Whether inexplicable patterns of abnormal stock market returns are detected in empirical studies of the stock market, a return anomaly is said to be found. There are other similar anomalies existing in the stock market. Economically meaningful stock market anomalies not only are statistically significant but also offer meaningful risk adjusted economic rewards to investors. Statistically significant stock market anomalies have yet-unknown economic and/or psychological explanations. A joint test problem exists because anomalies evidence that is inconsistent with a perfectly efficient market could be an indication of either market inefficiency or a simple failure of Capital Asset Pricing Model (CAPM) accuracy. Some of the most-discussed about market anomalies are return anomaly, market capitalization effect, value effect, calendar effect, and announcement effect. Though various studies have been conducted to find out the presence of these anomalies across the stock markets worldwide, very few studies with reference to Indian stock market are available in the financial literature. This study aims to find the evidence of one of the anomalies, calendar effect in BSE Sensex, India’s leading stock exchange.Anomalies; Calender Effecr; Indian Stock Market; SENSEX

Chandra, Abhijeet

English

Research Papers in Economics

STOCK MARKET ANOMALIES: 
A SURVEY OF CALENDAR EFFECT IN BSE-SENSEX 
 
Abhijeet Chandra 
 
Whether inexplicable patterns of abnormal stock market returns are detected in empirical studies 
of the stock market, a return anomaly is said to be found. There are other similar anomalies 
existing in  the  stock  market.  Economically  meaningful  stock  market  anomalies  not  only  are 
statistically significant but also offer meaningful risk adjusted economic rewards to investors. 
Statistically  significant  stock  market  anomalies  have  yet-unknown  economic  and/or 
psychological  explanations.  A  joint  test  problem  exists  because  anomalies  evidence  that  is 
inconsistent with a perfectly efficient market could be an indication of either market inefficiency 
or  a  simple  failure  of  Capital  Asset  Pricing  Model  (CAPM)  accuracy.  Some  of  the  most-
discussed about market anomalies are return anomaly, market capitalization effect, value effect, 
calendar effect, and announcement effect. Though various studies have been conducted to find 
out the presence of these anomalies across the stock markets worldwide, very few studies with 
reference to Indian stock market are available in the financial literature. This study aims to find 
the  evidence  of  one  of  the  anomalies,  calendar  effect  in  BSE  Sensex,  India’s  leading  stock 
exchange. 
 
Calendar effect connotes the changes in security prices in stock market following certain trends 
based on seasonal effects. Such trends or consistent patterns occur at a regular interval or at a 
specific time in a calendar year. Presence of such anomalies in any stock market is the biggest 
threat  to  the  concept  of  market  efficiency  as  these  anomalies  may  enable  stock  market 
participants beat the market by observing these patterns. This notion again violates the basic 
assumption of efficient market hypothesis (EMH) that no one can beat the market and earn the 
profit in excess of market. 
 
Anomalies are the result in the shortfalls in the models applied for testing market efficiency, 
rather than of inefficiency of market (Bowman and Buchanan, 1995). There could be a number 
of calendar anomalies that exist in the stock. A January effect has been documented in several   ii 
studies that show unusually large positive rate of returns for stocks during the first few trading 
days of the year (Cooper et al., 2006). Unusually good performance for the stocks on the day 
prior  to  market  closing  holidays  have  also  been  documented;  this  phenomena  be  known  as 
Holiday effect. Monday is the only day of the week that averages a negative rate of return. This 
Monday effect has given rise to the refrain, “Do not sell stocks on blue Monday”. Change in 
month itself acts as a psychological stimulator and boosts up the purchase behaviour in stock 
market. This implies that average daily returns of stock on turn of the month are different from 
the average daily returns in rest of the month. Ariel (1987) was the first to identify this anomaly 
in US stock prices at the beginning of one month and end of the other month. He studied this 
effect by considering last day of one month and the first three days of upcoming month. Changes 
in stock prices in these days are found positive. However different studies have given different 
conclusions for this effect which could be opposite to these results as well.  
 
Daily stock returns are also different from each other at different points of time during a month. 
This study tried to test this difference by dividing a month into segments and then analyzing the 
returns for these segments separately in order to find out that in which segment daily stock 
returns are highest. 
 
Different researchers have used different event windows to study Turn of the Month Effect. Ariel 
(1987) while evaluating turn of the month effect, defined his event window as (-1, +4) i.e. last 
working day of previous month and first four days of upcoming month. Lakonishok and Smidt 
(1988) analyzed Dow Jones Industrial Average (DJIA) for turn of the month effect with an event 
window of (-1, +3) i.e. last working day of previous month and first three days of new month. 
Evidence of turn of the month effect for USA, Canada, Switzerland, Germany, UK and Australia 
has also been found, however no such effect has been reported fro Japan, Hong Kong, Italy, and 
France (Cadsby and Ratner, 1992). Existence of turn of month effect has also been proved for 
stock markets of eighteen countries in 1970s (Agarwal and Tondon, 1994). 
 
As far as the Time of Month Effect is concerned, very first evidence about this effect is found 
back in 1999 when Kohers and Patel tested this effect in USA using S&P index for period 1960-
1995 and NASDAQ index for period 1972-1995 by dividing whole month into three parts. Their   iii 
results revealed stock returns were highest in first segment, second segment showed a decreasing 
trend and in third and last segment returns were either too low or negative in most cases. Cadsby 
and Torbey (2003) questioned the robustness of Kohers and Patel’s (1999) time of month effect 
and argued whether it is a reality or just a mirage or illusion? Their study focused on two things. 
One whether time of  month effect is present in other than US  markets as well, and second 
whether it is independent of turn of month effect or just an extension of it? Significant results for 
Time of Month effect were found for Canada, France, Switzerland, Germany, UK, Hong Kong 
and Italy. For three out of nine countries, other than USA, time of month effect partially presents 
when turn of month days are removed. But two other countries provided evidence in opposite 
direction as well. 
 
This study has been conducted to find out whether Turn of the Month Effect and Time of the 
Month Effect in BSE-SENSEX. Data pertaining to daily stock index of SENSEX, the capital 
weighted index of Bombay Stock Exchange (BSE) for the period April 1998 to March 2008 has 
been used in this study. 
 
In  order  to  test  the  calendar  effect,  daily  logarithmic  market  returns  are  calculated  for  the 
specified period of ten years using the following formula: 
Rt = 100 X Ln (Pt / Pt-1) 
 Where: 
Rt = Continuously compounded rate of change 
Ln = Natural Log 
Pt = BSE 30 index at time t 
Pt-1 = BSE 30 index at time t-1. 
 
For Turn of the Month effect, a five-day window has been used as suggested by Hansel and 
Ziemba (1996) and following regression equation has been run to find out the Turn of Month 
effect: 
Rt = β0 + β1d2t + εt 
Significant positive value of coefficient β1 will prove the Turn of the Month effect. 
   iv 
For Time of the Month Effect, this study followed the model given by Bahadur and Joshi (2005). 
Following regression equation with dummy variables is used to find out the Time of the Month 
effect: 
Rt = β0 + β1d2t + β2d3t + εt 
One significant positive value of any of the coefficients β1 and β2 can prove the Time of the 
Month effect in Bombay Stock Exchange (BSE 30 SENSEX). 
 
This study has been conducted to test the market efficiency in Indian stock market by examining 
calendar effect present in Bombay Stock Exchange, the largest stock exchange in India. In order 
to test the evidence of calendar anomalies, BSE’s leading index BSE 30 SENSEX has been 
selected as a sample for this study.  
 
Results from this study reveal that a very anomalous behaviour towards returns has been found 
in BSE 30. For both the effects, the Turn of the Month effect as well as the Time of the Month 
effect, significant values were found. Both the effects are found to be almost same. Returns 
during a month are analyzed by dividing that month into three parts separately. And it was found 
that early days of the month witness higher mean returns than later days of the same month. The 
reason  behind  this  trend  could  be  the  cognitive  belief  of  investors  with  regard  to  new  and 
positive changes in policies and newer information in the coming month. This results in selling 
pressure by investors with the hope to get positive benefits, leading to low returns at the end of 
month. With the beginning of new month, investors start buying into stocks following the same 
cognitive belief and incorporating new information. 
 
Existence of  these anomalies in  Bombay  Stock  Exchange  is  against  the  principle  of  market 
efficiency as it may offer abnormal economic rewards to the investors tracking these anomalies. 
Those at helm should chalk out policies to check this anomalous behaviour of the stock market 
so that the market could become really efficient. 
 
 
 
   v 
Key References: 
1)  Agarwal, A., K. Tandon (1994), “Anomalies or Illusions? Evidence from Stock Markets 
in Eighteen Countries”, Journal of International Money and Finance, 13(1), 83-106. 
2)  Ariel, R.A. (1987), “A Monthly Effect in Stock Returns”, Journal of Financial Economics, 
18, 161-174. 
3)  Buchanan, John (1995), “The Efficient Market Hypothesis – A Discussion of Institutional 
Agency and Behavioural Issues”, Australian Journal of Management, 20, 2. 
4)  Cadsby,  C.  B.,  V.  Torbey  (2003),  “Time-of-month  anomaly:  Reality  or  Mirage?”, 
Applied Economic Letters, 10(1), 741-745. 
5)  Cooper, Michael, John McConnell, and Alexei Ovtchinnikov (2006), “The Other January 
Effect”, Journal of Financial Economics, 82(2), 315-341. 
6)  Hensel, R., M. Ratner (1992), “Investment Results from Exploiting TOM Effects”, Journal 
of Portfolio Management, 22(3), 17-23. 
7)  Kohers,  T.,  J.B.  Patel  (1999),  “A  New  Time  of  the  Month  Anomaly  in  Stock  Index 
Returns”, Applied Economic Letters, 6(2), 115-120. 
8)  Lakonishok.  J.,  S.  Smidt  (1998),  “Are  Seasonal  Anomalies  Real?  A  Ninety  Years 
Perspective”, Review of Financial Studies, 1(4), 403-425. 

Stock Market Anomalies: A Calender Effect in BSE-Sensex

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