2.1 INTRODUCTION - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/37492/12... · Kong, India,...
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69 2.1 INTRODUCTION Every piece of ongoing research needs to be connected with the work already done, to attain an overall relevance and purpose of the present study. The review of literature thus becomes a link between the proposed research and the studies already done. Therefore before proceeding towards analysis of stock market data, it was felt necessary to have a look at the work already done by others in this area. The review of available literature is important for various reasons. Review shows the originality and relevance of the research problem and also facilitates justification of proposed methodology. It tells the reader about aspects that have been already established or concluded by other authors and also gives a chance to the reader to appreciate the evidences that has already been collected by previous researchers, and thus projects the current research work in the proper perspective. It also prohibits the current study from being a replica of an earlier one. Most importantly it is also helpful in identifying research gaps so as to generate new original ideas and avoid duplicating results of other researchers. 2.2 REVIEW OF LITERATURE While getting through the available literature for seasonal effects, it was found that several types of effects have been tested. Therefore, the available researches were classified into three categories according to their focus of the study namely month-of-the-year effect, day-of-the-week effect and mixed effects. 2.2.1 Month-of-the-year Effect This category of reviews includes those studies which had the objective of exploring the existence and reasons for month-of-the-year effect across various countries. A summarized view of those studies has been presented in Table 2.1.
Transcript of 2.1 INTRODUCTION - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/37492/12... · Kong, India,...
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2.1 INTRODUCTION
Every piece of ongoing research needs to be connected with the work already
done, to attain an overall relevance and purpose of the present study. The review of
literature thus becomes a link between the proposed research and the studies already
done. Therefore before proceeding towards analysis of stock market data, it was felt
necessary to have a look at the work already done by others in this area. The review
of available literature is important for various reasons. Review shows the originality
and relevance of the research problem and also facilitates justification of proposed
methodology. It tells the reader about aspects that have been already established or
concluded by other authors and also gives a chance to the reader to appreciate the
evidences that has already been collected by previous researchers, and thus projects
the current research work in the proper perspective. It also prohibits the current
study from being a replica of an earlier one. Most importantly it is also helpful in
identifying research gaps so as to generate new original ideas and avoid duplicating
results of other researchers.
2.2 REVIEW OF LITERATURE
While getting through the available literature for seasonal effects, it was
found that several types of effects have been tested. Therefore, the available
researches were classified into three categories according to their focus of the study
namely month-of-the-year effect, day-of-the-week effect and mixed effects.
2.2.1 Month-of-the-year Effect
This category of reviews includes those studies which had the objective of
exploring the existence and reasons for month-of-the-year effect across various
countries. A summarized view of those studies has been presented in Table 2.1.
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Table 2.1: Summary of Researches Based on Month-of-the-year Effect
Sr.
No. Researchers Scope Confirmation Rejection
1 Patel (2014)1
BSE -- Month-of-
the-year
Effect
2 Albert, Ida and
Nasiru (2013)2
Treasury Bills of
Ghana
July effect in both
91 and 182 days
treasury bills
--
3 Ray (2012)3
BSE January Effect --
4 Debasish (2012)4
Gas, Oil &
Refineries Sectors
of NSE
September, August
and February --
5 Verma and
Sharma (2012)5
BSE -- Month-of-
the-year
Effect
6 Chia and Liew
(2012)6
Nikkei 225 index November Effect --
7 Das, Dutta and
Sabharwal (2011)7
Indian Stock
Market
Positive: November,
August and
December
Negative: March
--
8 Merreti and
Worthington
(2011)8
Australian Stock
Market
High returns in
April, July and
December
--
9 Hamid (2010)9
U. S. Corporate
Bond Market
High returns in
January and Low
Returns in March
--
10 Keong, Yat and
Ling (2010)10
11 Asian
Countries
December Effect – 7
Countries
--
11 Giovanis (2009)11
55 Stock Markets December Effect –
12 Countries
--
12 Tsuji (2009)12
Japanese Stock
Market
April Effect --
13 Haug and
Hirschey (2006)13
U. S. Equities January Effect in
small capitalization
stocks
--
14 Starks, Yong and Municipal Bond January Effect due --
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Zhang (2006)14
Closed-end Funds to tax-loss Selling
Hypothesis
15 Al-Saad (2004)15
Kuwait Stock
Market
July Effect --
16 Silvapulle
(2004)16
OECD countries January Effect --
17 Chen and Singal
(2003)17
NYSE, AMEX
and NASDAQ
December and
January Effect --
18 Ogden (2003)18
NYSE and
AMEX
Losses in April to
September and
profits in October
through March
--
19 Pandey (2002)19
BSE January Effect
20 Bhabra, Dhillon
and Remirez
(1999)20
NYSE and
AMEX
November and
Januray Effect
--
21 Maxwell (1998)21
Corporate Bond
Market
January
Effect
--
22 Friday and
Peterson (1997)22
Real Estate
Investment Trust
January Effect --
23 Priestley (1997)23
London Stock
Exchange
December, January
and April Effect
--
24 Haugen and Jorion
(1996)24
NYSE January Effect --
25 Johnston and Cox
(1996)25
NYSE and
AMEX
January Effect --
26 Clare, Psaradakis
and Thomas
(1995)26
U. K. Stock
Market
January Effect
27 Raj and Thurston
(1994)27
New Zealand
Stock Market
-- January
andApril
Effect
28 Kramer (1994)28
NYSE -- January
Effect
29 Kohers and Kohli
(1991)29
S&P composite,
S&P industrials,
S&P
transportation,
S&P utilities, and
S&P financial
January Effect in
large firms
--
72
index
30 Reinganum and
Shapiro (1987)30
London Stock
Exchange
January and April
Effect
--
31 Chan (1986)31
NYSE January Effect
32 Bondt and Thaler
(1985)32
NYSE January Effect --
33 Brown, Kim,
Kleidon and
March (1983)33
Stock Markets of
U. S. and
Australia
U. S. – January
Australia –
December-January
and July_August
--
34 Gultekin and
Gultekin (1983)34
NYSE and
American Stock
Exchange
April – U. K.
January -
--
35 Rozeff and Kinney
(1976)35
NYSE January Effect --
Brief explanation about the studies covered in Table 2.1 is as follows:
• Patel (2014)1 examined if any particular calendar month return can
effectively be used as a monthly barometer to accurately predict future
direction of the Indian stock market. The results indicated none of the
calendar month returns had consistent ability to accurately predict the
performance of the Indian stock market over the next twelve months. The
accuracy of prediction did not substantially improve whether the predictor
month had generated positive or negative returns. The results continued to
remain remarkably consistent when the predictability accuracy was analyzed
over time by examining the effect separately over years. The findings of this
study clearly demonstrated that the Indian stock market did not possess a
monthly barometer that can accurately predict future direction of the stock
market.
• Albert, Ida and Nasiru (2013)2 used regression on periodic dummies to
investigate the existence of month-of-the-year effects in the Ghanaian
Treasury bill rate and their significance by considering the 91-day and 182-
day bills rate. The results revealed that a pronounced month-of-the-year
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effects existed in both the 91-day and 182-day Treasury bills rate. It was also
realized that, the month of July averagely had the highest rate within the
period 1998 to 2012. However, the seasonal changes in Treasury bills rate
were not a reflection of the effect of celebrative periods.
• Ray (2012)3 stated that increasing globalization of the financial markets and
the flawless nature of cross border investment flows had sharpened interest
in emerging markets. The objective of the study was to investigate the
existence of seasonality in stock returns in Bombay Stock Exchange (BSE)
SENSEX. They used monthly closing share price data of the Bombay Stock
Exchange’s share price index from January, 1991 to December, 2010 for this
purpose. He used a combined regression –time series model with dummy
variables for months to test the existence of seasonality in stock returns. The
results of the study provided evidence for a month-of-the-year effect in
Indian stock markets confirming the seasonal effect in stock returns in India
and also support the ‘ tax-loss selling’ hypothesis and ‘January effect’.
• Debasish (2012)4 investigated the existence of seasonality in stock price
behaviour in Indian stock market and more specifically in the Gas, Oil and
Refineries sector. The period of the study was from 1st January 2006 to 31st
December 2010. For the purpose of analysis, the study had employed daily
price series selected eight Gas, Oil and Refineries companies were selected,
and used multiple regression technique to examine the significance of the
regression coefficient for investigating month-of-the-year effects. It was
found that all the eight selected Gas, Oil and Refineries companies evidenced
month-of-the-year effect and mostly either on September, August or
February. Only GAIL, and HPCL evidenced significant October and July
effect.
• Verma and Sharma (2012)5 investigated the existence of month of the effect
in return series of Indian stock market. The study based on the monthly
return data of the BSE SENSEX for the period from January 2001 to
December 2010 for the analysis. The month of year effect in Indian stock
market was examined using Unit root test, OLS regression model, and
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ARCH and ARIMA model. The results of the study provided no evidence in
favor of existence of month of year effect in Indian stock marketing post
liberalization period. Further, the study also shown that Indian stock market
had become efficient in post liberalization period. Finally, it was concluded
from the study that Indian stock market was efficient in weak form of
efficiency and Random Walk Theory worked in India.
• Chia and Liew (2012)6 found significant November effect in the Nikkei 225
index of the Tokyo Stock Exchange (TSE). This finding was consistent with
previous evidence supportive of tax-loss selling hypothesis for the stock
markets of U.S. and U.K. In addition, the estimated Threshold generalized
autoregressive conditional heteroscedasticity (TGARCH) model revealed no
significant asymmetrical effect on good and bad news. The existence of
month-of-the-year effect in TSE suggested that by means of properly timed
investment strategies, financial managers, financial counselors and investors
could take advantage of the patterns and gain profit.
• Dash, Dutta and Sabharwal (2011)7 stated that the presence of seasonal
effects in monthly returns had been reported in several developed and
emerging stock markets. The objective of their study was to explore the
interplay between the month-of-the-year effect and market crash effects on
monthly returns in Indian stock markets. The study used dummy variable
multiple linear regression to assess the seasonality of stock market returns
and the impact of market crashes on the same. The results of the study
provided evidence for a month-of-the-year effect in Indian stock markets,
particularly positive November, August, and December effects, and a
negative March effect. Further, the study suggested that the incidence of
market crashes reduces the seasonal effects.
• Merreti and Worthington (2011)8 examined the month-of-the-year effect in
Australian daily returns using a regression-based approach. The results
indicated that market wide returns were significantly higher in April, July
and December combined with evidence of a small cap effect with
systematically higher returns in January, August, and December. At the sub-
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market level, month-of-the-year effects are found in the diversified
financials, energy, retail, telecommunications and transport industries, but
not in the banking, healthcare, insurance, materials and media industries. The
analysis of the sub-market returns was also supportive of disparate month-of-
the-year effects. However, only in the case of small cap firms and the
telecoms industry did these coincide with the higher returns associated with
the January effect as typified in work elsewhere.
• Hamid (2010)9 explored monthly seasonality in high grade long term
corporate bonds from January 1926 to December 2008. He tested three types
of month effects. In addition, he analyzed the data based on Republican and
Democratic presidencies. The mean of monthly total returns for the entire
data set (0.50%) was significantly greater than zero. The mean return of
January was significantly higher than the mean of the other eleven months
stacked together; the mean of March was significantly lower. He found
significantly higher or lower volatilities for some months compared to the
other months. January experienced the highest mean monthly return,
followed by a dip in February and March, and then an upward trend until
January. The mean of monthly returns during the Republican presidencies
(0.66%) was significantly higher than during the Democratic presidencies
(0.33%). Though not fully efficient the U.S. corporate bond market exhibited
a high degree of efficiency.
• Keong, Yat and Ling (2010)10
investigated the presence of the month-of-the-
year effect on stock returns and volatility in eleven Asian countries- Hong
Kong, India, Indonesia, Japan, Malaysia, Korea, Philippines, Singapore,
Taiwan, China and Thailand. GARCH (1, 1) model was used to analyze the
stock returns pattern for a period of twenty years (1990-2009). Results
exhibited positive December effect, except for Hong Kong, Japan, Korea,
and China. Meanwhile, few countries did have positive January, April, and
May effect and only Indonesia demonstrated negative August effect.
• Giovanis (2009)11
examined the month-of-the-year and the January effect.
Since the most studies were restricted and repeated in major stock markets in
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the world, as Dow Jones Industrial and S&P 500 in USA and FTSE-100 in
UK among others, they tried to examine representative stock markets around
the world and the analysis was not restricted in national and regional level or
major stock markets, but was extended in global level. The results concluded
that January effects didn’t exist in global level and it was a very weak
calendar effect, as it was presented only in seven stock markets, while
December presented higher returns in twelve stock markets. Furthermore,
this study showed that the market efficiency hypothesis, always based on the
month-of-the-year effects, was violated, as in each stock market separately
monthly patterns, with the purpose of exploitation of profits, were
formulated.
• Tsuji (2009)12
showed that in Japan, big and low book-to-market equity
firms experienced higher risk-adjusted returns in April. He also revealed that
volatility in April was significantly lower than in other months. Furthermore,
he demonstrated that several trading strategies using this April effect could
produce profitable returns, even after considering transaction costs.
Moreover, additional analysis using the trading volume of financial
institutions implied that the abnormally higher returns of big firms and low
book-to-market equity firms appeared to be derived not from the tax-loss
selling effect but mainly from the dressing-up behaviour of Japanese
financial institutions at the end of the fiscal year.
• Haug and Hirschey (2006)13
documented by analysis of broad samples of
value-weighted and equal-weighted returns of U.S.equities that abnormally
higher rates of return on small-capitalization stocks continued to be observed
during the month of January. This January effect in small-cap stock returns
was remarkably consistent over time and did not appear to have been
affected by passage of the Tax Reform Act of 1986. This finding brought
new perspective to the tax-loss selling hypothesis and suggested that
behavioural explanations were relevant to the January effect.
• Starks, Yong and Zhang (2006)14
provided direct evidence supporting the
tax-loss selling hypothesis as an explanation of the January effect.
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Examining turn-of-the-year return and volume patterns for municipal bond
closed-end funds, which were held mostly by tax-sensitive individual
investors, they documented a January effect for these funds, but not for their
underlying assets. They provided evidence that this effect could be largely
explained by tax-loss selling activities at the previous year-end. Moreover,
they found that funds associated with brokerage firms display more tax-loss
selling behaviour, suggesting that tax counseling played a role.
• Al-Saad (2004)15
examined seasonality in the Kuwaiti stock market. The
purpose of the paper was to determine if a monthly pattern in the return of
stock market index existed in Kuwait, and whether such a pattern was similar
to the one found in developed stock markets. Daily data for the three indices
for the period from January 1985 to December 2002 were converted to
monthly observations by taking the arithmetic mean. The empirical results
showed significant July seasonality, which could be explained by the
summer holiday.
• Silvapulle (2004)16
investigated the seasonal behaviour of monthly stock
return series of some OECD countries and emerging economies. The
Bealieu-Miron’s (1993) and the Franses’ (1991) procedures were used for
testing for the presence of multiple unit roots at the monthly seasonal
frequencies, followed by Canova- Hansen's (1995) procedure for testing for
stability of seasonal patterns. Evidence suggested that many stock return
series were non-stationary at some monthly seasonal frequencies and that the
January effect was present in many stock returns. Utilizing the nature of
seasonality found in this study, the prediction of stock returns can be
improved.
• Chen and Singal (2003)17
presented evidence on the December effect. When
investors did not sell winner stocks in December but postponed their sale to
January so that capital gains would not be realized in the current fiscal year,
the "winners" appreciated in December. The December effect was relatively
easy to arbitrage. The initial sample for the study consisted of common
stocks traded on the NYSE, AMEX, and NASDAQ exchanges. The study
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covered the period January 1963 through December 2001. Evidences were
presented regarding the December effect and also persistence of the January
effect and note that the January effect continued because it was difficult to
exploit profitably.
• Ogden (2003)18
documented, for 1947–2000, seasonalities in economic
activity, stock and bond returns, and relationships among them. Evidence
was consistent with an annual cycle view of economic activity and risk
conditions. The power of lagged stock returns to forecast economic activity
was greater for quarters ending in December and March. Mean excess
returns on NYSE stocks in October through March accounted for 78–107%
of their annual means and reflected a seasonal asymmetric return reversal
tendency, which in turn explains low long-horizon variance ratios. Both
market losses in April through September and subsequent returns in October
through March were related, but with opposing signs, to October through
March economic activity. The forecasting power of five variables was
greatest for October through March. Tests of an asset-pricing model
indicated that expected returns vary both cross-sectionally and over time.
• Pandey (2002)19
stated that the presence of the seasonal or monthly effect in
stock returns had been reported in several developed and emerging stock
markets. This study investigated the existence of seasonality in Indian stock
market in the post-reform period. The study used the monthly return data of
the Bombay Stock Exchanges Sensitivity Index for the period from April
1991 to March 2002 for analysis. After examining the stationarity of the
return series, an augmented auto-regressive moving average model was
specified to find the monthly effect in stock returns in India. The results
confirmed the existence of seasonality in stock returns in India and the
January effect. The findings were also consistent with the "tax-loss selling"
hypothesis. The results of the study implied that the stock market in India
was inefficient, and hence, investors could time their share investments to
improve returns.
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• Bhabra, Dhillon and Remirez (1999)20
documented the existence of
seasonality in stock returns in the form of November Effect. The uniqueness
of the study was that this effect was observed only after the passage of Tax
Reform Act 1986. They documented a unique and significant relationship
between excess returns and the potential for tax-loss selling hypothesis. They
also showed that the January effect was likely due to the Act’s elimination of
the preferential treatment for capital gains. The evidence suggested that tax-
loss selling was a dominant explanation for the seasonality of stock returns.
• Maxwell (1998)21
examined the strength and causes of the January effect in
the corporate bond market. The findings supported a relation between this
anomaly and the small-firm effect. The January effect was found to be a
function of at least two phenomena. First, individual investors showed a
seasonal demand for noninvestment-grade bonds, but they showed no such
seasonal demand for investment-grade bonds. These findings were consistent
with the increased strength of the January effect as bond rating declined.
Second, the study demonstrated a shift in demand for higher-rated bonds at
year-end that was related to institutional window dressing.
• Friday and Peterson (1997)22
examined the January return seasonality of real
estate investment trust (REIT) common stock and underlying assets. Both
stock returns and the National Association of Realtors median home price
index exhibited January seasonals. However, the median home price index
explained little of the seasonal stock returns and a significant January effect
in stock returns remained for small REITs. Thus, information effects were
not the likely cause of the January effect in REITs. Further analysis indicates
that tax-loss selling was the more likely cause of the January effect.
• Priestly (1997)23
examined the nature of seasonality in UK stock returns. A
multifactor model of stock returns estimated. Data on fifty-nine randomly
selected, individual stock returns traded on the London Stock Exchange over
the period October I968 to December I993 were collected. The first finding
was that seasonalities in UK stock returns were caused by seasonalities in
expected returns. The evidence suggested that the seasonality in stock returns
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was due to the high risks involved in holding stocks, first in January and
December because this was an important period in the yearly business cycle
and has implications for current and subsequent levels of economic activity.
Second, the April seasonal might be related to the risk of changes in
government policy that may come about due to the annual Government
Budget and the end of the tax year, both of which may affect future
economic activity.
• Haugen and Jorion (1996)24
stated that the year-end disturbance in the prices
of small stocks that had come to be known as the January effect was
arguably the most celebrated of the many stock market anomalies discovered
during the past two decades. If this anomaly was exploitable and if the stock
market was reasonably efficient, one would expect that opportunity would
have been priced away by now. Evidence indicated, however, that the
January effect was still going strong 17 years after its discovery. The
magnitude of the effect had not changed significantly, and no significant
trend threatened its eventual disappearance.
• Johnston and Cox (1996)25
provided a direct test of the tax-loss selling
hypothesis. They isolated firms that were the most likely candidates for tax-
loss selling. For firms that experienced the largest declines in the last half of
the year, evidences were found of a strong positive relation between the level
of individual investor ownership and the abnormal January return in the
following year and a significant negative relation between firm size and
January returns. Further, firms that experienced a rebound in January were
smaller and had a higher proportion of individual ownership than firms that
did not rebound. Overall, this evidence was consistent with the tax-loss
selling hypothesis.
• Clare, Psaradakis and Thomas (1995)26
examined the nature and importance
of seasonal fluctuations in the UK equity market. The presence of seasonal
unit roots in the relevant time series was rejected, a result which suggested
the absence of non-stationary stochastic seasonal movements in the UK
equity market. But the results indicated, however, that the market tends to
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rise in both January, April and to a lesser extent in December and fall in
September. The results appeared to be robust across different size groups of
UK stocks. When risk was accounted for by considering a GARCH-M model
of equity returns, it was found that the average positive returns in January,
April and December and average negative returns in September are robust to
the inclusion of risk proxy in the conditional mean specification. Having
ruled out a 'size effect' and having controlled for equity market risk, the
results suggested that other explanations must be considered for the observed
seasonality. Some evidences were found in support of the 'window dressing'
hypothesis, which might explain the seasonal increase in January, and was
postulated that the seasonal increase experienced in April was due to the tax
year end on 5 April.
• Raj and Thurston (1994)27
reported that turn-of-the-year effect had been
observed in many markets throughout the world and various explanations
had been suggested for this anomaly in the markets. The ‘tax-loss selling’
hypothesis was one such explanation that had received some support. They
examined the validity of this hypothesis in the New Zealand context. Since
the financial year in New Zealand ends in March there should be an April
effect if the tax-loss selling theory was to hold. The study found that there
was neither a January effect nor an April effect in New Zealand. The small
size and the poor liquidity of the market might be factors influencing this
observation.
• Kramer (1994)28
asserted that many financial markets researchers had sought
an explanation for the role of January in stock returns. Any explanation of
this phenomenon that was consistent with rational pricing must specify a
source of seasonality in expected returns. The pervasive seasonality in the
macro economy was an appealing possibility. A multifactor model that links
macroeconomic risk to expected return was found to show substantial
seasonality in expected returns. This model accounted for the seasonality in
average returns, while the capital asset pricing model could not.
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• Kohers and Kohli (1991)29
demonstrated the existence of a January effect for
the S&P 500 index over the period from January 1930 through December
1988. With some exceptions, not only were the January returns the highest
among the monthly returns, but that month's variation per unit of return was
also the lowest. Because this anomaly also existed over the three sub-periods
examined in this paper, it was concluded that this phenomenon was not a
onetime occurrence. Furthermore, as virtually all firms on the S&P indexes
were relatively large in size, it was reasoned that the abnormal returns in
January were independent of the small firm effect Also, consistently for all
the S&P component indexes (i.e., S&P industrials, S&P transportation, S&P
utilities, and S&P financial) the January mean monthly returns were the
highest and had the lowest variations per unit of return compared to any
other month of the year. Therefore, the similarity in the results for the S&P
component indexes suggested that this seasonal anomaly existed in all
industries represented by the S&P indexes.
• Reinganum and Shapiro (1987)30
confirmed that after the imposition of a
capital gains tax, the British stock return data exhibited apparent tax effects
in both January and April. The seasonal component of stock market returns
was consistent with a January effect that was driven by the behaviour of
corporations and partnerships and with an April effect that was due to the
behaviour of individuals. Unlike the United States and Canada, the tax year
ends for individuals and corporations generally do not coincide. While
British individuals close their tax year on April 5, partnerships and
corporations typically select a December tax year end. But closer inspection
of the data, which involved studying the differential returns of winners and
losers in both months, indicated that we cannot attribute all the January
effect to tax-loss selling associated with the introduction of capital gains
taxation. The behaviour of winners and losers in April, however, was
consistent with the tax-loss-selling story.
• Chan (1986)31
tried to confirm that the January seasonal was associated with
losses in stock prices. The January effect found for both long- and short-term
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losses did not confirm or reject the existence of tax-motivated trading at the
end of the year nor did it suggest that investors depart from optimal tax
trading. The question of interest in this paper was whether there was pressure
on stock prices at the end of the year. If tax-loss selling indeed produced the
January seasonal, the evidence suggested that a distinction between short-
and long-term holding periods was not a significant factor, which was
contradictory to rational tax selling behaviour. In conclusion, the results were
inconsistent with a model that explained the January seasonal by optimal tax-
loss selling.
• Bondt and Thaler (1985)32
in their article “Does Stock Market Overreact?”
collected monthly return data for New York Stock Exchange (NYSE)
common stocks for the period between January 1926 and December 1982 to
find out the effect of overreaction of individual investors to unexpected and
dramatic news events on stock prices. The results were consistent with the
overreaction hypothesis but the overreaction effect was asymmetric i.e. it
was higher for loser portfolios than winner portfolios. Further, most of the
excess returns were found to be in January consistent with January Effect.
• Brown, Kim, Kleidon and March (1983)33
stated that the ‘tax-loss selling’
hypothesis had frequently been advanced to explain the ‘January effect’.
This paper concluded that U.S. tax laws did not unambiguously predict such
an effect. Since Australia had similar tax laws but a July–June tax year, the
hypothesis predicted a small-firm July premium. Australian returns showed
pronounced December–January and July–August seasonals, and a premium
for the smallest-firm deciles of about four percent per month across all
months. This contrasted with the U.S. data in which the small-firm premium
was concentrated in January. It was concluded that the relation between the
U.S. tax year and the January seasonal might be more correlation than
causation.
• Gultekin and Gultekin (1983)34
empirically examined stock market
seasonality in major industrialized countries. Evidence was provided that
there are strong seasonalities in the stock market return distributions in most
84
of the capital markets around the world. The seasonality, when it exists,
appeared to be caused by the disproportionately large January returns in most
countries and April returns in the U.K. With the exception of Australia, these
months also coincide with the turn of the tax year.
• Rozeff and Kinney (1976)35
presented evidence on the existence of
seasonality in monthly rates of return on the New York Stock Exchange from
1904–1974. With the exception of the 1929–1940 period, there were
statistically significant differences in mean returns among months due
primarily to large January returns. Dispersion measures revealed no
consistent seasonal patterns and the characteristic exponent seems invariant
among months. They also explored possible implications of the observed
seasonality for the capital asset pricing model and other research.
2.2.2 Day-of-the-week Effect
This is the second category of earlier studies reviewed by the researcher
which focuses on studies with the objective of finding out the existence of day-of-
the-week effect in a particular stock market. The researches may be summarized as
in Table 2.2.
Table 2.2: Summary of Researches Based on Day-of-the-week Effect
Sr.
No.
Researchers Scope Confirmation Rejection
1 Cicek (2013)36
BIST-100, BIST-
Financials, BIST-
Services, BIST-
Industrials and
BIST-Technology
Monday – All
except BIST-
Financials,
Tuesday –
BIST-
Industrials and
Services
BIST-Financials
2 Dimitrios and
Kyriaki (2013)37
U. S. Real Estate
Investment Trusts
-- Day-of-the-week
Effect
3 Shakila, Prakash
and Babitha
(2013)38
NSE Auto and
Pharma
Wednesday –
Auto sector
Pharma Sector
4 Mbululu and
Chipeta (2012)39
9 Sectoral Indies
of Johannesburg
Stock Exchange
Monday Effect
– Basic
Material Sector
Remaining 8
Sectors
85
5 Patel, Radadia
and Dhawan
(2012)40
BSE, Hang-Sang,
Tokyo and
Shanghai Stock
Exchange
-- All Markets
6 Al-Jafari
(2012)41
Muscat Securities
Market
-- Muscat Market
7 Sarangi, Kar and
Mohanthy
(2012)42
NSE Wednesday Monday
8 Caporale and
Gil-Alana
(2011)43
S&P, Dow Jones,
NYSE and
NASDAQ
Lower order
integration
between all
four markets
for Monday
and Friday
--
9 Lin, Ho and
Dollery (2010)44
KLCI Negative
Monday and
Positive
Wednesday
--
10 Tochiwou
(2010)45
West African
Regional Stock
Market
Lower returns
on Tuesday and
Wednesday;
Higher returns
on Thursday
and Friday
11 Algidede
(2008)46
7 African Stock
Markets
Significant
daily
seasonality in
Zimbabwe,
Nigeria and
South Africa
Egypt, Kenya,
Morocco and
Tunisia
12 Mangla (2008)47
NSE -- Day-of-the-week
Effect
13 Basher and
Sadorsky
(2006)48
21 Emerging
Markets
Day-of-the-
week Effect in
Pakistan,
Philippines and
Taiwan
Day-of-the-week
Effect in
remaining
countries
14 Chia, Liew, Syed
and Syed
(2006)49
Malaysian Stock
Markets
Negative
Monday
--
15 Hui (2005)50
Stock markets of Day-of-the- Day-of-the-week
86
Hong Kong,
Korea, Singapore,
Taiwan, U. S. and
Japan
week Effect
only in
Singapore
Effect in
remaining
countries
16 Sarkar and
Mukhopadhyay
(2005)51
BSE Day-of-the-
week Effect
--
17 Ali, Mehdian and
Perry (2004)52
Egyptian Stock
Market
-- Day-of-the-week
Effect
18 Gardeazabal and
Regulez (2004)53
Spanish Stock
Market
Positive
Monday and
Friday and
Negative
Wednesday and
Tuesday
--
19 Sarma (2004)54
SENSEX,
NATEX and BSE
200
Monday-Friday
set with
positive
deviations
--
20 Nishat and
Mustafa (2002)55
Karachi Stock
Exchange
-- Day-of-the-week
Effect
21 Demirer and
Karan (2002)56
Istanbul Stock
Exchange
Start-of-the-
week Effect
Weekend Effect
22 Brooks and
Persand (2001)57
5 Southeast Asian
stock markets
Day-of-the-
week Effect in
3 markets
Day-of-the-week
Effect in 2
countries
23 Chordia, Roll
and
Subrahmanyam
(2001)58
U. S. equities Strong Tuesday
and weak
Friday
--
24 Chen. Kwok and
Rui (2001)59
Chinese Stock
Market
Tuesday --
25 Marshall and
Walker (2000)60
Chilean Stock
Market
Positive Friday
and negative
Monday
--
26 Mookerjee and
Yu (1999)61
Shanghai and
Shenzen Stock
Markets
High returns on
Thursday
--
27 Kamara (1997)62
S&P 500 and
Small Cap indices
of NYSE
Monday Effect
87
28 Chang, Pinegar
and
Ravichandran
(1993)63
Stock markets of
24 countries
Monday Effect
in 11 countries
The brief elaboration of researches covered in Table 2.2 has been presented below:
• Cicek (2013)36
investigated the presence of the day-of-the-week effect on the
return and return volatility of the BIST (Borsa Istanbul) stock indexes, those
of the BIST-100, the BIST-Financials, the BIST-Services, the BIST-
Industrials, and the BIST-Technology for the period January 7, 2008 to
December 28, 2012 in Turkey. Empirical findings obtained from EGARCH
(1,1) model showed that the returns on Mondays were positive and the
highest during the week for all indexes, and only the BIST-Financials index
returns did not show the significant Monday effect. There wasn’t any
evidence of the day-of-the-week effect on the BIST-Financials returns. The
BIST-100 Industrials returns also showed a significant positive Tuesday and
Wednesday effects, while the BIST-Technology showed a positive Tuesday
effect. On Fridays, all index returns were positive and not significant except
the BIST-Services.
• Dimitrios and Kyriaki (2013)37
proposed to examine the US real estate
investment trusts (REITs) for the 2000-2012 period using GARCH models
that included the day-of-the-week effect and the stock-market index as
explanatory variables. This technique documented the return and volatility of
equity, mortgage and hybrid REITs. The study started with a CAPM model
and continued with GARCH(1,1), TGARCH(1,1) and EGARCH(1,1) models
for each of the REIT subcategories with and without the days of the week as
dummy variables. The results showed that the best-fitted model was
EGARCH except the equity REIT series without the dummy variables that
was better described with the GARCH. The stock market had a significant
impact on REIT returns but no remarkable significance in respect of the day-
of-the-week effect.
• Shakila, Prakash and Babitha (2013)38
examined the days of the week effect
in the two sectoral indices of National Stock Exchange, India for the period
88
from 1st April 2009 to 31st March 2011. Daily stock prices were converted
in to daily returns by taking natural log of the difference in the price at day t
and the price at day t-1. To test the equality of means for different days of
the week Kruskal-Wallis H test was used. The study discovered that three
companies in Auto sector and four companies in Pharma sector had highest
mean returns on Wednesdays. While subjecting the daily stock returns to
KWH test, during the study period it was found that the mean returns were
statistically significant on Wednesday only in Auto sector.
• Mbululu and Chipeta (2012)39
examined the existence of the day-of-the-
week effect in nine major sector indices listed on the JSE. These sectors
included Oil and Gas (J500), Basic Materials (J510), Industrials (J520),
Consumer Goods (J530), Health Care (J540), Consumer Services (J550),
Telecommunications (J560), Financials (J580) and Technology (J590). The
empirical results of this study showed the absence of the day-of-the-week
effects on skewness and kurtosis for eight of the nine JSE stock market
sectors. However, the Monday effect was detected for the basic materials
sector. As such, this study presented new evidence for the day-of-the-week
effect on the JSE. It was tentatively concluded from this study that the day-
of-the-week effect did not exist on the major JSE stock market sectors and
that the JSE was weak-form efficient.
• Patel, Radadia and Dhawan (2012)40
examined day-of-the-week effect in
four selected stock markets of Asian countries namely: India (Bombay Stock
Exchange), Hong Kong (Hong Kong Stock Exchange), Japan (Tokyo Stock
Exchange) and China (Shanghai Stock Exchange). The data included daily
adjusted closing index prices of Asian stock markets understudy from 1st
Jan. 2000 to 31st March. 2011. The data was also divided in three sub-
periods, - Period 1: from 05/01/2000 to 20/10/2003, Period 2: from
21/10/2003 to 29/06/2007 and Period 3: from 03/07/2007 to 31/03/2011.
BSE had maximum average return on Wednesday; Hang-Sang had highest
returns on Friday whereas, Nikkei and SSE Composite had highest returns on
Thursday and Wednesday respectively. The Monday was a day of high
89
volatility in Asian markets understudy. The return distributions in all market
were not normally distributed. The research suggested that there was no
evidence of “day-of-the-week effect” in the markets understudy during the
period. This finding was also similar in all sub-periods
• Al-Jafari (2012)41
investigated the anomalous phenomenon of the day-of-the-
week effect on Muscat securities market. The study used a sample that
covers the period from 1 December 2005 until 23 November 2011. It also
utilized a nonlinear symmetric GARCH (1,1) model and two nonlinear
asymmetric models, TARCH (1,1) and EGARCH (1,1). The empirical
findings provided evidence of no presence of the day-of-the-week effect.
However, unlike other developed markets, Muscat stock market seemed to
start positive and ended also positive with downturn during the rest of the
trading days. In addition, the parameter estimates of the GARCH model
suggested a high degree of persistent in the conditional volatility of stock
returns. Furthermore, the asymmetric EGARCH, and TARCH models
showed no significant evidence for asymmetry in stock returns. The study
concluded that Muscat securities market was an efficient market.
• Sarangi, Kar and Mohanthy (2012)42
reported that investors had a tendency
to search for investment opportunities. They investigated whether abnormal
patterns existed concerning rates of returns on Mondays. The paper tested the
seasonality of the stock market, using observations of 14 years, from 1998 to
2011, of the two major indices reported by National Stock Exchange (NSE),
i.e. Standard & Poor's (S & P) Nifty and CNX Nifty Junior. The day-of-the-
week effect was examined by using analysis of variance, Mann-Whitney U-
test and dummy variable regression analysis, which are tests for seasonality.
The results showed that Wednesdays’ returns were highest in both the
indices and there was non-existence of the Monday effect.
• Caporale and Gil-Alana (2011)43
used fractional integration techniques to
examine the degree of integration of four US stock market indices, namely
the Standard and Poor (S&P), Dow Jones, NASDAQ and New York Stock
Exchange (NYSE), at a daily frequency from January 2005 till December
90
2009. They analyzed the weekly structure of the series and investigated their
characteristics depending on the specific day-of-the-week. The results
indicated that the four series were highly persistent; a small degree of mean
reversion (i.e. orders of integration strictly smaller than 1) was found in some
cases for S&P and the Dow Jones indices. The most interesting findings
were the differences in the degree of dependence for different days of the
week. Specifically, lower orders of integration were systematically observed
for Mondays and Fridays, consistently with the ‘day-of-the-week’ effect
frequently found in financial data.
• Lim, Ho and Dollery (2010)44
investigated the ‘day-of-the-week’ effect and
the ‘twist of the Monday’ effect for Kuala Lumpur Composite Index for the
period May 2000 to June 2006. The empirical results found support for the
Monday effect in that Monday exhibited a negative mean return (0.09%) and
represented the lowest stock returns in a week. The returns on Wednesday
were the highest in a week (0.07%), followed by returns on Friday (0.04%).
Monday returns were partitioned into positive and negative returns; it was
found that the Monday effect was clearly visible in a ‘bad news’
environment, but it failed to appear in ‘good news’ environment. This study
also found evidence on ‘twist of the Monday’ effect, where returns on
Mondays were influenced by previous week’s returns and previous Friday’s
returns.
• Tochiwou (2010)45
provided the first evidence for the presence of the day-of-
the-week effects in West African regional stock market in the sample for the
period September 1998 to December 2007.The observed daily patterns
exhibiting lower daily means and lower standard deviations. In local
currency terms, a pattern of lower returns around the middle of the week,
Tuesday and then Wednesday; and a higher pattern towards the end of the
week, Thursday and then Friday, were observed.
• Algidede (2008)46
investigated the day-of-the-week anomaly in seven of the
Africa’s largest stock markets by looking at both the first and second
moments of returns. He also incorporated market risk. Result revealed that
91
day-of-the-week effect was not present in Egypt, Kenya, Morocco and
Tunisia. However, there were significant daily seasonality in Zimbabwe,
Nigeria and South Africa. Friday average return was found to be consistently
higher than other days in Zimbabwe. The Nigerian market displayed more
seasonality in volatility than in expected return but the reverse was true for
South Africa. Finally, the anomalies did not disappear even after accounting
for risk.
• Mangla (2008)47
explored in her article the existence of day-of-the-week
effect in Indian stock market. For the purpose of analysis she collected daily
close to close returns of S&P CNX Nifty from January 1991 to December
2007. Results showed that the mean returns were most negative on Tuesday
and Highest on Wednesday. With the use of non-parametric tests, market
inefficiency was confirmed. Further analysis revealed that the said
seasonality was confined to the period when NSE had Tuesday settlement.
With the introduction of rolling settlement high Wednesday returns
disappeared and seasonality in stock returns distribution across weekdays
became statistically insignificant.
• Basher and Sadorsky (2006)48
used both unconditional and conditional risk
analysis to investigate the day-of-the-week effect in 21 emerging stock
markets. In addition, risk was allowed to vary across the days of the week.
Different models produced different results but overall day-of-the-week
effects were present for the Philippines, Pakistan and Taiwan even after
adjusting for market risk. The results in this study showed that while the day-
of-the-week effect was not present in the majority of emerging stock markets
studied, some emerging stock markets did exhibit strong day-of-the-week
effects even after accounting for conditional market risk.
• Chia, Liew, Syed and Syed (2006)49
examined the calendar anomalies in the
Malaysian stock market. Using various GARCH models; this study revealed
the different anomaly patterns in this market for before, during and after the
Asian financial crisis periods. Among other important findings, the evidence
of negative Monday returns in post-crisis period was consistent with the
92
related literature. However, this study found no evidence of a January effect
or any other monthly seasonality. They suggested that the current empirical
findings on the mean returns and their volatility in the Malaysian stock
market could be useful in designing trading strategies and drawing
investment decisions. For instance, as there appears to be no month-of-the-
year effect, long-term investors may adopt the buy-and-hold strategy in the
Malaysia stock market to obtain normal returns. In contrast, to obtain
abnormal profit, investors have to deliberately looking for short-run
misaligned price due to varying market volatility based on the finding of
day-of-the-week effect. Besides, investors can use the day-of-the-week effect
information to avoid and reduce the risk when investing in the Malaysian
stock market. Further analysis using EGARCH and TGARCH models
uncovered that asymmetrical market reactions on the positive and negative
news, rendering doubts on the appropriateness of the previous research that
employed GARCH and GARCH-M models in their analysis of calendar
anomalies as the later two models assume asymmetrical market reactions.
• Hui (2005)50
extended the determination of day-of-the-week effect existing
in a sample of Asia–Pacific markets such as Hong Kong, Korea, Singapore
and Taiwan. At the same time, the presence of weekend effects in developed
markets of the US and Japan was also tested. In view of recent studies
regarding the disappearing day-of-the-week effect for US firms, they focused
on the recent years to better track the presence of weekend effects during and
after the Asian financial crisis in 1997 and the recent collapse of the blue
chip stocks in the United States. The results revealed that no evidence
existed of the day-of-the-week effect in all countries except Singapore. For
Singapore, it was low returns on Monday and Tuesday and high returns on
Wednesday to Friday.
• Sarkar and Mukhopadhyay (2005)51
suggested a systematic approach to
studying predictability and nonlinear dependence in the context of the Indian
stock market, one of the most important emerging stock markets in the
world. The proposed approach considered nonlinear dependence in returns
93
and envisages appropriate specification of both the conditional first- and
second-order moments, so that final conclusions were free from any probable
statistical consequences of misspecification. A number of rigorous tests were
applied on the returns, based on four major daily indices of the Indian stock
market. It was found that the Indian stock market was predictable, and this
observed lack of efficiency was due to serial correlation, nonlinear
dependence, day-of-the week effects, parameter instability, conditional
heteroskedasticity (GARCH), daily-level seasonality in volatility, the short-
term interest rate (in some sub periods of some indices), and some dynamics
in the higher-order moments.
• Ali, Mehdian and Perry (2004)52
investigated daily stock market anomalies
in the Egyptian stock market using its major stock index, the Capital Market
Authority Index (CMA), to shed some light on the degree of market
efficiency in an emerging capital market with a four-day trading week. The
results indicated that Monday returns in the Egyptian stock market were
positive and significant on average, but were not significantly different from
returns of the rest of the week. Thus, no evidence was uncovered to support
any daily seasonal patterns in the Egyptian stock market, indicating that
stock market returns were consistent with the weak form of market
efficiency. These results should be interpreted with caution since the
Egyptian stock market had only a limited number of stocks that are actively
traded.
• Gardeazabal and Regulez (2004)53
asserted that most empirical evidence on
stock market seasonality was based on the Dummy Variable Approach
(DVA). Typically, the DVA leaves too much variability of stock returns
unexplained and inference usually leads to weak or null evidence in favor of
seasonality. In this paper, he proposed an extended DVA (EDVA) which
leaves a lower fraction of stock return variability unexplained. Empirical
evidence was provided on daily seasonality in the Spanish stock market.
Inference based on the EDVA found positive and significant Monday and
Friday effects and negative and significant Wednesday and Thursday effects.
94
Extending the analysis to a model with GARCH conditional variances
confirmed these results and showed heavy daily seasonality in conditional
variances.
• Sarma (2004)54
explored the day-of-the-week effect on the Indian stock
market returns in the post-reform era. Daily returns generated by the
SENSEX, NATEX, and BSE200 during January 1st 1996 to August 10th
2002 comprising a total of 1,667 observations for each of the indices were
considered for testing the seasonality. This study employed the daily mean
index value for generating the daily returns earlier studies. A non-parametric
test, Kruskall-Wallis test using ‘H’ statistic, was employed for testing the
seasonality in the Indian stock market returns. The null hypothesis tested was
that there were no differences in the mean daily returns across the weekdays.
The findings suggested that the Indian stock markets did manifest seasonality
in their returns’ pattern. The Monday-Tuesday, Monday-Friday, and
Wednesday-Friday sets had positive deviations for all the indices. The
Monday-Friday set for all the indices had the highest positive deviation
thereby indicating the presence of opportunity to make consistent abnormal
returns through a trading strategy of buying on Mondays and selling on
Fridays. The above-mentioned active strategy was found to be beneficial in
case of SENSEX alone during the study period while for the others —
NATEX and BSE200 — a passive ‘buy and hold’ strategy was more
effective. The study concluded that the observed patterns were useful in
timing the deals thereby exploring the opportunity of exploiting the observed
regularities in the Indian stock market returns.
• Nishat and Mustafa (2002)55
investigated day-of-the-week effect in the
Karachi stock market. The daily data during December 14, 1991 to
December 31, 2001 and non-overlapping sub-sample period (December 14,
1991 to June 06, 1992: June 07, 1992 to February 27, 1997 and February 28,
1997 to December 31, 2001) were used to determine the day-of-the-week
effect in Karachi stock market by using mean and median approach. The
empirical results found no significant day-of-the-week effect on stock returns
95
and on conditional variance. However, significant positive variance was
found in third sub- sample period i.e. Tuesday and Wednesday effect on
volatility. This study also found first day and second day effect on trading
volume. First day was found to be the day with the lowest trading volume
and the second day was the day with the highest trading volume. This result
showed the process of information, which were ultimately incorporated in
trading activity. With the opening of the week, there were accumulations of
three days news, which ultimately affected the decision of the investors. The
investors were hasty on the first day-of-the-week and wait for further
information. Moreover, in the kerb market investors were hasty to sell the
shares in the last day-of-the-week, even though the estimates are statistically
insignificant.
• Demirer and Karan (2002)56
examined the evidences for the possible
existence of the "daily effect" in the Istanbul Stock Exchange (ISE). The
analysis of sign transitions between returns for successive days suggested
that the daily effect showed itself in a different form (start-of-the-week
effect) in the sense that starting a week with a positive return was an
indicator of the overall return pattern for the week. In the context of the
models developed in the literature, the findings indicated that the Turkish
market appeared efficient in terms of expected returns. However, it seemed
inefficient in terms of expected variability of these returns and in terms of
investors' expectations.
• Brooks and Persand (2001)57
examined the evidence for a day-of-the-week
effect in five Southeast Asian stock markets: South Korea, Malaysia, the
Philippines, Taiwan and Thailand. Findings indicated significant seasonality
for three of the five markets. Market risk, proxied by the return on the FTA
World Price Index, was not sufficient to explain this calendar anomaly.
Although an extension of the risk-return equation to incorporate interactive
seasonal dummy variables could explain some significant day-of-the-week
effects, market risk alone appeared insufficient to characterize this
phenomenon.
96
• Chordia, Roll and Subrahmanyam (2001)58
reported that previous studies of
liquidity spanned short time periods and focused on the individual security.
In contrast, they studied aggregate market spreads, depths, and trading
activity for U.S. equities over an extended time sample. Daily changes in
market averages of liquidity and trading activity were highly volatile and
negatively serially dependent. Liquidity crashed down significantly in down
markets. Recent market volatility induced a decrease in trading activity and
spreads. There were strong day-of-the-week effects; Fridays accompanied a
significant decrease in trading activity and liquidity, while Tuesdays
displayed the opposite pattern. Long- and short-term interest rates influenced
liquidity. Depth and trading activity increased just prior to major
macroeconomic announcements.
• Chen, Kwok and Rui (2001)59
investigated the day-of-the-week effect in the
stock markets of China. They found negative returns on Tuesday after
January 1, 1995. This Tuesday anomaly disappeared after taking the non-
normality distribution and spillover from other countries into account. The
finding suggested that this day-of-the-week regularity in China may be due
to the spillover from the Americas. The evidence of the day-of-the-week
anomaly in China was clearly dependent on the estimation method and
sample period. When transaction costs were taken into account, the
probability that arbitrage profits are available from the day-of-the-week
trading strategies seemed very small. This conclusion was obviously
consistent with an efficient market approach.
• Marshall and Walker (2000)60
studied empirical regularities of daily log
returns for the years 1989 through 1996, using aggregate indexes and
quintiles rated by size, for a specific emerging market: the case of Chile. The
study's main result showed important day-of-the-week effects on average
returns and traded volumes, but not on variances. These results, obtained
with both classical and non-parametric methods, were valid for aggregate
indexes, quintiles and sub-periods. They also found a seasonal pattern in the
size-effect, which it was significantly positive on Fridays and significantly
97
negative on Mondays. There was stronger evidence that favoured the
hypothesis that investors comply with weekly investment plans, as proposed
herein. Other results confirmed that daily returns in the Chilean stock market
behaved very much like the more developed countries', although the different
effects (size-, kurtosis and autocorrelation) were more pronounced.
• Mookerjee and Yu (1999)61
investigated seasonal patterns in stock returns on
the Shanghai and Shenzhen stock markets. The paper documented several
interesting findings. First, unlike studies for other stock markets, the highest
daily returns on both exchanges occurred on Thursday rather than Friday.
Second, price change limits exerted an effect on the observed daily pattern of
returns. Third, daily stock returns appeared to be positively correlated with
risk. This result was at odds with the majority of findings for other stock
exchanges around the world. Finally, the paper documented other differences
in seasonal patterns on the two exchanges.
• Kamara (1997)62
stated that equity derivatives and the institutionalization of
equity markets affected the Monday seasonal. The seasonal in the Standard
and Poor's 500 (S&P) declined significantly over 1962-93. This decline was
positively related to the ratio of institutional to individual trading volume. In
contrast, the seasonal for small stocks did not decline and was unaffected by
institutional versus individual trading. Higher trading costs sustained the
seasonal in small stocks, and unlike the S&P, these costs were not lower for
institutions than for individuals. Futures minus spot S&P returns exhibited a
reverse seasonal. Informed traders might use the less costly market to exploit
the seasonal.
• Chang, Pinegar and Ravichandran (1993)63
found that sample size and/or
error term adjustments rendered U.S. day-of-the-week effects statistically
insignificant. In contrast, day-of-the-week effects in seven European
countries and in Canada and Hong Kong were robust to individual sample
size or error term adjustments, and day-of-the-week effects in five European
countries survived the simultaneous imposition of both types of adjustments.
In most countries where day-of-the-week effects were robust, however, the
98
effects were statistically significant in not more than two weeks out of the
month. These findings were inconsistent with explanations of the day-of-the-
week effect based on institutional differences or on the arrival of new
information.
2.2.3 Mixed Effects
This section presents those studies which undertook two or more seasonal
trends as their objectives. It also includes those studies which had seasonal trends
other than day-of-the-week or month-of-the year effects as their objective. The
summary of these researches have been presented in Table 2.3.
99
Ta
ble
2.3
: S
um
ma
ry o
f R
esea
rch
es
Ba
sed
on
Mix
ed E
ffect
s
Sr.
No
.
Res
earc
hers
S
cop
e O
bje
ctiv
e C
on
firm
ati
on
R
ejec
tion
1
Bas
hir
and Z
eb (
201
5)6
4
Shan
gh
ai –
180,
NIK
KIE
-225, T
WH
and
Han
g-S
ang
Co
inci
den
ce o
f re
turn
effe
ct w
ith h
oli
day
effe
cts
Ho
lid
ay E
ffec
ts
--
2
Dey
shap
pri
ya
(201
4)6
5
Colo
mbo S
tock
Exch
ange
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. M
onth
-of-
the-
yea
r
Eff
ect
Januar
y E
ffec
t in
both
per
iods
and
Mo
nday
Eff
ect
on D
uri
ng-
War
per
iod
--
3
Aru
mugam
and
Sound
arar
ajan
(2013
)66
B
SE
and N
SE
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. M
onth
-of-
the-
yea
r
Eff
ect
--
Bo
th E
ffec
ts
4
Pat
hak
(2013)6
7
NS
E
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. M
onth
-of-
the-
yea
r
Eff
ect
--
Bo
th E
ffec
ts
5
Kuri
a an
d R
iro
(2
013)6
8
Nai
robi
Sto
ck
Exch
ange
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. M
onth
-of-
the-
yea
r
Eff
ect
3. W
eek
end
Eff
ect
All
Eff
ects
--
6
Gam
a an
d V
ieir
a (2
01
3)6
9
Port
ugues
e S
tock
Mar
ket
Holi
day
Eff
ect
Ho
lid
ay E
ffec
t
7
Ten
g a
nd L
iu (
201
3)7
0
Tai
wan
Sto
ck
Mar
ket
Holi
day
Eff
ects
H
igh p
re-h
oli
day
ret
urn
s --
8
Dia
conas
u, M
ehdia
n a
nd
Rom
ania
n E
quit
y
1. D
ay-o
f-th
e-w
eek E
ffec
t T
hurs
day
Eff
ect
M
onday
Eff
ect
and
Jan
uar
y
10
0
Sto
ica
(2012)7
1
mar
ket
2. M
onth
-of-
the-
yea
r
Eff
ect
Eff
ect
9
Khal
ed a
nd
Kee
f (
201
2)7
2
50 i
nte
rnat
ional
stock
ind
ices
1. T
urn
-of-
the-
month
Eff
ect
2. T
urn
-of-
the-
yea
r E
ffec
t
1.
Turn
-of-
the-
month
Eff
ect
2.
Turn
-of-
the-
yea
r
Eff
ect
--
10
Alm
onte
(20
12)7
3
Phil
ippin
e S
tock
Mar
ket
Wea
k F
orm
Eff
icie
ncy
--
D
ay-o
f-th
e-w
eek
Eff
ect,
Mo
nth
-of-
the-
yea
r E
ffec
t
and Q
uar
ter-
of-
the-
yea
r
Eff
ect
11
Alm
onte
(20
12)7
4
10 A
sian
Sto
ck
Mar
ket
s
Quar
ter-
of-
the-
yea
r
Eff
ect
--
Quar
ter-
of-
the-
yea
r
12
Kar
im, K
arim
and
Nee
(201
2)7
5
KL
CI
Holi
day
Eff
ects
--
H
oli
day
Eff
ects
13
Nag
eshw
ari
and S
elvam
(201
1)7
6
BS
E
Wea
k F
orm
Eff
icie
ncy
--
1.
Day
-of-
the-
wee
k
Eff
ect
2.
Mo
nth
-of-
the-
yea
r
Eff
ect
14
Cott
er a
nd
Dow
d (
2010
)77
D
EM
/US
D F
ore
ign
Exch
ange
Intr
aday
Sea
son
alit
y
Intr
aday
Sea
son
alit
y
--
15
Ble
y a
nd S
aad (
2010
)78
G
CC
Reg
ion
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. J
anuar
y E
ffec
t
3. H
oli
day
Eff
ect
All
Eff
ects
--
16
Rom
po
tis,
(20
09)7
9
Gre
ek E
quit
y
Mutu
al F
unds
1. D
ay-o
f –
the-
Eff
ect
2. M
onth
-of-
yea
r E
ffec
t
3. S
emi-
month
Eff
ect
Oth
ers
Mo
nth
-of-
the-
yea
r E
ffec
t
10
1
4. H
oli
day
Eff
ect
17
Ogun
c, N
ippan
i an
d
Was
her
(2009
)80
S
han
gh
ai a
nd
Shen
zhen
Sto
ck
Mar
ket
s
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. J
anuar
y E
ffec
t
Both
Eff
ect
in S
han
ghai
O
ther
s
18
Mer
ett
and
Wort
hin
gto
n
(200
9)8
1
Aust
rali
an S
tock
Mar
ket
Holi
day
Eff
ects
S
trong p
re-h
oli
day
Eff
ect
Post
-Holi
day
Eff
ect
19
Hong a
nd Y
u (
200
9)8
2
51 S
tock
Mar
ket
s S
um
mer
Vac
atio
n E
ffec
t L
ow
ret
urn
s du
rin
g s
um
mer
--
20
Stu
rm (
200
9)8
3
Month
ly R
eturn
s of
S&
P 5
00
In
dex
of
NY
SE
‘Oth
er’
Januar
y E
ffec
t
Januar
y h
eld g
reat
er
pre
dic
tive
pow
er d
uri
ng
cert
ain y
ears
of
the
pre
siden
t’s
term
in o
ffic
e
21
Maz
al (
2008)8
4
Indic
es o
f 2
9
cou
ntr
ies
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. M
onth
-of-
the-
yea
r
Eff
ect
1.
Day
-of-
the-
wee
k
Eff
ect
– 1
2 i
ndic
es
2.
Mo
nth
-of-
the-
yea
r
Eff
ect
– 1
7 i
ndic
es
--
22
Alg
ided
e (2
008)8
5
Afr
ican
Sto
ck
Ret
urn
s
1. M
onth
-of-
the-
yea
r
Eff
ect
2. H
oli
day
Eff
ect
Both
Eff
ects
--
23
McC
onn
el a
nd Y
u (
200
8)8
6
Sto
ck m
arket
s of
35
cou
ntr
ies
Turn
-of-
the-
mo
nth
Eff
ect
Turn
-of-
the-
month
Eff
ect
in
31 c
oun
trie
s
--
24
Lea
n, S
myth
and W
on
g
(200
7)8
7
Asi
an M
arket
s 1. D
ay-o
f-th
e-w
eek E
ffec
t
2. J
anuar
y E
ffec
t
Wee
kday
and M
onth
ly
Sea
sonal
ity
Januar
y E
ffec
t
25
Guo a
nd W
ang (
20
07)8
8
Shan
gh
ai S
tock
Index
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. J
anuar
y E
ffec
t
3. S
emi-
month
Eff
ect
Day
-of-
the-
wee
k E
ffec
t,
Mar
ch a
nd
July
Eff
ect
1.
Januar
y E
ffec
t
2.
Sem
i-m
onth
Eff
ect
10
2
26
Raj
an
d K
um
ari
(2006)8
9
BS
E a
nd N
SE
1. W
eek
day
Eff
ect
2. W
eek
end
Eff
ect
3. J
anuar
y E
ffec
t
4. A
pri
l E
ffec
t
Posi
tive
Mo
nday
and
Neg
ativ
e T
ues
day
Wee
ken
d E
ffec
t an
d
Januar
y E
ffec
t
27
Sey
yed
and A
l-H
ajji
(200
5)9
0
Sau
di
Ara
bia
n S
tock
Mar
ket
Ram
adan
Eff
ect
Syst
emat
ic P
atte
rn o
f
Dec
lin
ed V
ola
tili
ty d
uri
ng
Ram
adan
--
28
Al-
Saa
d a
nd M
oo
sa
(200
5)9
1
Kuw
ait
Sto
ck
Exch
ange
Holi
day
Eff
ect
Sum
mer
Ho
lid
ay E
ffec
t –
July
--
29
Kee
f an
d R
oush
(20
05)9
2
S&
P 5
00
In
dex
H
oli
day
Eff
ect
Up
to 1
987 –
Pre
-holi
day
Eff
ect,
Lab
our
Day
Eff
ect.
--
30
Yak
ub, B
eal
and
Del
pac
hit
ra (
2005)9
3
Ten
Asi
an S
tock
Mar
ket
s
1. D
ay-o
f –
the-
Eff
ect
2. M
onth
-of-
yea
r E
ffec
t
3. S
emi-
month
Eff
ect
4. H
oli
day
Eff
ect
Day
-of-
the-
wee
k –
5
coun
trie
s
Mo
nth
-of-
the-
yea
r – 8
coun
trie
s
Mo
nth
ly E
ffec
t – 6
co
untr
ies
Ho
lid
ay E
ffec
t –
4 c
ountr
ies
--
31
Josh
i an
d B
ahad
ur
(200
5)9
4
Nep
ales
e S
tock
Mar
ket
Sev
eral
Eff
ects
D
ay-o
f-th
e-w
eek E
ffec
t 1.
Holi
day
Eff
ect
2.
Turn
-of-
the-
mo
nth
3.
Tim
e-of-
the-
month
4.
Mo
nth
-of-
the-
yea
r
5.
Hal
f-M
onth
Eff
ect
32
Gao
and K
ling (
20
05)9
5
Shan
gh
ai a
nd
Shen
zhen
1. D
ay-o
f-th
e-w
eek E
ffec
t
2. M
onth
-of-
yea
r E
ffec
t
Fri
day
Eff
ect
Yea
r-en
d E
ffec
t
33
Kau
r (2
004)9
6
BS
E a
nd N
SE
1. D
ay-o
f-th
e-w
eek E
ffec
t 1.
Wed
nes
day
Eff
ect
1.
Wee
ken
d E
ffec
t or
10
3
2. W
eek
end
Eff
ect
3. J
anuar
y E
ffec
t
2.
Dec
ember
Eff
ect
Mo
nday
Eff
ect
2.
Sep
tem
ber
Eff
ect
34
Cou
tts
and S
hei
kh (
20
02)9
7
Gold
In
dex
of
Johan
nes
burg
sto
ck
Exch
ange
1. W
eek
end
Eff
ect
2. J
anuar
y E
ffec
t
3. H
oli
day
Eff
ect
--
All
Eff
ects
35
Bil
dik
(2001
)98
T
urk
ish S
tock
Mar
ket
Intr
aday
Eff
ect
Sig
nif
ican
t O
pen
ing
(Over
nig
ht)
and C
losi
ng
retu
rns
--
36
Chan
, K
han
thav
it a
nd
Th
om
as (
199
6)9
9
Indic
es o
f K
ual
a
Lu
mp
ur,
Bo
mb
ay,
Sin
gap
ore
and
Thai
land
Sev
eral
Eff
ects
D
ay-o
f-th
e-w
eek E
ffec
t- A
ll
4
Mo
nth
-of-
the-
yea
r E
ffec
t-
KL
SE
and S
ES
Chin
ese
New
Yea
r E
ffec
t -
KL
SE
and S
ES
Isla
mic
New
Yea
r E
ffec
t –
KL
SE
Wea
k H
oli
day
Eff
ect
- B
SE
--
37
Mil
ls a
nd
Coutt
s (1
99
5)1
00
F
T-S
E 1
00,
Mid
-
250 a
nd 3
50
Sev
eral
Eff
ects
Ja
nuar
y,
Wee
ken
d, H
alf
of
the
Mo
nth
and H
oli
day
Eff
ects
--
38
Won
g (
19
95)1
01
8 S
tock
Mar
ket
s In
tra-
month
Eff
ect
Intr
a-m
onth
Eff
ect
– U
. S
.
and A
ust
rali
a
Rev
erse
In
tra-
month
Eff
ect
–
Japan
Sin
gap
ore
, M
alay
sia,
Hon
g
Kon
g, T
aiw
an a
nd T
hai
lan
d
39
Gio
van
ni
and D
on
(199
4)1
02
S
&P
500
In
dex
Futu
res
Jan
uar
y, M
onth
ly a
nd
Quar
terl
y E
ffec
ts
--
All
Eff
ects
10
4
40
Aggar
wal
and
Tan
don
(199
4)1
03
S
tock
Mar
ket
s o
f 1
8
Coun
trie
s
1. W
eek
end
Eff
ects
2. T
urn
of
the
Mo
nth
Eff
ect
3. E
nd o
f D
ecem
ber
Eff
ect
4. M
onth
ly E
ffec
ts
5. F
riday
-the-
thir
teen
th
Eff
ect
Dai
ly S
easo
nal
– a
ll 1
8
Wee
ken
d E
ffec
t –
9
Turn
of
the
month
Eff
ect
Januar
y E
ffec
t
Mo
nth
ly E
ffec
t -
10
--
41
Gri
ffit
hs
and W
hit
e
(199
3)1
04
T
oro
nto
Sto
ck
Exch
ange,
NY
SE
and
AM
EX
Turn
-of-
the-
yea
r E
ffec
t T
urn
-of-
the-
yea
r E
ffec
t --
42
Cad
sby a
nd R
atner
(199
2)1
05
7 S
tock
Mar
ket
s T
urn
-of-
month
and
Pre
-
Holi
day
Eff
ects
Turn
-of-
mon
th –
Can
ada,
U.
K.,
Aust
rali
a, S
wit
zerl
and
and W
est
Ger
man
y
Pre
-ho
liday
Eff
ects
- A
ll
--
43
Ogd
en (
199
0)1
06
N
YS
E
Turn
-of-
month
Eff
ect
Turn
-of
Dec
ember
month
44
Ari
el (
19
90)1
07
D
JIA
P
re-H
oli
day
Eff
ects
P
re-H
oli
day
Eff
ect
--
45
Jaff
e an
d W
este
rfie
ld
(198
9)1
08
4 S
tock
Mar
ket
s M
on
thly
Eff
ect
Las
t d
ay o
f m
onth
Eff
ect
and
Mo
nth
ly E
ffec
t
--
46
Dic
kin
son a
nd P
eter
son
(198
9)1
09
C
all
and P
ut
Opti
on
s
Ret
urn
s
1.
Januar
y S
easo
nal
Eff
ect
2.
Day
-of-
the-
wee
k
Eff
ect
Cal
l O
pti
ons
– H
igh J
anuar
y
and L
ow
Monday
Put
Opti
ons
– L
ow
Jan
uar
y
--
47
Aggar
wal
and
Riv
oli
(198
9)1
10
S
tock
Mar
ket
s o
f
Hong K
on
g,
Sin
gap
ore
, M
alay
sia
Sea
sonal
and D
aily
Pat
tern
s
Day
-of-
the-
wee
k E
ffec
t,
Januar
y E
ffec
t, L
ow
Mo
nd
ay
and S
tro
ng T
ues
day
Eff
ect
--
10
5
and
Ph
ilip
pin
es
48
Rit
ter
and C
hopra
(19
89)1
11
Month
ly r
etu
rns
of
NY
SE
Turn
-of-
the-
yea
r E
ffec
t --
Ja
nuar
y E
ffec
t
49
Lak
on
ishok
and S
mid
t
(198
8)1
12
D
ow
Jo
nes
Indu
stri
al A
ver
age
Sev
eral
Eff
ects
T
urn
-of-
the-
Wee
k E
ffec
t,
Turn
-of-
the-
Month
Eff
ect,
Turn
-of-
the-
Yea
r E
ffec
t an
d
Ho
lid
ay E
ffec
t
--
50
Ari
el (
19
87)1
13
N
YS
E
Mon
thly
Eff
ect
Fir
st-h
alf
of
a m
onth
--
51
Kei
m (
1983)1
14
N
YS
E a
nd A
ME
X
1.
Mon
th-o
f-th
e-yea
r
Eff
ect
2.
Siz
e E
ffec
t
Januar
y E
ffec
t an
d S
mal
l
Siz
e E
ffec
t
--
52
Sha1
15
S
&P
CN
X N
ifty
an
d
Nif
ty J
unio
r
1.
Day
-of-
the-
wee
k
Eff
ect
2.
Mon
th-o
f-th
e-yea
r
Eff
ect
Day
-of-
the-
wee
k E
ffec
t an
d
Mo
nth
-of-
the-
yea
r E
ffec
t
--
106
The concise elucidation of researches summarized in Table 2.3 is as follows:
• Basher and Zeb (2015)64
reconnoitered the holiday effect of East Asian stock
markets over each other. SSE-180 index (Shanghai Stock Exchange),
NIKKIE 225 (Japan), TWII (Taiwan Weighted Index), HSI (Hang Seng)
epitomized East Asian region. The key objective was to investigate the return
effect on East Asian stock markets coinciding with the S&P 500 (Standard
and poor) holidays further the return effect on East Asian stock markets
during the trading session when there was no trading on other East Asian
stock markets. By means of TGARCH model using daily return of East
Asian Stock Exchanges and S&P 500 from January 1, 2003 to December 31,
2012. Day-of-the- Week effect, as well as regional and international
spillovers was considered, robust results were found by this study.
• Deyshappriya (2014)65
examined the stock market anomalies in Colombo
Stock Exchange (CSE) during the period of 2004 to 2013. The existences of
both day-of the Week Effect and Monthly Effect was tested using daily and
monthly data respectively. The Ordinary Least Squares (OLS) method and
GARCH (1, 1) model were employed to capture the day-of-the-week effects
and Monthly Effects along with the daily volatility behaviour. The sample
period was divided in to two periods as War Period and Post War Period in
order to take the impacts of the War in to account. The results indicated the
presence of negative Monday effect and the positive effects for all other days
only for the war period. Further, the positive volatility effect on Monday and
the negative volatility effect on Friday was examined for both war period and
the entire sample period with significant Wald F statistics.
Despite, the positive January effects were common for all sample periods,
the negative December effects cannot be identified for post war period. Hence, the
study confirmed the existence of Stock Market anomalies; both day-of-the-week
effect and monthly effect particularly during the war period. Moreover, these
seasonality patterns limited the validity of Efficient Market Hypothesis in the
context of Colombo Stock Exchange.
107
• Arumugam and Soundararajan (2013)66
investigated seasonality and time
varying volatility in the Indian stock markets. The researcher found that there
was a divergent cyclic pattern in investor actions that was reflecting not only
returns, but in all aspects of trade activity. The information diffusion
apparatus ensured that the stock returns across all days of the weeks and
months were equal and the market participant, the balanced financial
decision-maker, could not earn any extra-normal profits. By applying Chi-
sqaure Test, Kruskal-Wallis Test and ANOVA, it was concluded that the
means of the stock return and market return for five days were equal for the
sample companies listed at BSE and at NSE. This was applicable to both
daily and monthly returns of sample companies. It was also found that none
of the company had unequal mean returns. It was important to note that there
were variations in explosive nature of stock takings by the “each day-of-the
Week”, “every month-of-the-year” and “Semi-Month”. Besides, a high (low)
return was connected with a correspondingly high (low) volatility for a given
day. Changes in the market return and stock return of the selected companies
have been analyzed on daily basis, monthly basis and yearly basis. The
present study also premeditated the risk parameter for the market return and
stock return of the selected companies.
• Pathak (2013)67
proposed to examine stock market seasonality effect (month-
of-the-year effect and the day-of-the-week effect) in Indian stock market for
the S&P CNX Nifty (NSE). The data used in this study consisted of daily
closing prices of the market index (NSE-Index) over the period from 1st
April 2002 to 31st March 2012 for Month-of-the-year effect and 1st April
2007 to 31th march 2012 for day-of-the-week effect. To test for the presence
of the month-of–the-year effect and day-of-the-week effect on stock market
returns (NSE). Kruskal Walis test and one way ANOVA were used to see if
any significant difference exists in average daily returns across week day and
monthly return. The result of the study revealed non existence of the day
effect and month of year effect, which implies that the seasonality was not
present in Indian stock Market.
108
• Kuria and Riro (2013)68
examined the presence of day-of-the-week effect
anomaly in Nairobi Securities Exchange (NSE). For analysis t-test, F-test
and the ANOVA analysis model were used in the study. The study examined
three types of anomalies namely, day-of-the-week effect, weekend effect and
monthly effect. The analysis provided evidence about the presence of the
seasonal effect in the NSE. Thus it was established that the stock markets in
Kenya were not free from seasonal anomalies despite increased use of
information technology and numerous regulatory developments.
• Gama and Vieira (2013)69
provided evidence on the holiday effect by
analyzing Portuguese stock market behaviour on the days a public holiday
was not accompanied by a stock market break. Indeed, since 2003, when the
trading calendar of Portuguese stock market was harmonized with the
remaining Euronext national markets, on several occasions Portuguese
national holidays were not weekdays on which the stock market was closed.
Results showed a statistically significant negative liquidity effect and an
economically and statistically significant positive price effect during
Portuguese-specific national holidays relative to a typical trading day.
Return-related impact effects were driven by the smaller-sized stocks and
robust to the recent crisis period. These results suggested the prevalence of a
mood effect, by which those non-distracted traders’ positive feelings
translate into a buying pressure, or reluctance to sell, that drives up prices on
the onset of country-specific holidays.
• Teng and Liu (2013)70
presented a behavioural explanation of the pre-
holiday effect. For the period 1971 to 2011, firstly mean pre-holiday return
in Taiwan’s major stock market index was found statistically significantly
higher than the mean non-pre-holiday return. Second, the pre-holiday event
offered a return that differs from that on non-pre-holidays in an economically
significant manner. Third, the high return on pre-holidays was not
attributable to risk, other calendar anomalies, nor macroeconomic factors.
Finally, the pre-holiday effect was related to proxies for positive emotion
109
among investors. It was concluded that these findings were consistent with
the positive emotion and the pre-holiday effect hypothesis.
• Diaconasu, Mehdian and Stoica (2012)71
investigated the presence of the-
day-of-the week and the-month-of-the-year effects in the Romanian equity
market, using Bucharest Stock Exchange returns between 2000 and 2011.
While the presence of Thursday effect in Romanian equity market was
observed, they did not find any traditional Monday or January effect for the
entire sample period. Furthermore, they observed the January effect during
pre-crisis period. However, the subsample analysis provided very different
results, perhaps due to increasing degree of capital market maturity, EU
accession and other important events, such as the financial crisis.
• Khaled and Keef (2012)72
examined of the turn of the month (TOM) and turn
of the year (TOY) effects in 50 international stock indices, for the period
1994–2006 and characterised the degree that the effects were influenced by:
(i) the gross domestic product of the economy, (ii) the sign of the return on
the prior day (called the prior day effect), (iii) a temporal indicator and (iv)
the Monday effect. These effects were assessed by the use of an estimated
generalised least square (EGLS) panel regression model incorporating panel-
corrected standard errors. Three important results relating to the TOM and
TOY effects were observed. When the prior day effect on control days was
used as the reference and controls are made for market development and
year, they found that: (i) there was a relatively enhanced return on all TOM
days, (ii) there was a relatively enhanced return on good TOY days and (iii)
returns of bad TOY days were not remarkable.
• Almonte (2012)73
tested the returns of the Philippine stock market’s
Composite Index (PSEi) for conformity with weak form of market efficiency
using daily values from 2001 to 2010. Analyses were made annually and
cumulatively. The results revealed that the existence of a day-of-the-week
effect, the month-of-the-year effect was evidently absent, and the quarter-of-
the-year effect was also absent with the exception of the phenomenon
110
occurring in 2002. Thus, generally, traders are advised to buy equities on a
Tuesday and sell them on a Thursday or Friday.
• Almonte (2012)74
tested the quarter-of-the-year effect by using returns of ten
Asian stock market indices from 2001 until 2011. The following indices
were studied: the Hang Seng Index (HSI), the Jakarta Composite Index
(JCI), the Kuala Lumpur Composite Index (KLSE), the Seoul Composite
Index (KOSPI), the Nikkei Stock Average (NIKKEI), the Philippine
Composite Index or Philippine Stock Index (PSEi), the Bangkok SET Index
(SET), the Shanghai Composite Index (SSE), the Singapore Straits Times
Index (STI), and the Taipei Weighted Price Index (TWSE). Based on the
statistical tests, the quarter-of-the-year effect was non-existent in all indices.
However, the runs test showed that the returns exhibited a pattern while tests
for the month-of-the-year effect, another calendar anomaly, revealed that the
particular anomaly was non-existent.
• Karim, Karim and Nee (2012)75
used nine-year daily closing prices of Kuala
Lumpur Composite Index (KLCI) from beginning of the January 2001 to the
end of December 2009 to examine the holiday effects in Malaysia. Simple
dummy variable regression was applied for analysis purpose and the total
period was divided into three sub-periods of three years each. They
considered only important holidays as they admitted that looking and
lumping together insignificant holidays might reduce possible impact of the
more important holidays. The results showed that to some extent the pre-
holiday returns were higher than the other days. However, the equality test of
the mean returns was not rejected for all sample periods. Thus it indicated
that there was no holiday effect in Malaysia. Therefore, the Malaysian stock
market was considered informational efficient.
• Nageshwari and Selvam (2011)76
investigated the existence of seasonality in
India’s stock market. They asserted that the Efficient Market Hypothesis
suggests that all securities are priced efficiently to fully reflect all the
information intrinsic in the asset while the Seasonal Effects create higher or
lower returns depending on the Time Series. They are called Anomalies
111
because they cannot be explained by traditional asset pricing models. This
study explored the Indian Stock Market’s Efficiency in the ‘weak form’ in
the context of Seasonal Effects. For the purpose this analysis BSE SENSEX
index was chosen for a period of ten years from 1st April 2000 to 31st March
2010. Using a non-parametric test i.e. Kruskal-Wallis test and OLS
regression model, the study found that the Day-of-the-week Effect and
Monthly Effect Pattern did not appear to exist in the Indian Stock Market
during the study period.
• Cotter and Dowd (2010)77
examined the intra-day seasonality of transacted
limit and market orders in the DEM/USD foreign exchange market.
Empirical analysis of completed transactions data based on the Dealing
2000-2 electronic inter-dealer broking system indicated significant evidence
of intra-day seasonality in returns and return volatilities under usual market
conditions. Moreover, analysis of realised tail outcomes supported
seasonality for extraordinary market conditions across the trading day.
• Bley and Saad (2010)78
analyzed daily market index and company level
stock return data across the Gulf Cooperation Council (GCC) region in
search of calendar effects. The presence of day-of-the-week anomalies
suggested the existence of a global phenomenon. In spite of the unique status
of the Gulf region as a tax haven, company level data showed spill-over
effects of tax-selling that could be used to identify market segments with a
high presence of foreign investors trying to reduce the home tax burden as
traces of the January effect were found in these segments. Lastly, the
magnitude of the holiday effect depended not only on the cultural/religion
setting of a country market but on the cultural/religious background of its
participants also.
• Rompotis (2009)79
searched for seasonality patterns in performance of Greek
equity mutual funds during the period 2002-2005. Four types of seasonality
were assessed: day-of-the week effect, monthly effect, half-monthly effect
and holidays’ effect. Results revealed a negative Monday effect and a
positive Friday effect. Monday returns were also more volatile than the other
112
day-of-the-week returns. Furthermore, the paper demonstrated that the well-
known January effect did not apply to Greek equity funds while performance
was not affected by any other monthly impact either. However, a half-
monthly effect was revealed, namely returns during the first half of each
month exceeded these in the second half. Finally, a positive holiday effect on
returns was found in the week after Easter, August 15th and Christmas.
• Ogunc, Nippani and Washer (2009)80
investigated day-of-the-Week and
January Effects in the Shanghai and Shenzhen stock markets over the period
1990 to 2006 for both the ‘A’ and ‘B’ indices. During this period, these two
Chinese stock markets went through the limit period and non-limit period
and then again through a limit period. They examined the seasonality effects
both during the different periods and also over the whole period. Results
indicated that the Shanghai A index was prone to higher volatility and also
showed some January and Weekend Effects.
• Merett and Worthington (2009)81
examined the holiday effect in Australian
daily stock returns at the market and industry level and for small
capitalization stocks from Monday 9 September 1996 to Friday 10
November 2006. The eight annual holidays specified were New Years Day,
Australia Day (26 January), Easter Friday and Easter Monday, ANZAC Day
(25 April), the Queen's Birthday (second Monday in June), Christmas Day
and Boxing Day. A regression-based approach was employed. The results
indicated that the Australian market overall provided evidence of a pre-
holiday effect in common with small cap stocks. However, the market level
effect appeared to be solely the result of a strong pre-holiday effect in the
retail industry. No evidence was found of a post-holiday effect in any market
or industry.
• Hong and Yu (2009)82
used seasonality in stock trading activity associated
with summer vacation as a source of exogenous variation to study the
relationship between trading volume and expected return. Using data from
51 stock markets, they first confirmed a widely held belief that stock
turnover was significantly lower during the summer because market
113
participants were on vacation. They found that mean stock return was also
lower during the summer for countries with significant declines in trading
activity. This relationship was not due to time-varying volatility. Moreover,
both large and small investors traded less and the price of trading (bid-ask
spread) was higher during the summer. These findings suggested that
heterogeneous agent models were essential for a complete understanding of
asset prices.
• Sterm (2009)83
stated that the ‘other’ January effect posits that when
January’s stock returns are positive (negative), the remaining 11 months of
the year tend to be positive (negative) as well. While no explanation was
currently offered, this departure from market efficiency carried important
implications for the portfolio management decision. When the ‘other’
January effect was examined in the presence of the presidential election
cycle, it was clear that January held greater predictive power during certain
years of the president’s term in office. Therefore, in portfolio management
decisions, investors should not view either in isolation, but consider both
together.
• Mazal (2008)84
in his master’s thesis used a dummy variable approach and
an extended dummy variable approach to test for the existence of calendar
effects in the rates of return of common stocks. It applies the extended
dummy variable approach based on a factor model to returns of 30 stocks
traded at the German Stock Exchange and the dummy variable approach to
returns of 28 world indices. Furthermore, it investigated time persistence and
evolution of these calendar effects. Finally, it simulated two portfolio
strategies based on the Monday effect and the September effect. By
estimating a rolling dummy variable regression, this thesis provided evidence
confirming that the day-of-the-week effect started disappearing in the second
half of 1990s. The simulated portfolios were able to outperform the buy and
hold strategy in all the eight indices considered.
• Algidede (2008)85
stated that seasonal anomalies (calendar effects) might be
loosely referred to as the tendency for financial asset returns to display
114
systematic patterns at certain times of the day, week, month or year. Two
popular calendar effects were investigated for African stock returns: the
month-of-the-year and the pre-holiday effects, and their implication for stock
market efficiency. They extended the traditional approach of modeling
anomalies using OLS regressions and, examined both the mean and
conditional variance. They found high and significant returns in days
preceding a public holiday for South Africa, but this finding was not
applicable to the other stock markets in the sample. Their results also
indicated that the month-of-the-year effect was prevalent in African stock
returns.
• McConnel and Xu (2008)86
described that the turn-of-the-month effect in
U.S. equities was found to be so powerful in the 1926-2005 period that, on
average, investors received no reward for bearing market risk except at turns
of the month. The effect was not confined to small-capitalization or low-
price stocks, to calendar year-ends or quarter- ends, or to the United States:
This study found that it occurs in 31 of the 35 countries examined.
Furthermore, it was not caused by month-end buying pressure as measured
by trading volume or net flows to equity funds.
• Lean, Smyth and Wong (2007)87
stated that extensive evidence on the
prevalence of calendar effects suggested that there exist abnormal returns.
Some recent studies, however, have concluded that calendar effects have
largely disappeared. In spite of the non-normal nature of stock returns, most
previous studies had employed the mean-variance criterion or CAPM
statistics. These methods relied on the normality assumption and depended
only on the first two moments to test for calendar effects. A limitation of
these approaches was that they miss important information contained in the
data such as higher moments. In this paper they used a stochastic dominance
(SD) test to test for the existence of day-of-the-week and January effects.
They used daily data for 1988–2002 for several Asian markets. Their
empirical results supported the existence of weekday and monthly
115
seasonality effects in some Asian markets, but suggested that first-order SD
for the January effect had largely disappeared.
• Guo and Wang (2007)88
tried to explore seasonal effects and focused on
Shanghai Stock Exchange Composite Index. They tried to test the
seasonality in Chinese stock market by day-of-the-week effect, January
effect and semi-month effect. Deductive approach and quantitative research
method were used in their thesis. To analyze seasonality effect, the data were
collected from Shanghai Stock Exchange Index and were tested in four
periods: 1992-1996, 1997-2001, 2002-2006 and the whole period 1992-2006.
The results showed that seasonal anomalies like day-of-the-week effect,
positive March effect, and negative July effect existed in the Chinese stock
market, while semi-month effect did not occur significantly; but the existing
seasonal effect was not persistent over times. The above indicated that the
Chinese stock market was not fully efficient yet. Investors may have
opportunities to make use of the seasonal anomalies to earn abnormal return.
• Raj and Kumari (2006)89
attempted to investigate the presence of seasonal
effects in the Indian stock market. The seasonal effects in the Indian market
had been examined by the two major indices, the Bombay Stock Exchange
Index and the National Stock Exchange Index. They tested the efficiency of
the Indian stock market through week day effects, weekend, January and
April effects by applying a variety of statistical techniques. The negative
Monday effect and the positive January effects were not found in India.
Instead the Monday returns were positive while Tuesday returns were
negative. This study indicated that the Indian stock market did not exhibit the
usual seasonal anomalies such as Monday and January effect. The absence of
Monday effect could be due to the settlement period in Indian market.
• Seyyed and Al-Hajji (2005)90
asserted that calendar anomalies in stock
returns were well documented but the existence of seasonality in return
volatility associated with moving calendar events such as the Muslim holy
month of Ramadan was less obvious. Using a GARCH specification and data
for the Saudi Arabian stock market they documented a systematic pattern of
116
decline in volatility during Ramadan, implying a predictable variation in the
market price of risk. An examination of trading data showed that this
anomaly appeared to be consistent with a decline in trading activity during
Ramadan. Evidence of systematic decline in volatility during Ramadan had
significant implications for pricing of securities especially option-like
products and asset allocation decisions by investors in the Islamic countries.
• Al-Saad and Moosa (2005)91
investigated the nature of seasonality in the
monthly stock returns derived from a general index of the Kuwait Stock
Exchange. A structural time series model incorporating stochastic dummies
revealed that seasonality was present but it was deterministic as implied by
the constancy of the monthly seasonal factors over the sample period. Two
conventional models that incorporated deterministic seasonal dummies
corroborate these results. Moreover, seasonality was found to take the form
of a July effect, as opposed to the better-recognized January effect. This
finding was attributed to the ‘summer holiday effect’.
• Keef and Roush (2005)92
investigated the day-of-the-week effects in the pre-
holiday returns of the Standard & Poor's 500 index for the period 1930–
1999. The analysis was based on within-day contrasts and between-day
contrasts. There were three major findings. First, the results were consistent
with prior research in that there was a strong pre-holiday effect up to 1987,
but the pre-holiday effect was greatly diminished after 1987. Second,
contrary to that frequently observed in the literature for typical days, there
was no evidence of a weekend effect in pre-holiday returns. Third, a Labour
Day effect was observed in the pre-1987 era. The return on the day before
Labour Day was significantly greater than the return before other holidays
that fall on a Monday. However, this effect was not observed after 1987.
• Yakub, Beal and Delpachitra (2005)93
examined the issue of stock market
seasonality in the Asia Pacific stock market. Using the most recent set of
data, the paper employed the GARCH(1,1) and GARCH(1,1)-M models to
study the day-of-the-week, month-of-the-year, monthly and holiday effects
in ten Asia Pacific countries, namely Australia, China, Hong Kong, Japan,
117
India, Indonesia, Malaysia, Singapore, South Korea and Taiwan. Overall,
evidence to support the presence of day-of-the-week effect was documented
in five countries, month-of-the-year effect was detected in eight countries,
monthly effect was reported in six countries and holiday effect was found in
four countries. In most cases, the calendar effects could not be associated
with conditional risk. Although the presence of seasonality implied a lack of
informational efficiency in the respective stock market, this study did not
refute the validity of the Efficient Market Hypothesis, as the presence of
significant returns was not tantamount to abnormal profits. Further studies
were felt necessary to ensure that stock market seasonality can yield
significant returns in excess of transaction costs.
• Joshi and Bahadur (2005)94
examined the seasonality phenomenon
empirically in the Nepalese stock market for daily data of Nepal Stock
Exchange Index from February 1, 1995 to December 31, 2004 covering
approximately ten years. Using regression model with dummies, they found
persistent evidence of day-of-the-week anomaly but disappearing holiday
effect, turn-of-the-month effect and time-of-the-month effect. They also
documented no evidence of month-of-the-year anomaly and half-month
effect. Their result for the month-of-the-year anomaly was consistent to the
finding observed for the Jordanian stock market and that for the day-of-the-
week anomaly to the Greek stock market. In addition, their finding regarding
half-month effect was consistent with the US market. For the rest, they found
inconsistent results with that in the international markets. Their results
indicated that the Nepalese stock market was not efficient in weak form with
regard to the day-of-the-week anomaly but weakly efficient with respect to
the other anomalies.
• Gao and Kling (2005)95
examined calendar effects in Chinese stock market,
particularly monthly and daily effects. Using individual stock returns, they
observed the change of the calendar effect over time. In Shanghai and
Shenzhen, the year-end effect was strong in 1991 but disappeared later. As
the Chinese year-end is in February, the highest returns could be achieved in
118
March and April. Studying daily effects, they found that Fridays were
profitable. In addition, China differs in two major aspects related to calendar
effects, from other markets: the year ends in February, so one should not
expect a January effect; tax-loss selling was irrelevant, as there are no taxes
for capital gains. Especially, lacking taxes and the minor role of institutional
trading in China extinguish two main justifications for monthly calendar
effects. Hence, finding monthly patterns in China would require additional
explanations and might serve as a hint that former explanations cover just a
part of the story. Yet the daily effect possesses a minor magnitude and
relevance for determining average returns compared to monthly effects.
• Kaur (2004)96
investigated the nature and characteristics of stock market
volatility in India. The volatility in the Indian stock market exhibited
characteristics similar to those found earlier in many of the major developed
and emerging stock markets. Various volatility estimators and diagnostic
tests indicated volatility clustering, i.e., shocks to the volatility process
persist and the response to news arrival was asymmetrical, meaning that the
impact of good and bad news was not the same. Suitable volatility forecast
models were used for SENSEX and Nifty returns to show that the ‘day-of-
the-week effect’ or the ‘weekend effect’ and the ‘January effect’ were not
present while the return and volatility do show intra-week and intra-year
seasonality. The return and volatility on various weekdays have somewhat
changed after the introduction of rolling settlements in December 1999.
There was mixed evidence of return and volatility spillover between the US
and Indian markets. For both the indices, among the months, February
exhibits highest volatility and corresponding highest return. The month of
March also exhibits significantly higher volatility but the magnitude was
lesser as compared to February. This implies that, during these two months,
the conditional volatility tends to increase. This phenomenon could be
attributed to probably the most significant economic event of the year, viz.,
presentation of the Union Budget. The investors, therefore, should keep
away from the market during March after having booked profits in February
itself. The surveillance regime at the stock exchanges around the Budget
119
should be stricter to keep excessive volatility under check. Similarly, the
month of December gives high positive returns without high volatility and,
therefore, offers good opportunity to the investors to make safe returns on
SENSEX and Nifty. On the contrary, in the month of September, i.e., the
time when the third quarter corporate results are announced, volatility was
higher but corresponding returns are lower. The situation was, therefore, not
conducive to investors. The ‘weekend effect’ or the ‘Monday effect’ was not
present. For other weekdays, the ‘higher (lower) the risk, higher (lower) the
return’ dictum did not hold consistently and Wednesday provided higher
returns with lower volatility making it a good day to invest.
• Coutts and Sheikh (2002)97
investigated the existence of the Weekend,
January and Pre-Holiday effects in the All Gold Index on the Johannesburg
Stock Exchange over an 11-year period; 5 January 1987 through 15 May
1997, and for three sub-samples of equal length. Their results were in severe
contrast to the overwhelming international evidence documented for the
stock markets of many other countries, there appears to be no Weekend,
January or Pre-Holiday effects, present in the All Gold Index. This was
somewhat surprising as some financial economists had suggested that the
above seasonalities were now accepted "stylised facts". This paper suggested
that the lack of any detectable calendar effects, may, in part, be due to the
particular market microstructure of the Johannesburg Stock Exchange or the
composition of the All Gold Index.
• Bildik (2001)98
examined the intra-daily seasonalities of the stock returns in
the emerging Turkish Stock Market which was an order-driven market
using electronic trading without market makers, in the period from 1996 to
1999, by using 15-min (and also 5- and 1-min) interval data. Results showed
that stock returns follow a U-shaped or more precisely a W-shaped pattern
over the trading day at the Istanbul Stock Exchange (ISE) since there were
two separate trading sessions in a day. Opening (Overnight) and closing
returns were significantly large and positive. Volatility was higher at the
openings and followed an L-shaped pattern during the both sessions.
120
Interestingly, the daily average close-to-close returns were generated only
during the opening and closing intervals and the average intra-day return was
negative when the returns at the opening and/or closing intervals (even the
first and the last minutes of the day) were excluded from the analyses.
Relatively higher mean return and standard deviation at the openings of the
trading sessions seemed to be significantly generated by the accumulated
overnight information and the closed- market effect (halt of trade). Large
day-end returns were strongly affected by the activities of fund managers and
speculators for the window-dressing around the close. Finally, intra-day
seasonalities existed significantly also in the Turkish Stock Market as
consistent with those of the international stock markets.
• Chan, Khanthavit and Thomas (1996)99
used daily returns to identify
seasonality on the Kuala Lumpur Stock Exchange (KLSE), The Stock
Exchange, Bombay (SEB), the Stock Exchange of Singapore (SES) and The
Stock Exchange of Thailand (SET). On all four, they found strong day-of-
the-week effects. Month-of-the-year effects existed on the KLSE and the
SES but not on the SET or the BSE. Strong Chinese New Year effects were
evident on the SES and the KLSE. The Chinese New Year effect on the SET
was among small capitalization stocks. On the KLSE, they also found
Islamic New Year and Vesak effects, but no Aidilfitri effect. Only weak
holiday effects concerning several Indian lunar holidays were evident on the
BSE. In general they found that cultural holidays evidence a stronger effect
than state holidays. These results confirmed the importance of cultural
influences in the pricing of stocks.
• Mills and Coutts (1995)100
investigated the presence of various anomalies, or
‘calendar effects’, in the FT-SE 100, Mid 250 and 350 indices, and the
accompanying industry baskets, for the period January 1986 to October
1992. Their results broadly supported similar evidence found for many
countries concerning stock market anomalies, for the ‘January’, ‘weekend’
and ‘half of the month ’and‘ holiday effects all appeared to be present in at
least some of the indices.
121
• Wong (1995)101
explored that an intra-month effect on stock returns was
found in the US stock market and the Australian stock market, but a reverse
intra-month effect was found in the Japanese market. It was shown that such
an effect was almost non-existent in the stock markets of Singapore,
Malaysia, Hong Kong, Taiwan, and Thailand. The returns of these markets
seemed to be generated by a process which was fairly independent of other
major markets. This supported the argument that investors should diversify
beyond country boundaries.
• Giovanni and Don (1994)102
examined for the presence of “January,”
monthly and quarterly seasonalities from the volatilities implied from
options on the S&P 500 index futures. The testing methodology employed
was that of multiple-input intervention analysis which provided a rigorous
test of non-stationarity in implied volatilities given the presence of serial
correlation. Contrary to previous studies, results indicated the absence of any
of the above seasonalities, in ex-ante market risk.
• Aggarwal and Tandon (1994)103
examined five seasonal patterns in stock
markets of eighteen countries: the weekend, turn-of-the-month, end-of-
December, monthly and Friday-the-thirteenth effects. They found a daily
seasonal in nearly all the countries, but a weekend effect in only nine
countries. Interestingly, the daily seasonal largely disappeared in the 1980s.
The last trading day of the month had large returns and low variance in most
countries. Many countries had large December pre-holiday and inter-holiday
returns. The January returns were large in most countries and a significant
monthly seasonal existed in ten countries.
• Griffiths and White (1993)104
tested the tax-induced trading hypothesis as an
explanation of the turn-of-the-year anomaly using Canadian and U.S.
intraday data. Since the Canadian tax year-end preceded the calendar year-
end by five business days, tax effects might be isolated. It was found that the
anomaly was related to the degree of seller- and buyer-initiated trading and
depended upon the incidence of the taxation year-end. Seller-initiated
transactions (at bid prices) dominated until the tax year-end after which
122
buyer-initiated trades (at ask prices) dominated. The anomaly was a function
of bid-ask prices.
• Cadsby and Ratner (1992)105
examined turn-of-month and pre-holiday
effects on international markets. Turn-of-month effects were significant in
Canada, the UK, Australia, Switzerland, and West Germany. Pre-holiday
effects were significant in Canada, Japan, Hong Kong, and Australia. The
absence of these effects in certain markets suggested that they originate from
country-specific institutional practices. All countries exhibiting pre-holiday
effects did so before local holidays; only Hong Kong did so before US
holidays. This reinforced the conclusion that such anomalies were not
generated solely by American institutions.
• Ogden (1990)106
tested a hypothesis that the standardization of payments in
the United States at the turn of each calendar month generally induced a
surge in stock returns at the turn of each calendar month. The hypothesis also
asserted that returns generally would be greater following the month of
December and will vary inversely with the stringency of monetary policy.
Empirical results using stock index returns of NYSE for 1969-1986
supported the hypothesis. The analysis provided an explanation for the
previously documented monthly effect in stock returns and a partial
explanation for the January effect.
• Ariel (1990)107
reported that on the trading day prior to holidays, stocks
advanced with disproportionate frequency and showed high mean returns
averaging nine to fourteen times the mean return for the remaining days of
the year. Over one third of the total return accruing to the market portfolio
over the 1963–1982 periods was earned on the eight trading days which each
year fell before holiday market closings. Examination of hourly pre-holiday
stock returns revealed high returns throughout the day. Pre-holiday stock
returns in the post-test 1983–1986 period were also examined.
• Jaffe and Westerfield, (1989)108
reported that in a recent paper, Ariel
documented a monthly pattern for U.S. stock market returns. They examined
this pattern of returns in four other countries. They found only weak
123
evidence supporting this phenomenon in these foreign markets; just one
country exhibited a significant seasonal consistent with Ariel's work.
However, they did found stronger evidence of a ‘last day of the month’
effect. In addition there was evidence of a country unique monthly pattern
(i.e. one that was not consistent with the U.S. pattern).
• Dickinson and Peterson (1989)109
examined call and put option returns from
1983 to 1985 for the presence of a January seasonal effect, a monthly effect,
and a day-of-the-week effect. Results indicated the presence of seasonality in
call returns, with returns significantly higher in early January and
significantly lower on Mondays. Put returns generally exhibited less
seasonality, although out-of-the-money put options were significantly lower
in January and in-the-money put options were significantly lower in early
January. These results were generally consistent with stock return patterns.
• Aggarwal and Rivoli (1989)110
stated that the “January effect” and the
“weekend effect” have proven to be persistent anomalies in U.S. equity
markets. The objective of this paper was to examine seasonal and daily
patterns in equity returns of four emerging markets: Hong Kong, Singapore,
Malaysia, and the Philippines. These markets were gaining importance with
the globalization of business; therefore, it was felt necessary to examine the
efficiency and functioning of these capital markets. They used daily data for
the 12 years from September 1, 1976, to June 30, 1988. The results
supported the existence of a seasonal pattern in these markets. Returns in the
month of January were higher than any other month for all markets examined
except the Philippines. A robust day-of-the-week effect was also found.
These markets exhibited a weekend effect of their own in the form of low
Monday returns. In addition, there existed a strong “Tuesday effect,” which
may be related to the + 13 hour time difference between New York and these
emerging markets.
• Ritter and Chopra (1989)111
found that, for the 1935-1986 period, the
market's risk-return relation did not have a January seasonal. The findings
differed from those of other studies due to the use of value-weighted, rather
124
than equally weighted, portfolios. Inferences were sensitive to the weighting
procedure because of the small-firm return patterns in January. In particular,
even in those Januaries for which the market return was negative, small-firm
returns were positive, and they were more positive the higher was beta. This
was consistent with the portfolio rebalancing explanation of the turn-of-the-
year effect.
• Lakonishok and Smidt (1988)112
used 90 years of daily data on the Dow
Jones Industrial Average to test for the existence of persistent seasonal
patterns in the rates of return. Methodological issues regarding seasonality
tests were considered. They found evidence of persistently anomalous
returns around the turn of the week, around the turn of the month, around the
turn of the year, and around holidays.
• Ariel (1987)113
found that the mean return for stocks was positive only for
days immediately before and during the first half of calendar months, and
indistinguishable from zero for days during the last half of the month. This
‘monthly effect’ was independent of other known calendar anomalies such as
the January effect documented by others and appeared to be caused by a shift
in the mean of the distribution of returns from days in the first half of the
month relative to days in the last half.
• Keim (1983)114
examined month-by-month, the empirical relation between
abnormal returns and market value of NYSE and AMEX common stocks.
The sample consisted of firms listed on NYSE or AMEX during the
seventeen-year sample period from 1963 to 1979. Evidence was provided
that daily abnormal return distributions in January had large means relative
to the remaining eleven months, and that the relation between abnormal
returns and size was always negative and more pronounced in January than
in any other month – even in years when, on average, large firms earned
larger risk-adjusted returns than small firms. In particular, nearly fifty
percent of the average magnitude of the ‘size effect’ over the sample period
was due to January abnormal returns. Further, more than fifty percent of the
125
January premium was attributable to large abnormal returns during the first
week of trading in the year, particularly on the first trading day.
• Sha115
tried to examine the seasonality of stock market in India. They
considered the S&P CNX Nifty as the representative of stock market in India
and tested whether seasonality was present in Nifty and Nifty Junior returns
using daily and monthly data sets. The study found that daily and monthly
seasonality were present in Nifty and Nifty Junior returns. The analysis of
stock market seasonality using daily data, Friday Effect was found in Nifty
returns while Nifty Junior returns were statistically significant on Friday,
Monday and Wednesday. In case of monthly analysis of returns, the study
found that Nifty returns were statistically significant in July, September,
December and January. In case of Nifty Junior, June and December months
were statistically significant. The results established that the Indian stock
market was not efficient and investors can improve their returns by timing
their investment.
2.3 RESEARCH GAPS IDENTIFIED
After reviewing all the works previously done, it was observed that majority
of researchers tried to explore two or more effects together. Most of the studies
focused on foreign stock markets and further, sectoral indices were not studied.
Almost all the studies took closing prices for calculating returns. A wide variation
was reflected in the studies in terms of type of analysis technique used. Some studies
were based on traditional parametric and non-parametric tests while others used
time-series econometric techniques. Apart from these, it was also observed that
hardly any study could be traced by researcher which was based on primary survey.
Researcher could not find any single study with the objective of exploring trading
strategies used by common investors or market analysts or finding out whether they
are aware of seasonal stock market trends and they are using these strategies while
trading. Thus following points were identified as research gaps:
126
1. Very few studies have been undertaken on seasonality of Indian stock
markets.
2. Closing price was generally used by earlier researchers as if trading is done
at closing prices only, instead average price is proposed to be used in the
study.
3. Sectoral indices were not the area of interest of previous studies.
4. Researcher could not find studies based on primary survey.
Therefore, to fill this gap it was decided to take BSE and NSE to be
representative of Indian stock markets and to take average instead of closing prices.
Further, sectoral indices were also included in the study since stocks of different
sectors may have different types of calendar anomalies.
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