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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: vinothkumarjohn@yahoo.com
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
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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
Source: Compiled from EViews -7
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
DS MONDAY TUESDAY WED THU FRI ALL
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
Source: Compiled from EViews -7
From the table 4, it is evident that descriptive statistics of the day of the week returns for
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
volatility was found on Monday through the Maximum daily returns (8.906654) and the value of
standard deviation of (1.837907). 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
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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 5
DAY WISE DESCRIPTIVE STATISTICS OF BSE MID CAP
DS MONDAY TUESDAY WED THU FRI ALL
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
Source: Compiled from EViews -7
From the table 5, it is evident that descriptive statistics of the day of the week returns for
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
volatility was found on Monday through the Maximum daily returns (11.73899) and the value of
standard deviation of (11.73899). 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 daily 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.
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Table 6
DAY WISE DESCRIPTIVE STATISTICS OF BSE LARGE CAP
DS MONDAY TUESDAY WED THU FRI ALL
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
Source: Compiled from EViews -7
From the table 6, 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
(0.098835) 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
volatility was found on Monday through the Maximum daily returns (16.19270) and the value of
standard deviation of (1.810017). 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 daily 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.
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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
Source: Compiled from EViews -7
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*
Source: Compiled from EViews -7
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
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.000782 Mean dependent var 0.032070
Adjusted R-squared -0.000834 S.D. dependent var 1.506255
S.E. of regression 1.506883 Akaike info criterion 3.659979
Sum squared resid 5615.432 Schwarz criterion 3.671713
Log likelihood -4529.714 Hannan-Quinn criter. 3.664241
F-statistic 0.483998 Durbin-Watson stat 1.777998
Prob(F-statistic) 0.747526
Source: Compiled from EViews -7
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
The International journal of analytical and experimental modal analysis
Volume XII, Issue II, February/2020
ISSN NO: 0886-9367
Page No:42
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
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.001992 Mean dependent var 0.017658
Adjusted R-squared 0.000379 S.D. dependent var 1.522998
S.E. of regression 1.522710 Akaike info criterion 3.680875
Sum squared resid 5736.327 Schwarz criterion 3.692605
Log likelihood -4557.444 Hannan-Quinn criter. 3.685135
F-statistic 1.234608 Durbin-Watson stat 1.452500
Prob(F-statistic) 0.293980
Source: Compiled from EViews -7
The table 11 clearly exhibits that, regression equation output for the OLS using dummy
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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ISSN NO: 0886-9367
Page No:43
Table 12
DUMMY VARIABLE OF OLS REGRESSION MODEL FOR BSE MID CAP
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.000798 Mean dependent var 0.026658
Adjusted R-squared -0.000818 S.D. dependent var 1.496384
S.E. of regression 1.496996 Akaike info criterion 3.646813
Sum squared resid 5544.227 Schwarz criterion 3.658543
Log likelihood -4515.225 Hannan-Quinn criter. 3.651073
F-statistic 0.493867 Durbin-Watson stat 1.610583
Prob(F-statistic) 0.740270
Source: Compiled from EViews -7
The table 12 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.
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ISSN NO: 0886-9367
Page No:44
Table 13
DUMMY VARIABLE OF OLS REGRESSION MODEL FOR BSE LARGE CAP
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.000756 Mean dependent var 0.031530
Adjusted R-squared -0.000860 S.D. dependent var 1.553701
S.E. of regression 1.554369 Akaike info criterion 3.722032
Sum squared resid 5974.924 Schwarz criterion 3.733766
Log likelihood -4606.598 Hannan-Quinn criter. 3.726294
F-statistic 0.467747 Durbin-Watson stat 1.843873
Prob(F-statistic) 0.759463
Source: Compiled from EViews -7
The table 13 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.
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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ISSN NO: 0886-9367
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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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ISSN NO: 0886-9367
Page No:47
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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ISSN NO: 0886-9367
Page No:48
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.
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Punithavathi pandiyan (2013) – security analysis and portfolio management , Vikas
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V.A. Avadhani (2007) – security analysis and portfolio management, Himalaya
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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.
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ISSN NO: 0886-9367
Page No:49
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The International journal of analytical and experimental modal analysis
Volume XII, Issue II, February/2020
ISSN NO: 0886-9367
Page No:50
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