financial Market and the Risk Performance on U.S Equity”, Journal of Financial Economics,Vol.32,pp.137-

26 Chaudhury, S K (1991), “Seasonality in Share Returns: Preliminary Evidence on Day of the Week Effect,’’ Chartered Accountant, Vol. 40, No.8, pp.107-9.

27 Choudhury,T (2000),“Day of the Week Effect in Emerging Asian Stock Markets:Evidence from GARCH model,” Applied Financial Economics, Vol. 20, No.3, pp. 235-42.

28 Corha,F and A.T.Rad (1994), “Statistical Propirties of Daily Returns. Evidence from European Stock Market,’’ Journal of Business Finance and Accounting, Vol.20,pp.271-282.

29 Damodaran, Aswath (1989), “The Weekend Effect in Information Releases: A Study of Earnings and Dividend Announcements,” The Review of Financial Studies, Vol. 2, No.4, pp.607-623.

29 Dyl, E. and Maberly, E (1986) "The Weekly Pattern in Stock Index Futures :A Further Note", Journal of Finance, Vol.41, No.5,pp. 1149- 1152.

30 Dyl,E and Holland,C.W (1987),“Why a Weekend Effect: A Comment”, Journal of Portfolio Management, Vol.16, No.2, pp.199-228.

31 Fama, Eugene.F (1965), “The Behavioural of Stock Market Price,” Journal of Finance, ol.15, N.5, pp.34-105.

32 Fama, Eugene. F (1970), “Efficient Capital Market: A Review of Theory and Empirical Work,” Journal of Finance, Vol, 25, No. 5, pp. 383-412

33 Flannery, M and Protopapadakis, A (1988),"From T-bills to Comm Market", Journal of Futures Markets, Vol. 7, pp.169-181.

34 French, K.R (1980),“Stock Returns and the Weekend Effect,” Journal of Financial Economics, Vol.8, No.1, pp.55-69.

35 French,K.R,G,W.Schwent and R,F.Stambaugh (1987), “Expected Stock Returns and Volatility,’’ Journal of Financial Economics,Vol.19,pp.3-27.

36 Geo,Lie and Kling,Gerhard (2005), “Colendar Effects in Chinese Stock Market”, Journal of Economics and Finance, Vol.5, pp.75-88.

37 Gibbons, Michael. R and Hess, Patrick (1981), “ Day of the Week Effect and Asset Returns”, Journal of Business, Vol.54, No.4, pp.579-596.

38 Gupta (2003), “ Day-of the Week Effect on the Indian Stock Market : New Evidence” ,The ICFAI Journal of Applied Finance ,Vol.12, No .8. pp .5-14.

39 Hsieh,D.A (1988),“The Statistical Properties of Daily Foreign Exchange Rate 1974-1983,’’ Journal of International Economics, Vol,24,pp.129-145.

40 Levi, Maurice,(1988 ), “Weekend Effects in Stock Market Returns: An Overview”, in Elroy Dimson (ed.) Stock Market Anomalies Cambridge University Press,

Cambridge).

41 Nath, C. Golka, and Dalvi, Manoj (2004), “Day of the Week Effect and Market Efficiency: Evidence form Indian Equity Market Using High Frequency Data of National Stock Excahange,” ICFAI Journal of Applied Finance, Vol.7, No.5, pp, 445-456.

42 Panman,S.H (1987),“The Distribution of Earnings News Over Time and Seasonalities in Aggeregate Stock Returns,’’ Journal of Financial Economics, Vol.18, pp.199-228.

43 Poshakwale, Sunil (1996), “Evidence on Weak Form Efficiency and Day of the Week Effect in the Indian Stock Market”, Finance India, Vol. X, No3, pp.605-616.

44 Rogalski J, Richard (1984), “New Findings Regarding Day of the week Returns on Trading and Non-Trading Periods: A Note,” Journal of Finance, Vol-34, No-6, pp.1603-1615.

45 Sarma,S.N (2004),“Stock Market Seasonality in an Emerging Market,” Vikalpa, Vol. 29, No.3, pp.35-42.

46 Smirlock, Michael and Laura Starks,(1986 ),”Day of the Week and Intraday Effects in Stock Returns “, Journal of Financial Economics 17, 197–210.

47 Wang, K., Li, Y., and Erickson, J., (1997), “A New Look at the Monday Effect”, Journal of Finance 52, 2171–2187.

Rate this Article:
Article Tags: Trading Strategies, Anomalies, Emh, Day Of The Week Effects, Jel Classification: G15, G17

Article Source: http://www.articlesbase.com/investing-articles/a-study-on-movement-of-share-price-on-day-basis-and-market-efficiency-of-indian-stock-exchange-218324.html

Print article Send to friend Publish this Article Author feedback
About the Author:

NIDHEESH K B
LECTURER
DEPARTMENT OF COMMERCE
PONDICHERRY UNIVERSITY
PONDICHERRY