The efficient market hypothesis and calendar anomalies: A literature review

Abstract

One of the most important principles used in measuring the market's efficiency is the ability of prices to reflect all currently available information. The Efficient Market Hypothesis (EMH) is the proposition that current stock prices fully reflect all available information about the value of the firm and that there is no way to earn excess profits by using this information. The EMH has received an abundance of attention since its inception. However, evidence against the EMH is growing, and numerous studies have documented return predictability. In fact, despite its relative simplicity, this hypothesis has also generated considerable controversy. After all, the EMH questions the ability of investors to consistently detect mispriced securities. For these reasons, scholars have recently been studying the calendar anomalies that are one of the characteristics of financial markets, and these anomalies are found to contradict the EMH. The purpose of this paper is to present a systematic review of the existing literature on calendar anomalies. This critical examination of the relationship between the EMH and calendar anomalies provides new insights for scholars and executives.

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nt. J. Managerial and Financial Accounting, Vol. 7, Nos. 3/4, 2015 285
Copyright © 2015 Inderscience Enterprises Ltd.
The efficient market hypothesis and calendar
anomalies: a literature review
Matteo Rossi
DEMM Department,
University of Sannio,
Via delle Puglie, 82, 82100 Benevento, Italy
Email: mrossi@unisannio.it
Abstract: One of the most important principles used in measuring the market’s
efficiency is the ability of prices to reflect all currently available information.
The Efficient Market Hypothesis (EMH) is the proposition that current stock
prices fully reflect all available information about the value of the firm and that
there is no way to earn excess profits by using this information. The EMH has
received an abundance of attention since its inception. However, evidence
against the EMH is growing, and numerous studies have documented return
predictability. In fact, despite its relative simplicity, this hypothesis has also
generated considerable controversy. After all, the EMH questions the ability of
investors to consistently detect mispriced securities. For these reasons, scholars
have recently been studying the calendar anomalies that are one of the
characteristics of financial markets, and these anomalies are found to contradict
the EMH. The purpose of this paper is to present a systematic review of
the existing literature on calendar anomalies. This critical examination of the
relationship between the EMH and calendar anomalies provides new insights
for scholars and executives.
Keywords: efficient market hypothesis; calendar anomalies; January effect;
turn-of-the-month effect; day-of-the-week effect.
Reference to this paper should be made as follows: Rossi, M. (2015) ‘The
efficient market hypothesis and calendar anomalies: a literature review’,
Int. J. Managerial and Financial Accounting, Vol. 7, Nos. 3/4, pp.285–296.
Biographical notes: Matteo Rossi is an Assistant Professor of Corporate
Finance at the University of Sannio, Italy. He earned a PhD in Corporate
Governance and his prime research interests are corporate finance, financing
innovation, wine marketing and innovation systems. In all of these areas, he
has published, contributed chapters to books, edited books and presented
papers to national and international conferences. He is the Vice President for
International Relations of the EuroMed Research Business Institute (EMRBI).
In 2014, he won the Highly Commended Paper Award of the International
Journal of Organizational Analysis (Emerald) for the paper ‘Mergers and
acquisitions in the hightech industry: a literature review’ and in 2012, he won
the Outstanding Paper Award of the International Journal of Organizational
Analysis (Emerald) for the paper ‘Italian wine firms: strategic branding and
wine performance’.

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1 Introduction
One of the most important criteria used in measuring the market’s efficiency is whether
or not prices reflect all available information (information efficiency). The Efficient
Market Hypothesis (EMH), also known as the Random Walk Theory (Kendall, 1953), is
the proposition that current stock prices fully reflect all available information about the
value of the firm and that when using this information, there is no way to earn higher
profits. The term “efficient market” was introduced in 1965 by Eugene Fama, who
argued that in an efficient market, “on the average, competition will cause the full effects
of new information on intrinsic values to be reflected instantaneously in actual prices”
(Fama, 1965, p.57). Thus, the EMH predicts that at any point in time, market prices
should incorporate and reflect all available information. However, different kinds of
information can influence security values. Consequently, economists define three levels
of market efficiency, which are distinguished by the amount and type of information
reflected in security prices (Brealey et al., 2011): weak form efficiency, semi-strong form
efficiency and strong form efficiency.
The weak form of the Efficient Market Hypothesis asserts that the current stock price
fully incorporates only that information contained in the past history of prices. In this
situation, one should not be able to profit from using information that is available to
everyone. However, many financial analysts attempt to generate profits by studying
exactly what this hypothesis asserts is without value: that is, past stock price series and
trading volume data. This technique is called technical analysis. The empirical evidence
for this form of market efficiency, and therefore against the value of technical analysis, is
quite strong and consistent (Clarke et al., 2001).
The semi-strong form of the efficient market hypothesis suggests that the current
stock price fully incorporates all publicly available information, e.g., past prices, data
reported in a company’s financial statements, earnings, announced merger plans, and
expectations regarding macroeconomic factors. In fact, this public information does not
even have to be of a strictly financial nature. If markets are semi-strong efficient, then
prices will adjust immediately to reflect publicly available information. Like the weak
form of the efficient market hypothesis, the semi-strong form still asserts that one should
not be able to profit using something that everyone knows, as the information is public.
This public information, however, may be relatively difficult to gather and costly to
process. Major newspapers and company-produced publications may not be sufficient
sources. Many scholars have found empirical evidence that is overwhelmingly consistent
with the semi-strong form of the EMH.
The strong form of the efficient market hypothesis states that the current stock price
fully incorporates all existing information, both public and private (including so-called
insider information). The main difference between the semi-strong and strong efficiency
hypotheses is that in the latter case, no one should be able to systematically generate
profits. In the case of strong-form market efficiency, the market anticipates future
developments in an unbiased manner. Therefore, the stock price may have already
incorporated both public and private information and evaluated it in a much more
objective and informative way than even insiders. Not surprisingly, however, empirical
research in finance has found evidence that is inconsistent with the strong form of the
EMH.
The EMH has received an abundance of attention since its inception. However,
evidence against the EMH is mounting, and numerous studies have documented return

The efficient market hypothesis and calendar anomalies 287
predictability (Rossi, 2016). In fact, despite its relative simplicity, this hypothesis has
also generated considerable controversy. After all, the EMH questions the ability of
investors to consistently detect mispriced securities. For these reasons, scholars have
recently been studying calendar anomalies that are characteristic of the financial markets
and finding that they contradict the EMH (Nasir et al., 2016).
On the basis of the above, this paper is organised as follows. The first section
presents a literature review on calendar anomalies. I include a separate subsection for
each calendar anomaly. In the final section, I conclude the paper with some important
considerations for scholars and executives.
2 Calendar anomalies: a literature review
During the 1970s, many empirical tests were conducted to demonstrate the informational
efficiency of stock markets. In contrast, during the 1980s, 1990s, and over the last 15
years, much has been written with the goal of demonstrating the markets’ inefficiency by
identifying orderly variations in stock prices related to the calendar of the civil year.
The aim of this section is to describe calendar anomalies across the globe among
markets with different micro-structures. A calendar effect is an economic consequence or
market anomaly related to the calendar (Nasir et al., 2016). The calendar time hypothesis
states that the market behaves differently at different hours of the day, on different days
of the week, and at various times of the month and year. Various studies have been
conducted on this topic. Below is a chronological literature review of principal studies on
calendar effects (Table 1).
Table 1 Principal studies on calendar effects
Calendar anomalies Main studies
January effect
Wachtel, 1942; Rozeff and Kinney, 1976; Banz, 1981; Reinganum,
1981; Blume and Stambaugh, 1983; Brown et al., 1983; Gultekin and
Gultekin, 1983; Keim, 1983; Roll, 1983; Barone, 1990; Fama, 1991;
Agrawal and Tondon, 1994; Athanassakos and Schnabel, 1994; Raj
and Thurston, 1994; Alagidede, 2008; Mylonakis and Tserkezos, 2008
Day-of-the-week
effect
Osborne, 1962; Cross, 1973; French, 1980; Lakonishok and Levi,
1982; Gibbons and Hess, 1981; Keim and Stambaugh, 1984; Rogalski,
1984; Jaffe and Westerfield, 1985; Jacobs and Levy, 1988; Jaffe et al.,
1989; Barone, 1990; Lakonishok and Maberly, 1990; Balaban, 1995;
Hawawini and Keim, 1995; Arsad and Coutts, 1996; Wang et al.,
1997; Berument and Kyimaz, 2003; Steeley, 2001; Bildik, 2004; Chan
et al., 2004; Chinko and Avci, 2009
Turn-of-the-month
effect
Ariel, 1987; Lakonishok and Smidt, 1988; Pettengill and Jordan, 1988;
Barone, 1990; Cadsby and Ratner, 1992; Agrawal and Tandon, 1994;
Hensel and Ziemba, 1996; Arsad and Coutts, 1996; van der Sar, 2003;
Bildik, 2004; McConnell and Xu, 2008;
Other (minor) effects
Lakonishok and Smidt, 1988; Barone, 1990; Cadsby and Ratner, 1992;
Kim and Park, 1994; Meneu and Pardo, 2004; Cao et al., 2009; Marrett
and Worthington, 2009; Dodd and Gakhovic, 2011; Almudhaf, 2012;
Barmak, 2012; Ehrmann and Jansen, 2012; Nasir et al., 2016
Source: Author’s calculation

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2.1 January effect
The January effect is known to be the most important calendar anomaly: “as goes
January, so goes the year” is a popular rule in the stock market. This effect is also known
as the “turn of the year” effect.
Though Wachtel (1942) provided the earliest evidence of abnormal stock returns in
January for US stock markets, Rozeff and Kinney (1976) were the first to formally report
this effect in the US equity market. They found “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 are statistically significant differences in mean returns
among months due primarily to large January returns” (Rozeff and Kinney 1976, p.379).
Similar results were reported by Gultekin and Gultekin (1983), Barone (1990), and
Agrawal and Tondon (1994). They showed abnormal positive returns in major
industrialised countries: “Evidence is provided that there are strong seasonalities in the
stock market return distributions… The seasonality, when it exists, appears 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” (Gultekin and Gultekin, 1983, p.469). Similarly, Agrawal and Tondon (1994)
reported monthly anomalies in 18 countries other than the USA: “the January returns are
large in most countries and a significant monthly seasonal effect exists in ten countries”
(Agrawal and Tondon, 1994, p.83).
Barone (1990) conducted a study based on the Milan Stock Exchange’s index with
reference to the period 2 January 1975 to 22 August 1989. “The results obtained on the
basis of the MIB index for the whole period considered show that the Italian stock market
also has a pronounced seasonal pattern with the daily changes in stock prices during
January account equal on average to 0.33 per cent and significantly different from zero at
a level of confidence of less than 0.001” (Barone, 1990, p.495).
Other studies (Banz, 1981; Reinganum, 1981; Blume and Stambaugh, 1983; Roll,
1983; Keim, 1983) investigated the interaction of the month-of-the-year and size effects
and found a significant negative relationship between stock returns and the firm size
(measured by the total market value of outstanding equity). Additionally, Fama (1991)
has analysed the behaviour of the S&P 500 over a period of 40 years (1941–1981).
During this period, small stocks averaged a return of 8.06% in January, which was
substantially higher than returns during other months of the year.
Brown et al. (1983) examined the month-of-the year effect in Australia, where the tax
year ends on 30 June. For Australian stocks, they found seasonal effects in December–
January and July–August with the largest effects in January and July. On the other hand,
Raj and Thurston (1994) found that the January and April (taxation period) effects were
not statistically significant in the New Zealand Stock Market. Athanassakos and
Schnabel (1994) also observe that superior January performance comes from tax loss
trading at the end of December. The risk to small stocks is not constant throughout the
year and tends to be particularly high at the beginning of the year.
Mylonakis and Tserkezos (2008) examined the January effect in the Athens Stock
Exchange (ASE). Their research analyses the period of January 1985 to December 2001
and confirms that the “January effect” exists in the ASE general index. The primary
analysis of this study shows that mean returns during January are considerably higher
than other months throughout the 17 years of the study period. Alagidede (2008) studied
seven African countries and found that the month-of-the-year effect is prevalent in

The efficient market hypothesis and calendar anomalies 289
African stock returns. Particularly, January returns were positive and significant for
Egypt, Nigeria and Zimbabwe. February returns were higher for Kenya, Morocco and
South Africa but Tunisia had no monthly seasonality. Norvaisiene et al. (2015) study
seasonality in the Baltic Stock Market: “the daily log return indexes of Nasdaq OMX
Tallinn, Nasdaq OMX Riga, and Nasdaq OMX Vilnius in the Baltic stock exchange were
analysed for the period of 2003–2014” (Norvaisiene et al., 2015, p.468). One of the main
results of this study is the existence of the Month effect. In particular, results show “that
return in the Estonian stock market is statistically significantly higher in January if
compared to the return of other months” (Norvaisiene et al., 2015, p.471).
2.2 Day-of-the-week effect
The day-of-the-week effect was first documented by Osborne (1962) in the US stock
market and was subsequently analysed by many scholars (Cross, 1973; French, 1980;
Gibbons and Hess, 1981; Lakonishok and Levi, 1982; Keim and Stambaugh, 1984;
Rogalski, 1984; Jaffe and Westerfield, 1985; Jacobs and Levy, 1988; Jaffe et al., 1989;
Barone, 1990; Wang et al., 1997). Typically in the USA, low mean returns are observed
on Monday in comparison to the other days of week. Mean returns on Friday are
observed to be positive and abnormally higher than the mean returns on other days of the
week.
Two important studies were conducted by French (1980) and Jaffe et al. (1989). They
reported that average returns are significantly negative on Mondays and that these are
significantly lower than the average returns for other weekdays in the USA. On the other
hand, average returns on Friday are found to be positive and higher than average returns
for the rest of the week.
Jain and Joh (1988) reported that liquidity in the stock market is lower on Monday
than on other days of the week; they found that the total volume of the New York Stock
Exchange (NYSE) on Monday is approximately 90% of its average trading volume for
Tuesday through Friday.
Lakonishok and Maberly (1990) show that individual investors tend to increase their
trading activity on Monday. Thus, the main reason for the Monday effect could be related
to the trading pattern of individual investors. Hawawini and Keim (1995) explain the
Calendar Time Hypothesis as suggesting that Monday’s mean is different than the mean
returns on all other days, indicating that the return generating process is a continuous
activity. Additionally, Wang et al. (1997) observed that in the US market, the well-
known Monday effect occurs primarily in the last two weeks of each month. Wang used
a long time series (1962–1993) for his empirical research.
The week effect was studied by Kiymaz and Berument (2003). They look at the
presence of the day-of-the-week effect on the stock market using the S&P 500 index
from January 1972 to October 1997. Their study shows that the day-of-the-week effect
exists in both volatility and in returns. Their findings suggest that the highest and lowest
returns are observed on Wednesdays and Mondays, but the highest and lowest volatility
are present on Wednesdays and Fridays, respectively.
Chinko and Avci (2009) analyse the existence of ‘day-of-the-week effect’ for the
Istanbul Stock Exchange (ISE) based on the ISE-100 index during the time period 1995–
2008. Their analysis shows negative Monday returns and positive Thursday and Friday
returns at a 5% significance level. However, Tuesday and Wednesday have no significant

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return patterns. Their final analysis shows that regardless of market capitalisation, all
portfolios showed significant negative Monday returns, no results for Tuesday and
Wednesday, but positive Thursday and Friday returns.
Various explanations have been suggested by researchers (Arsad and Coutts, 1996;
Steeley, 2001; Chan et al., 2004). Chan et al. (2004) observed that the Monday effect is
stronger in stocks with low institutional holdings. Arsad and Coutts (1996) and Steeley
(2001) found that the general trend of the market is an important variable in determining
the existence of the day-of-the-week effect. Bildik (2004) asserted that low and negative
Monday effects disappear when returns of the final trading day of the previous week are
positive.
Some countries show variations in the day-of-the-week effect. In fact, Jaffe and
Westerfield (1985) and Balaban (1995) report different results. In particular, Jaffe and
Westerfield (1985) examine the daily stock market returns for four foreign countries.
They find that “lowest mean returns for the Japanese and Australian stock markets occur
on Tuesday” (Jaffe and Westerfield, 1985, p.433). A negative Tuesday effect was mostly
observed in European countries. In contrast to Chinko and Avci (2009), Balaban (1995)
also reports these results in his analysis of the ISE.
2.3 Turn-of-the-month effect
The turn-of-the-month (TOM) effect was first identified by Ariel in 1987 for the US
equity market. He observed that mean returns are higher at the end of one month and at
the beginning of the next month. His study covered a period of nineteen years (1963–
1981), focusing on the last day of one month and the first three days of the following
month (–1, +3). He divided each month into two parts, with the first part commencing on
the final day of the previous month. Next, he compared the cumulative returns for both
parts using value-weighted and equally weighted indexes. All returns occurred during the
first part of the month for the period.
van der Sar (2003) explains the Turn-of-the-Month effect as producing exceptionally
high returns that are realised repeatedly only on 5 or fewer back-to-back trading days
around the turn of the month. In other words, this effect is a temporary rise in stock
prices that normally occurs during the last few days of each month.
Lakonishok and Smidt (1988) extended Ariel’s investigation. They used a 90-year
sample period (1897–1986) and a narrow study window of front-trading days comprising
the final trading day of one month and first four trading days of the next month (–1, +4).
They “find 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”
(Lakonishok and Smidt, 1988, p.403).
Their finding has been supported by many subsequent studies (Hensel and Ziemba,
1996; Agrawal and Tandon, 1994). Hensel and Ziemba (1996) created a five-day event
window by adding the final two days of one month and the first three days of the next
month (–2, +3) in the US stock market to show the existence of the TOM effect. They
analysed data from 1928 to 1993 and found that returns on –1, +2 and +3 days were
significantly higher. Pettengill and Jordan (1988) and Agrawal and Tandon (1994)
showed that cumulative returns during the short window of the turn-of-the-month could
constitute as much as 55–70% of monthly returns.
“In the Italian market there is a clear difference between the first and second halves
of the calendar month. On the basis of the sample obtained by excluding the observations

The efficient market hypothesis and calendar anomalies 291
corresponding to the first day of each monthly account and to the trading days after a
public holiday, stock prices are found to fall in the first part of the calendar month (in
correspondence with the end of the monthly account) and then rise in the second (in
correspondence with the early part of the next account)” (Barone, 1990, p.491).
Some relatively recent studies have confirmed that this effect is still present in the US
equity market (McConnell and Xu, 2008). McConnell and Xu (2008) examined the turn-
of-the-month effect in US equities. Using CRSP daily returns, they found that the turn-of
the-month effect persists throughout the period 1987–2005: in essence, over this 19-year
period (and over the 109-year period of 1897–2005) all excess market returns occurred
during the four-day turn-of-the-month intervals. They “further find that the turn-of-the-
month effect is not confined to small or low-priced stocks; it is not confined to the
December-January turn-of-the-month; it is not confined to calendar-quarter-ends; it is not
confined to the U.S.; and it is not due to market risk as traditionally measured: the
standard deviation of returns at the turn-of-the-month is no higher than during other
days” (McConnell and Xu, 2008, p.49). Thus, this anomaly seems to have a global
presence. Both Cadsby and Ratner (1992) and Jaffe and Westerfield (1989) observed in
their worldwide market studies that turn-of-the-month was significant at the 1% level in
Canada, Switzerland, and West Germany and at the 5% level in the UK and Australia.
They did not find significant results in Japan, Hong Kong, Italy and France.
Other studies focus their attention on particular stock exchanges. Bildik (2004)
observed statistically and economically large and positive returns in the first and last
weeks of the month for the Istanbul Stock Exchange. Arsad and Coutts (1997) found
significant positive differences between turn-of-the-month and non-turn-of-the-month
periods in their empirical study of the London stock market over a 60-year period.
This analysis of the turn-of-the-month effect suggest that this anomaly could be
exploited to construct certain profitable investment strategies, making it surprising that
this anomaly still remains relatively unexplored. Certainly its persistence poses a major
challenge to the theory of efficient markets.
2.4 Other calendar effects
Apart from the calendar anomalies discussed above, other scholars have analysed
additional calendar effects. Another seasonal variation that has been studied is the
holiday effect. This can be defined as a fully predictable stock exchange closure because
of a public holiday. This affects the performance of daily stock returns. Unlike monthly
or weekly effects, the holiday effect may vary from market to market in timing, duration
and frequency. This effect depends on the public holiday: for example, the USA has 8
holidays, compared to four in China. The holiday effect is known to produce unusual
results; specifically, it produces either high or low returns. Recent observations of the
holiday effect suggest that on pre-holiday trading days, mean returns are positive and
high in comparison with non-holiday trading days. Post-holiday returns are normally
significantly positive, however, they are not significantly different from normal trading
days. Recent studies focusing on developed financial markets confirm these results
(Barone, 1990). Lakonishok and Smidt (1988) document significant returns on days
before US public holidays. These pre-holiday returns are 23 times higher than returns on
normal days. These results are confirmed by several other studies (Pettengill, 1989; Ariel,
1990).

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Cadsby and Ratner (1992) analyse the holiday effect in ten different stock market
indices. Their study shows a pre-holiday effect in Australia, Canada, and Hong Kong but
not in European markets. Kim and Park (1994) note a pre-holiday effect in all three
major stock markets in the USA (NYSE, AMEX and NASDAQ), as well as in Japan and
the UK. They indicate that the persistence of a pre-holiday effect across countries
suggests that it is not determined by institutional arrangements unique to the stock market
of a particular country. Significant pre-holiday returns also exist in other markets. For
example, Meneu and Pardo (2004) find significant pre-holiday effects in Spain; Cao et al.
(2009) find significant pre-holiday effects in the New Zealand market; and Marrett and
Worthington (2009) document pre-holiday effects in Australia. Dodd and Gakhovich
(2011) confirm these results and “In addition to previous literature, we also document
abnormal post-holiday returns” (p.29).
This review of existing literature suggests that the attention paid to religious calendar
effects has been low compared to other effects mentioned earlier. However, some studies
do focus on these effects (Almudhaf, 2012; Barmak, 2012; Nasir et al., 2016). Almudhaf
(2012) investigates the Islamic calendar seasonal effect in the stock returns of 12
countries where Muslims make up the majority of the population. The results showed
evidence of Islamic calendar seasonal effects in all 12 countries in the sample. During the
month of Ramadan, Jordan, Kuwait, Pakistan and Turkey show a positive effect, which
contradicts the predictions of some investors. Surprisingly, for all 12 countries, there is a
positive coefficient during the month of Ramadan. Based on these results, there is an
average Ramadan return that exceeds the average returns for other months of the Islamic
calendar in the four countries mentioned above. Their final results showed evidence of
seasonality in stock returns based on the Islamic calendar. Similar results were
highlighted by Barmak (2012). He contends that this holy month in the Islamic calendar
affects the stock market, especially if the population in that country is dominated by
Muslims.
In particular, Nasir et al. (2016) analyse the Ramadan Effect in Pakistan. Their
analysis draws two conclusions: the first shows that Ramadan has a minor positive
impact on the stock market and the second conclusion states that the stock market is less
volatile during the holy month of Ramadan.
The behaviour of the stock market may also be affected by certain pre-determined
events. A rather strange and new phenomenon took place during the 2010 FIFA World
Cup in South Africa. Ehrmann and Jansen (2012) have analysed this situation, noting that
many matches took place during stock market trading hours. The pair examined 15
international stock exchanges using minute-by-minute trading data. Their three key
findings were that first, when a country’s national team was playing, trading in that
country’s market dropped by 45% alongside a fall of 55% in volume. Second, match
events influenced market activity; for example, there was an additional drop of 5% in
trading activity when a team scored a goal. The third and final finding was that the co-
movement of stock prices between global and national stock market returns dropped by
over 20% during matches. This clearly indicates that the stock market’s attention was on
the football pitches rather than the trading pit, leading to an alteration in the price
formation process.

The efficient market hypothesis and calendar anomalies 293
3 Conclusions
Although the efficient market hypothesis was widely accepted by academic financial
economists, over the last few years the evidence against the EMH has grown. The
preceding discussion began with the January effect, followed by day-of-the-week,
monthly, and weekend effects and finally the Islamic calendar effect.
These effects have attracted the attention of a large number of scholars, but results
have often been mixed. The EMH, particularly in its semi-strong and strong forms, is
often strongly criticised.
The fragmentation of the literature on calendar anomalies has prevented the
formulation of a general theory on this topic. In fact, this literature review shows that
there is no single, unified point of view on the relationship of the EMH to calendar
effects. This brief literature review has shown that, although progress has been made in
our understanding of this topic, there are still many opportunities to improve our
understanding of the principal motivations behind calendar effects.
The fragmentation of the literature on this topic and resulting lack of consensus
underscores the complexity of calendar effects. The main question is: is it possible that
all financial markets are characterised by calendar effects? A priori, this is impossible to
answer. If anything, the strong form of the EMH is an academic consideration. The
heterogeneity of scholarship in this field is also related to the difficulty of defining the
subject of investigation: calendar effects can be studied as a specific topic or as evidence
of a non-efficient market hypothesis. However, these considerations can only partially
explain the excessive fragmentation of the literature. Naturally, many fields are
characterised by fragmented literature, but in this field such fragmentation provides a
clear opportunity for progress. To take advantage of this opportunity, however, it is
necessary to compare the various themes, methods of investigation, and data used in
studying this topic. Only in this way will it be possible to explore this unresolved topic in
sufficient depth.
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