monday effect: day of the week effect of bse broad market...

MONDAY EFFECT: DAY OF THE WEEK EFFECT OF BSE BROAD

MARKET INDICIES FROM 2006 TO 2016

Dr. J. Vinoth Kumar

Assistant Professor of Commerce

St. Joseph’s College [Autonomous], Tiruchirappalli

Email: [email protected]

ABSTRACT:

Stock market functions as bridge and enables individuals as well as institutions to add in

country’s wealth through their participation in the secondary market of the country. A well-

regulated stock market promotes the phenomenon of fair pricing of securities and reduces

transactional costs. Calendar anomaly or Seasonality is the most puzzling topic in finance on

which numerous studies have been conducted during the last three decades. Some of these stock

anomalies are Day-of-the-Week Effect, Weekly Effect, Month-of-the-Year Effect, Weekend

Effect, Ramadan Effect and January Effect etc. The overall objectives of the study Is to find out

the day of the week effect. The study covers all the information related to the Day of the Week

Effect in BSE indices. It is confined to 10 years (1st April 2006 to 31

st March 2016) data of S&P

BSE SENSEX, BSE ALL CAP, BSE SMALL CAP, BSE MID CAP & BSE LARGE CAP. The

maximum average or mean return is occurred on Wednesday for all the indices and followed by

Monday and so on. This indicates that among the days of the week, mean returns for all the

trading days were different returns distributions. Hence the null hypotheses of the daily mean

returns are statistically equal across the trading days rejected.

Keywords:

INTRODUCTION:

Stock markets are considered as one of the major component of financial system of the

capitalistic world. They provide place for the trading of financial assets such as stocks and bonds

of joint stock companies, gilt edged securities, unit trusts and other financial products

efficiently, systematically, and by protecting the interest of investors. Stock market functions as

bridge and enables individuals as well as institutions to add in country’s wealth through their

participation in the secondary market of the country. A well-regulated stock market promotes the

The International journal of analytical and experimental modal analysis

Volume XII, Issue II, February/2020

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phenomenon of fair pricing of securities and reduces transactional costs. It stimulates economic

growth, promotes economic activity and an ample source of increasing employment in the

country. A good performance of stock market is a strong indicator of healthy economy. Because

of aforesaid reasons, stock traders keenly observe any single movement of stock index which

may affect their future profitability or help them to evaluate their portfolios. They also keenly

observe the economy; any sudden incident or change that may affect their decisions of buying

and selling stocks.

Calendar anomaly or Seasonality is the most puzzling topic in finance on which

numerous studies have been conducted during the last three decades. Some of these stock

anomalies are Day-of-the-Week Effect, Weekly Effect, Month-of-the-Year Effect, Weekend

Effect, Ramadan Effect and January Effect etc. The most interesting of them is Day-of-the-Week

Effect which has attracted attention of researchers around the world.

Day-of-the-Week Effect refers to the observations that mean stock returns are differently

distributed among different week days. This phenomenon contradicts the weak form of efficient

market hypothesis given by Fama (1970) which says that; all relevant information available to

the market participants should not allow them to earn abnormal returns (Marquering W. 2002).

More specifically, weak form of Efficient Market Hypothesis suggests that stock prices and stock

returns should be normally distributed. In contrast, the evidence collected from studies suggests

the presence of seasonal or calendar anomalies and thus stock returns are not constant.

STATEMENT OF THE PROBLEM

In the modern world, there are wide areas availed for investment, namely Bank Deposits,

Mutual Fund, Real Estates, Share Market and so on. Investment may be short term or Long term.

Short-term investments like Bank Deposits, Postal savings are having low risk. On the other

hand, long-term investment involves high risk and high return. Especially share market involves

high risk as well as high return. Basically, stock market price reflects the relevant information

about the market. Therefore, the investors are unable to follow the market prices hence, investors

can't earn any abnormal returns but investors can earn a normal return, sometimes may get the

loss. In order to earn an abnormal return, the investors have to understand the certain anomalies

to exist in the stock market.



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OBJECTIVES

The overall objectives of the study is to find out the day of the week effect. The following are

the more specific objectives.

1. To identify ‘day- of- the- week effect’ on the return of the various indices of BSE taken

for this study;

2. To know the descriptive statistic properties for the day of the week.

3. To know whether markets follow normal distribution; and

4. To study whether return series is stationary or not.

SCOPE OF THE STUDY

The study covers all the information related to the Day of the Week Effect in BSE

indices. It is confined to 10 years (1st April 2006 to 31

st March 2016) data of S&P BSE

SENSEX, BSE ALL CAP, BSE SMALL CAP, BSE MID CAP & BSE LARGE CAP. It also

includes the calculation of Return calculation, Descriptive statistic, Unit root test, OLS

regression. The analysis applies only to the selected indices taken for the study.

HYPOTHESIS

To prove Day-of-the-Week Effect on Monday

1) BSE Broad market indices (SENSEX, ALL CAP, SMALL CAP, MID CAP and LARGE

CAP) return series are stationarity in nature.

2) The daily mean returns for all the trading days were different returns distributions.

3) The BSE broad market indices daily returns series are not normally distributed.

METHODOLOGY

Ordinary Least Squares (OLS) Regression is the common approach employed by the

previous studies to investigate Day-of –the Week Effect in stock returns. But their shortcoming

lies in utilizing only one regression equation to find effect on all trading days of the week. This

approach is possible only if we hold a prior belief that an effect exists on one specific day, such

as Monday. However this specification is not appropriate if we have no previous expectation on

which of the Day-of –the Week Effect might exist. We overcome this short coming by estimating



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a different model for finding effect on each trading Day of the Week by omitting the dummy

variable for the day under scrutiny in each case.

Tools and Techniques Used This Study

(a) Return Calculation The daily returns are calculated as follow;

Rt = LN (Pt/Pt - 1)*100

Where:

Rt = Return of the day„t‟

LN = Natural Logarithm

Pt = closing price of the day and

Pt – 1 = closing price of the day (t – 1)

(b)Unit Root Test

The Augmented Dickey-Fuller (ADF) was used to test the stationarity of time-

series data of BSE indices.

(c) Descriptive Statistics

It consists of Mean, Maximum, Minimum, Standard Deviation, Skewness,

Kurtosis, Jarque-Beta Test and so on.

(d) Regression Equation

For investigating the day of the week effect the researcher used the following

regression equation including a constant term is Monday. For remaining days dummy

variables were included, suggested by Shilpa Lodha et al.,

Yt = α1 + α2DTue + α3DWed + α4DThu + α5DFri + ε1.

Where

Yt represents log return on the market index

α1 to α5 represent the mean returns for Monday through Friday

DTue to DFri represent the dummy variables taken for Tuesday to

Friday (so that DTue = 1 if day is Tuesday, zero otherwise and so

on)

ε1 is an error or residual term



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REVIEW OF LITERATURE:

Dr. Hem Chandra Kothari et al (2016), in his study is an attempt to measure Day-of-the-

Week Effect on the return and volatility of BSE and NSE indices for the period of 2005 through

2014. Along with the descriptive statistics, t-test and ANOVA has been used to capture mean

deference in returns for the trading days Monday through Friday. Mean returns of only one

index, Nifty Junior, has found statistically significant while using t-test whereas, no such

difference was observed in any of the index (BSE and NSE) in ANOVA. To confirm the findings

of t-test and ANOVA, an econometric model AR (1)-GARCH (1, 1) has been used. In contrast of

the findings of the other indices, return on Monday for BSE Small Cap has found statistically

significant. It has also observed that, volatility on Monday for return on BSE Small Cap is

statistically significant. Return on Tuesday, for BSE Small Cap and BSE Mid Cap, has found

negative and statistically significant. Returns on these two indices have also found negatively

volatile on Tuesday. Wednesday effect has only observed on Nifty Junior but, there is no

volatility captured on Wednesday for Nifty Junior.

Papia mitra & Gholam syedain khan (2014), reveals that Day of the week effect is one of the

most important calendar anomalies that have been observed in many stock markets in all over the

world with a lot of different results. Stock markets are speculative market, thus, investors are

more concerned about which day is the best for the trade. The primary objective of this paper is

to find out the significant day of the week effect in the emerging stock market of a developing

country like India for the period January 2001 to December 2012. In order to fulfill the

objectives of the paper, 5 models have been estimated. In each model risk factor is defined in

different ways thereby leading to different results. Empirical results verify that NSE Nifty 50

does not depict such day of the week effects on the intraday and inter-day stock returns. While

index exhibits Wednesday effect on inter-day return of the index, Monday gives lowest return

but maximum volatility. However, in certain cases, Friday also suffers from the lowest return

indicating presence of reverse weekend effect in the Indian stock market.

Ushad Subadar Agathee (2008), found the average returns of Stock Exchange of

Mauritius (SEM) to be the lowest in the Month of March and Highest in the Month of June. The

equality of means-return tests shows that returns were statistically the same across all months.



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The regression analysis reveals that returns were not independent of the Months of the Year,

except for January.

Fountas and Segredakis (2002), studied 18 markets and reported seasonal patterns in

returns. The reasons for the January effect in stock returns in most of the developed countries

such as US, and UK attributed to the tax loss selling hypothesis, settlement procedures, insider

trading information. Another effect is window dressing which is related to institutional trading.

To avoid reporting to many losers in their portfolios at the end of year, institutional investors

tend to sell losers in Decembers. They buy these stocks after the reporting date in January to hold

their desired portfolio structure again. Researchers have also reported half- month effect in

literature. Various studies have reported that daily stock returns in first half of month are

relatively higher than last half of the month. Ariel (1987) conducted a study using US market

indices from 1963 to 1981 to show this effect.

Poornima and Chitra (2014), in their recent study on Indian stock market found highest

mean return on Friday and the lowest mean return on Monday for the sample index NSE NIFTY.

The analysis of seasonality results point out there is no significant Friday Effect exists in NSE

NIFTY during the study period. Amarnani, Neeraj and Vaidya, Parth (2014), in their study on

calendar anomalies in Indian stock market, observed negative return on Monday for NSE NIFTY

and positive return on Monday for BSE SENSEX.

HYPOTHESES TESTING - 1

Research hypothesis (H1): BSE Broad market indices (SENSEX, ALL CAP, SMALL

CAP, MID CAP and LARGE CAP) return series are stationarity in nature.

Null hypothesis (H0): There is no sonority in return series of BSE Broad market indices

(SENSEX, ALL CAP, SMALL CAP, MID CAP, and LARGE CAP).



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AUGMENTED DICKEY-FULLER TEST

Table. 1

Index Type t-Value Test Critical Values

1% 5% 10%

S&P BSE SENSEX

Intercept

-25.27934 -3.4328 -2.86251 -2.56733

ALL CAP -44.554 -3.4328 -2.8625 -2.5673

SMALL CAP -23.182 -3.4328 -2.8625 -2.5673

MID CAP -40.906 -3.4328 -2.8625 -2.5673

LARGE CAP -46.051 -3.4328 -2.8625 -2.5673

Source: Compiled from EViews -7

Inference:

The unit root test of Augmented Dickey-Fuller test was used and presented in Table 1. It

shows that, Intercept test statistics of S&P BSE SENSEX (-25.27934), ALL CAP (-44.554),

SMALL CAP (-23.182), MID CAP (-40.906) and LARGE CAP (-46.051) are less than their

critical values of 1 per cent (-3.4328, 5 per cent (-2.8625) and 10 per cent (-2.5673) respectively.

The unit root test strongly evident that, BSE Broad market indices (SENSEX, ALL CAP,

SMALL CAP, MID CAP and LARGE CAP) return series are stationarity in nature. Hence, the

null hypothesis of no stationarity in return series is rejected.

DESCRIPTIVE STATISTICS

Table 2

DAY WISE DESCRIPTIVE STATISTICS OF S&P BSE SENSEX

DS MON. TUE. WED. THU. FRI. ALL

Mean 0.058048 -0.038490 0.066691 0.017597 0.017597 0.032039

Median 0.149175 0.037435 0.053767 0.044275 0.044275 0.076719

Maximum 13.96410 6.870393 5.800519 6.783617 6.783617 13.96410

Minimum -8.093658 -9.226862 -5.759626 -7.983364 -7.983364 -9.226862

Std. Dev. 1.609185 1.256198 1.214587 1.309063 1.309063 1.322421

Skewness 0.815425 -0.497656 -0.098879 -0.530880 -0.530880 -0.040129

Kurtosis 16.67386 11.77244 7.432203 9.192079 9.192079 13.16380

Jarque-Bera 3919.102 1614.139 408.4332 797.6062 797.6062 10670.98

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Observations 496 497 498 485 485 2479

Source: Compiled from EViews7.



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From the table 2, it is evident that descriptive statistics of the day of the week returns for

the S&P BSE SENSEX index. The maximum average or mean return is on Wednesday

(0.066691) and followed by Monday (0.058048), Thursday (0.017597) and so on. This indicates

that among the days of the week, mean returns for all the trading days were different returns

distributions. Therefore, the null hypothesis of daily returns are statistically equal across the

trading days rejected. The utmost volatility was found on Monday through the Maximum daily

returns (13.96410) and the value of standard deviation of (1.609185). The value of Skewness

returns distribution was found to be positive in Monday and rest of the days in a week are in

negative value. It’s evident that the daily trading returns are asymmetrical distributions. The

value of Kurtosis is greater than 3, it represent Leptokurtic distribution for all the trading days of

the week. Moreover, Jarque-Bera test suggests that rejects the null hypothesis of return series is

normally distributed at 1 per cent level of significance.

Table 3

DAY WISE DESCRIPTIVE STATISTICS OF BSE ALL CAP

DS MONDAY TUESDAY WED THU FRI ALL

Mean 0.020344 -0.014206 0.101690 -0.002888 0.054384 0.032070

Median 0.159786 0.061027 0.142933 0.130933 0.123712 0.127668

Maximum 14.47287 5.941618 6.190129 6.300756 6.654520 14.47287

Minimum -10.05495 -6.597079 -7.167723 -7.051077 -11.07457 -11.07457

Std. Dev. 1.774946 1.368420 1.420198 1.374672 1.558814 1.506255

Skewness 0.184649 -0.338920 -0.160932 -0.487205 -0.781521 -0.260674

Kurtosis 14.88552 6.064747 6.663595 6.852527 10.33661 10.89970

Jarque-Bera 2928.201 204.8424 281.7818 323.7245 1146.481 6471.406

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Observations 497 499 500 492 489 2478


From the table 3, it is predict that descriptive statistics of the day of the week returns for

the BSE ALL CAP index. The maximum average or mean return is on Wednesday (0.101690)

and followed by Monday (0.020344) and so on. This indicates that among the days of the week,



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mean returns for all the trading days were different returns distributions. Therefore, the null

hypothesis of daily returns are statistically equal across the trading days rejected. The utmost

volatility was found on Monday through the Maximum daily returns (14.88552) and the value of

standard deviation of (1.774946). The value of Skewness returns distribution was found to be

positive in Monday and rest of the days in a week are in negative value. Its evident that the daily

trading returns are asymmetrical distributions. The value of Kurtosis is greater than 3, it

represent Leptokurtic distribution for all the trading days of the week. Moreover, Jarque-Bera

test suggests that rejects the null hypothesis of return series is normally distributed at 1 per cent

level of significance.

Table 4

DAY WISE DESCRIPTIVE STATISTICS OF BSE SMALL CAP


Mean 0.067722 -0.025597 0.099995 -0.007604 -0.074125 0.017658

Median 0.310526 0.171035 0.225600 0.145052 0.083861 0.200481

Maximum 8.660121 6.476684 8.492419 4.027383 4.864603 8.660121

Minimum -10.83569 -8.441187 -7.146854 -8.388144 -7.971936 -10.83569

Std. Dev. 1.837907 1.441669 1.488620 1.376783 1.428234 1.522998

Skewness -1.205976 -0.759864 -0.373954 -1.236010 -0.917411 -0.940365

Kurtosis 8.906654 7.202517 7.779119 8.255743 6.986695 8.491103

Jarque-Bera 841.2590 413.5604 485.5364 688.7302 389.2186 3479.830

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Observations 496 497 498 490 485 2479



the BSE SMALL CAP index. The maximum average or mean return is on Wednesday

(0.099995) and followed by Monday (0.067722) and so on. This indicates that among the days of

the week, mean returns for all the trading days were different returns distributions. Therefore, the

null hypothesis of daily returns are statistically equal across the trading days rejected. The utmost



positive in Monday and rest of the days in a week are in negative value. It’s evident that the daily



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trading returns are asymmetrical distributions. The value of Kurtosis is greater than 3, it




Table 5

DAY WISE DESCRIPTIVE STATISTICS OF BSE MID CAP


Mean 0.025086 -0.019767 0.091297 -0.019977 0.035914 0.026658

Median 0.249869 0.136476 0.180300 0.122460 0.121451 0.168047

Maximum 11.11127 6.247612 7.835880 5.321059 6.213443 11.11127

Minimum -12.07639 -9.018693 -7.441482 -8.561900 -8.748935 -12.07639

Std. Dev. 11.73899 1.434355 1.442169 1.338388 1.444444 1.496384

Skewness -1.049460 -0.638106 -0.446853 -1.040911 -0.896371 -0.859807

Kurtosis 11.73899 8.112144 8.076195 8.811596 9.062979 10.26054

Jarque-Bera 1669.358 574.9199 551.2541 778.0512 807.8006 5750.512

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Observations 496 497 498 490 485 2479



the BSE MID CAP index. The maximum average or mean return is on Wednesday (0.091297)

and followed by Monday (0.025086) and so on. This indicates that among the days of the week,

mean returns for all the trading days were different returns distributions. Therefore, the null

hypothesis of daily returns are statistically equal across the trading days rejected. The utmost




trading daily returns are asymmetrical distributions. The value of Kurtosis is greater than 3, it






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Table 6

DAY WISE DESCRIPTIVE STATISTICS OF BSE LARGE CAP


Mean 0.009889 -0.017983 0.098835 0.004161 0.061866 0.031530

Median 0.106362 0.018688 0.094422 0.116413 0.087584 0.086757

Maximum 15.68286 5.777088 6.156546 6.683923 7.118784 15.68286

Minimum -8.579908 -6.600101 -7.314658 -7.107093 -11.81822 -11.81822

Std. Dev. 1.810017 1.392785 1.468384 1.421097 1.642286 1.553701

Skewness 0.751717 -0.193506 -0.050187 -0.271250 -0.678253 0.003210

Kurtosis 16.19270 5.858600 6.544977 6.382635 10.40451 11.19445

Jarque-Bera 3651.039 173.0152 262.0196 240.5988 1154.587 6933.141

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Observations 497 499 500 492 489 2478



the S&P BSE SENSEX index. The maximum average or mean return is on Wednesday

(0.098835) and followed by Monday (0.009889) and so on. This indicates that among the days of

the week, mean returns for all the trading days were different returns distributions. Therefore, the

null hypothesis of daily returns are statistically equal across the trading days rejected. The utmost




trading daily returns are asymmetrical distributions .The value of Kurtosis is greater than 3, it






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HYPOTHESES TESTING - II

Research hypothesis (H1): The daily mean returns for all the trading days were different

returns distributions.

Null hypothesis (H0): The daily mean returns are statistically equal across the trading

days.

DESCRIPTIVE STATISTICS

Table 7

Indices Monday Tuesday Wednesday Thursday Friday

S&P BSE SENSEX 0.058048 -0.038490 0.066691 0.017597 0.017597

ALL CAP 0.020344 -0.014206 0.101690 -0.002888 0.054384

SMALL CAP 0.067722 -0.025597 0.099995 -0.007604 -0.074125

MID CAP 0.025086 -0.019767 0.091297 -0.019977 0.035914

LARGE CAP 0.009889 -0.017983 0.098835 0.004161 0.061866


Inference:

From the table 7, it is evident that descriptive statistics of mean returns of different

indices on different trading days. The maximum average or mean return is occurred on

Wednesday for all the indices and followed by Monday and so on. This indicates that among the

days of the week, mean returns for all the trading days were different returns distributions. Hence

the null hypotheses of the daily mean returns are statistically equal across the trading days

rejected.

HYPOTHESES TESTING - III

Research hypothesis (H1): The BSE broad market indices daily returns series are not

normally distributed.

Null hypothesis (H0): The BSE broad market indices daily returns series are normally

distributed.



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JARQUE-BERA TEST

Table 8

Indices Monday Tuesday Wednesday Thursday Friday P-Value

S&P BSE SENSEX 3919.102 1614.139 408.4332 797.6062 797.6062 0.000*

ALL CAP 2928.201 204.8424 281.7818 323.7245 1146.481 0.000*

SMALL CAP 841.2590 413.5604 485.5364 688.7302 389.2186 0.000*

MID CAP 1669.358 574.9199 551.2541 778.0512 807.8006 0.000*

LARGE CAP 3651.039 173.0152 262.0196 240.5988 1154.587 0.000*


Inference:

From the table 8, it is evident that d Jarque-Bera test of normality distribution. The

coefficient value Jarque-Bera test all the trading days of the week for all the BSE broad market

indices are highly significant with 1 per cent level of significance. Hence, reject the null

hypothesis and accept the alternative hypothesis of the BSE broad market indices daily returns

series are not normally distributed.

OLS DUMMY VARIABLE REGRESSION

Table 9

DUMMY VARIABLE OF OLS REGRESSION MODEL FOR S&P BSE SENSEX

Variable Coefficient Std. Error t-Statistic Prob.

Monday (c) 0.069504 0.058636 1.185343 0.2360

Tuesday -0.107994 0.083423 -1.294538 0.1956

Wednesday -0.002813 0.083380 -0.033737 0.9731

Thursday -0.025769 0.083724 -0.307783 0.7583

Friday -0.051907 0.083943 -0.618358 0.5364

R-squared 0.000912 Mean dependent var 0.032039

Adjusted R-squared -0.000703 S.D. dependent var 1.322421

S.E. of regression 1.322885 Akaike info criterion 3.399523

Sum squared resid 4329.564 Schwarz criterion 3.411252

Log likelihood -4208.708 Hannan-Quinn criter. 3.403783

F-statistic 0.564685 Durbin-Watson stat 1.443188

Prob(F-statistic) 0.688313



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Source: Compiled from EViews7.

The table 9 clearly exhibits that, regression equation output for the OLS using dummy

variables for BSE Indices day of the week effect. Monday regression coefficients are positive

and other day’s regression coefficient is negative. None of the regression coefficients are

statistically significant at the five per cent level. The Adjusted R2 is negative and also the F-

Statistic with a low p-value is also showing the poor fit of the model. Moreover, The D-W

statistics is (1.443188), telling of the presence of positive serial correlation because it is less than

2, which showed that existence of serial correlation in the return series.

Table 10

DUMMY VARIABLE OF OLS REGRESSION MODEL FOR BSE ALL CAP


MONDAY C 0.021163 0.067525 0.313414 0.7540

TUESDAY -0.035369 0.095447 -0.370566 0.7110

WEDNESDAY 0.080527 0.095399 0.844100 0.3987

THURSDAY -0.024052 0.095786 -0.251099 0.8018

FRIDAY 0.033221 0.095933 0.346292 0.7292









The table 10 clearly shows that, regression equation output for the OLS using dummy

variables for BSE Indices day of the week effect. Monday, Wednesday and Friday regression

coefficients are positive and other day’s regression coefficient is negative. None of the regression

coefficients are statistically significant at the five per cent level. The Adjusted R2 is negative and

also the F-Statistic with a low p-value is also showing the poor fit of the model. Moreover, The



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D-W statistics is (1.777998), telling of the presence of positive serial correlation because it is

less than 2, which showed that existence of serial correlation in the return series.

Table 11

DUMMY VARIABLE OF OLS REGRESSION MODEL FOR BSE SMALL CAP


MONDAY C 0.091109 0.067493 1.349902 0.1772

TUESDAY -0.116705 0.096024 -1.215381 0.2243

WEDNESDAY 0.008886 0.095975 0.092589 0.9262

THURSDAY -0.098713 0.096370 -1.024309 0.3058

FRIDAY -0.165234 0.096623 -1.710086 0.0874


Adjusted R-squared 0.000379 S.D. dependent var 1.522998








variables for BSE Indices day of the week effect. Monday and Wednesday regression

coefficients are positive and other day’s regression coefficient is negative. None of the regression

coefficients are statistically significant at the five per cent level. The Adjusted R2

is positive but

the value is low and also the F-Statistic with a p-value is also showing the poor fit of the model.

Moreover, The D-W statistics is (1.452500), telling of the presence of positive serial correlation

because it is less than 2, which showed that existence of serial correlation in the return series.



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Table 12

DUMMY VARIABLE OF OLS REGRESSION MODEL FOR BSE MID CAP


MONDAY C 0.044821 0.066353 0.675488 0.4994

TUESDAY -0.064587 0.094402 -0.684173 0.4939

WEDNESDAY 0.046476 0.094354 0.492566 0.6224

THURSDAY -0.064798 0.094743 -0.683935 0.4941

FRIDAY -0.008906 0.094991 -0.093760 0.9253


















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Table 13

DUMMY VARIABLE OF OLS REGRESSION MODEL FOR BSE LARGE CAP


MONDAY C 0.010820 0.069653 0.155337 0.8766

TUESDAY -0.028803 0.098455 -0.292552 0.7699

WEDNESDAY 0.088015 0.098406 0.894409 0.3712

THURSDAY -0.006658 0.098804 -0.067388 0.9463

FRIDAY 0.051046 0.098956 0.515846 0.6060
















FINDINGS OF DAY OF THE WEEK EFFECT

STATIONARITY ANALYSIS:

1. It’s found that, the original or average share prices of BSE broad market indices namely

SENSEX, ALL CAP, SMALL CAP, MID CAP, and LARGE CAP are not stationarity in

nature.



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2. It’s found that, the daily returns distributions of series are stationarity in nature for all the

BSE board market indices.

DESCRIPTIVE STATISTICS:

1. It’s found that, the maximum average or mean return occurred on Wednesday for all the

broad market indices namely SENSEX (0.066691), ALL CAP (0.101690), SMALL CAP

(0.099995), MID CAP (0.091297), and LARGE CAP (0.098835).

2. It’s found that, the low or negative average or mean return occurred on Tuesday for all

the broad market indices namely.

3. It’s found that, among the trading days of the week, mean returns for all the trading days

were different returns distributions.

4. It’s found that, the maximum volatility, occurred on Monday for all the broad market

indices namely SENSEX (1.609185), ALL CAP (1.774946), SMALL CAP (1.837907),

MID CAP (11.73899), and LARGE CAP (1.810017).

5. It’s found that, low volatility occurred on Wednesday for SENSEX, Tuesday for ALL

CAP, Thursday for SMALL CAP, Thursday for MID CAP, Tuesday for LARGE CAP.

6. It’s found that the intraday traders utmost gain on Monday because for all the trading

days of the week Monday was high volatility day.

7. It’s found that, the daily trading days returns are negatively skewed distributions for all

broad market indices namely SENSEX, ALL CAP, SMALL CAP, MID CAP, and

LARGE CAP.

8. It’s found that, the value of Kurtosis greater than three, it represents Leptokurtic

distribution for all the trading days of the week.

9. The Skewness and Kurtosis are strongly evident that the daily returns distributions are

not normally distributed for all the broad market indices of BSE namely SENSEX, ALL

CAP, SMALL CAP, MID CAP, and LARGE CAP.

10. Jarque-Bera test also advocated that the daily returns series is not normally distributed for

all the broad market indices BSE namely SENSEX, ALL CAP, SMALL CAP, MID

CAP, and LARGE CAP.



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RESULT OF DAY OF THE WEEK EFFECT:

11. The Researcher used dummy variable regression equation for all the trading days of the

week for testing the day of the week effect. Hence, mean return of Monday taken as

Yardstick day for comparing returns of other days for all the BSE broad market indices.

Serial correlation were exited in every model, in order to remove the serial correlation the

proper ARIMA terms were included in the equation for all the BSE broad market indices.

After including the ARIMA terms the heteroscedasticity effect were persisted. Hence,

proper GARCH (p, q) model were applied for remove the heteroscedasticity effect for

day of the week effect.

Table

Summarised Result of GARCH model for Day of the week effect

Indices Monday Tuesday Wednesday Thursday Friday

SENSEX

ALL CAP

SMALL CAP

MID CAP

LARGE CAP

12. It’s found that, for SENSEX index regression coefficients for the dummy variables

representing Monday, Wednesday and Friday are positive; The Monday provides the

highest return than other trading days of the week at 1 per cent significant of the p-value.

It is a strong Monday effect.

13. It’s found that, for ALL CAP index the regression coefficients for the dummy variables

representing Monday, Wednesday and Friday are positive; The Monday provides the

highest return than other trading days of the week at 1 per cent significant of the p-value.

It is a strong Monday effect.

14. It’s found that, for SMALL CAP index the regression coefficients for the dummy

variables representing Monday value alone positive; The Monday provides the highest

return than other trading days of the week at 1 per cent significant of the p-value.



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However other regression coefficients for the dummy variables are negative obviously

indicate a strong Monday effect. On the other hand, Tuesday and Friday are negatively

significant at 1 per cent level

15. It’s found that, for MID CAP index the regression coefficients for the dummy variables

representing Monday and Wednesday are positive; The Monday provides the highest

return than other trading days of the week at 1 per cent significant of the p-value.

However other regression coefficients for the dummy variables are negative obviously

indicate a strong Monday effect. On the other hand, Tuesday coefficient negatively

significant at 1 per cent level.

16. It’s found that, for LARGE CAP index the regression coefficients for the dummy

variables representing Monday and Wednesday are positive; however other regression

coefficients for the dummy variables are negative and none of the coefficients are

significant. There is no day of the week effect persist in LARGE CAP index.

17. It understood that among the BSE broad market indices, Monday effect persistsin

SENSEX, ALL CAP, SMALL CAP, and MID CAP.

HYPOTHESES TESTING:

1. The unit root test strongly evident that, BSE Broad market indices (SENSEX, ALL CAP,

SMALL CAP, MID CAP and LARGE CAP) return series are stationarity in nature.

Hence, the null hypothesis of no stationarity in return series is rejected.

2. The maximum average or mean return is occurred on Wednesday for all the indices and

followed by Monday and so on. This indicates that among the days of the week, mean

returns for all the trading days were different returns distributions. Hence the null

hypotheses of the daily mean returns are statistically equal across the trading days

rejected.

3. The coefficient value Jarque-Bera test all the trading days of the week for all the BSE

broad market indices are highly significant with 1 per cent level of significance. Hence,

reject the null hypothesis and accept the alternative hypothesis of the BSE broad market

indices daily returns series are not normally distributed.



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SUGGESTIONS:

The Indian investors have to understand the market inefficiency i.e., calendar anomalies

exists in stock market. Through the anomalies the investors can earn abnormal return

rather than normal return.

The intraday day traders are advised to use Monday for the intraday trading because high

volatility found on Monday.

Through the day of the week effect Monday gives possible abnormal returns for five out

of four indices hence, investors are advised buy shares on other days of the trading and

sell shares on Monday.

CONCLUSION:

The researcher has taken an attempt to examine the existence of Day of the week effect in

BSE broad market indices namely SENSEX, ALL CAP, SMALL CAP, MID CAP, and LARGE

CAP. For the purpose of analysis the researcher used descriptive statistics, OLS dummy variable

regression, Serial Correlation test, proper ARIMA modelling terms and GARCH (p,q) . The

descriptive statistics evident that the maximum average or mean return occurred on Wednesday

for all indices and followed by Monday. The empirical results of the GARCH (p,q) shown that

among the BSE broad market indices, Monday effect persists in SENSEX, ALL CAP, SMALL

CAP, and MID CAP.

REFERENCES:

Punithavathi pandiyan (2013) – security analysis and portfolio management , Vikas

publishing house pvt ltd.

V.A. Avadhani (2007) – security analysis and portfolio management, Himalaya

publishing house, Mumbai

Donald E. Fischer (2004) - security analysis and portfolio management, prentile-hall of

indiapvt ltd.

Francis Cherunilam (2003) - Business Environment, Himalaya Publishing House ltd.

Y.K. Bhushan (2007) - Fundamentals of Business Organaisation & Management.

http://mpra.ub.uni-muenchen.de/46805.

http://www.abepro.org.br/biblioteca/ENEGEP2002_TR34_0666.pdf



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monday effect: day of the week effect of bse broad market...

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