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.
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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.
70
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 --
71
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
73
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
74
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-
75
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
76
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.
77
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
78
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.
79
• 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
80
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
81
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.
82
• 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
83
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