This document discusses calendar anomalies in stock markets, including the January effect, time of month effect, turn of month effect, day of week effect, and holiday effect. It analyzes evidence of these anomalies in both developed and emerging markets. While some strategies could exploit certain anomalies in the short term, the document concludes that consistently beating the market using anomalies is difficult. Anomalies vary over time and between markets. It is risky to base investment strategies solely on calendar effects due to the challenges of predicting market movements.

1. CAPITAL MARKET INVESTMENTS AND FINANCE
CALENDAR ANOMALIES IN STOCK MARKETS
Done by:
 Aanchal Saxena – 12032303
 Charmi Dutia – 12032275
 Avanti Mukul– 12032274
 Asiya Khatoon – 12032272
Word Count - 2756

2. 1
Table of Contents:
1. Introduction
2. Calendar Anomalies
 January Effect
 Time of the Month Effect
 Turn of the Month Effect
 Day of the Week Effect
 Holiday Effect
3. InvestmentStrategies
4. Conclusion
5. References
6. Appendices

3. 2
Introduction:
Stock Market Efficiency can be described as the price at which the selling and buying of a stock
would give a reasonable or fair return in relation to the risk which is associated with it, after
taking into account the transaction costs.
Efficient Market Hypothesis proposes that the markets are rational and their prices full reflect
all available information. Also, due to well-timed activities of the investors, the stock rapidly
adjusts to and reflects the new information that is now available in the market. Thus, investors
cannot beat the market by making abnormal returns (Fama, 1970). The inside information is
always withheld from the investors as it could result is better returns than the market and
create market inefficiency. It is believed that when one investor who finds a profit making
opportunity and shares it with the other investors and traders, the advantage is rapidly
exhausted from the market and no matter how much information is provided it can never be
sufficient enough to forecast the stock market returns in the long run.
For years, technical analysts have been trying to use numerous techniques to forecast the stock
market movements. One such famous technique of prediction was the hedge fund Long-Term
Capital Management (LTCM) that lost $5 billion in the year 1998 when the Russian stock market
fell due to their currency devaluation. This incident presented the confirmation that the stock
markets are highly efficient (Prabhu, 2001) and extremely difficult to beat using predictions.
The three main forms of market Efficiency are:
The Weak Form: All the past information like previous prices and returns are mirrored in the
current prices of stocks (Bodie et al, 2007).This form coincides with the random walk hypothesis
where stock prices move randomly and are independent of each other. Consequently, no one
can forecast the future values and beat the market by earning abnormal returns other than
through insider trading.
The Semi-strong Form: Current stock prices reflect all past and publicly available information.
Even here no one can earn abnormal profits other than through insider trading.
The Strong Form: All the past, public and private information is reflected in the current stock
prices. No one can beat the market in any way, not even through insider trading.
However, it can see be seen in many stock exchanges around the world are not following the
rules Efficient market hypothesis. The working of these markets deviates from the rules of
EMH. These deviations are termed as anomalies. They can be defined as the abnormal change
in the smooth movement of the stock market. A number of different researchers have tried to
understand and analyze anomalies, exhibiting them in their evidences. However anomalies
continue to remain debatable.

4. 3
In this assignment we are going to discuss the overwhelming evidences of persistent stock
market calendar anomalies for both developed and emerging markets and whether it is
possible to harness these anomalies as a part of an investment strategy.
Anomaly 1: THE JANUARY EFFECT
According to (Haugen and Jorion, 1996) the January effect is regarded as the best known example
of an abnormal behavior in the stock markets throughout the world. Stocks in overall and small
stocks in specific tend to have historically shown to generate abnormally high returns in the
month of January. The effect is usually due to small stocks recovering subsequent to the year-
end tax selling. This effect can also be observed in Australia and Great Britain which do not
observe 31st December as tax year-end.
To investigate this anomaly in emerging markets we have used the mean model shown in table
1 by Georgantopoulos et Al. (2011). We can notice that the there is no evidence of January
effect in the Balkan countries (Greece, Romania, Bulgaria, Croatia, Turkey). Alternatively, the
results in variance shown in table 2 are different since the effect can be noticed in Croatia,
Turkey and Greece at 10%, 1% and 5% levels respectively. The highest significant variance in
January can be seen in Greece.
Table 3 shows the mean returns in percentage per month for 16 developed countries. It is
found that the January effect is present in 7 out of the 16 countries analyzed In this study by
Folliott T.(2006). The highest effect was noticed in Belgium, Denmark, France, Italy, Spain,
Sweden, and UK. Also one of the countries (UK) does have 31st December as tax year-end. In
USA the MSCI data doesn’t show a January effect but the CRSP data does, thereby proving a
small firm effect in the USA. The bottom line is that even now, January seems to be an ideal
month to invest in the stock markets.
To make gains using this anomaly, investors should buy small caps or value stocks towards the
end of December.
Anomaly 2: THE TIME OF THE MONTH EFFECT
Time of the month effect signifies the return of stock market during the first-third of the month,
second-third of the month and third-third of the month. It analyses the insignificance in the
returns during the time of the month (Kohers and Patel, 1999).
Georgantopoulos et Al. (2011) tested the time of the month effect in emerging stock markets
like Romania, Bulgaria, Croatia and Turkey and Greece during the period 2000-08. This study
pointed out that the time of the month effect is greater in Greece and weaker in Turkey. The
both markets show similar features where first third of the month is significant but in Greece,

5. 4
the mean returns are greater than the last third of the month however returns are lower in
Turkey than the last third of the month (table 4).
The table 5 displays the effect in terms of risk. These results and the mean results go hand in
hand. The existence of this effect is to be highly felt in Greece and Turkey with confidence level
of 99%. This anomaly also exists in Croatia as the returns from the variance model indicate that
volatility of second third of the month is larger than last third of the month.
Below is the study done by Georgantopoulos et al. (2012) which considered developed stock
markets indices of Germany, France, Austria, Portugal and Greece.
The table 6 shows the summary statistics of the developed market indices in the period of
2000-08. Austrian index gains the highest average daily (0.051368) and the lowest appears for
the Greece Index (-0.000278). As noticed from the table, the Germany Index records the largest
unconditional volatility (1.546396) and greatest range for the returns. Generally, descriptive
statistics are regarded as leptokurtic and skewed because the returns are not normally
distributed.
The table 7 indicates that mean result of the Austrian index in the second-third of the month is
significantly lower than the last-third of the month. Also, this anomaly is similar in Portugal and
Greece where in first third of the month is significantly higher than last third of the month with
99% confidence level.
Evidences in these are that the result for Austria is that variance is seen less in the second third
of the month than the last third of the month. Also, since the first third of the month has higher
volatility than the last third of the month, presence of this anomaly can be felt in Portugal while
it is opposite in Greece (table 8).
Anomaly 3: TURN OF THE MONTH EFFECT
One of the most famous seasonal anomaly discovered by researchers was the ‘TURN-OF-THE-
MONTH’ effect. According to Ariel (1987), on an average, daily returns are abnormally higher
(positively) on the last four and first three days of each month compared to the rest of the
month. These unusually high positive returns at the turn of the month have been proposed to
have arisen from clustering of salaries and pensions being paid timely (Odgen, 1990), increase
in the liquidity (Pettengill and Jordan, 1988) and also from clustering of the earnings
announcements released and the tendency of firms to announce any good news in the first half
of the month (penman, 1987) the study conducted by (Hensel and Ziemba, 1996)implied that the
investors making regular purchases may perhaps benefit by planning to make those purchases
prior to the turn of the month. This turn-of-the-month effect in the American equity returns
was also recognized by Lakonishok and Smidt (1988) using the Dow Jones Industrial Average
(DJIA) from 1897-1986.

6. 5
From the table 4 which was a study conducted by (Mcconnell and Xu, 2008, pp. 49--64) on 34 Non -
US countries it can be see that the turn of the month effect is present in almost every country
except for Colombia. In all countries except Colombia, the daily average turn of the month
returns are higher compared to the returns over other days. On using the t-statistic standard it
is found that for 28 countries’ markets the t-stat is greater than 1.95.For Taiwan, the mean
TOM returns is 0.12% though the mean returns overall on other days is 0.00%, but then again
the t-stat for the difference is 1.59. The countries which do not show a meaning-full TOM effect
are Colombia, Italy, Argentina and Malaysia.
To make gains using this Anomaly investors with a monthly plan should ponder upon making
regular contributions at the middle of the month rather than the start.
Anomaly 4: DAY OF THE WEEK EFFECT
Based on the efficient market theory, day of the week effect signifies that an average market
daily return is not the same throughout the other days of the week. This effect is also known as
the Weekend effect or Monday effect. In developed markets like US, England and Canada,
empirical studies have shown that usually market returns are more on Fridays while there are
negative returns to common stocks on Monday while it may be opposite in emerging markets
(Smirlock & Starks, 1986).
Ally et al. (2004) studied the day of the week effect in the Egyptian market which is an emerging
stock market.
Research of daily stock market returns was done for the period April 26, 1998 to June 6, 2001
where equities in the emerging Egyptian market were traded on four day per week basis and
showing that returns are significantly positive statistically significant (5% level) on Mondays
than the rest of the days in the week. (Table 10)
Table 11 signifies that return on Monday are significantly positive but are not significantly
positive from the rest of the days of the week. Hence, empirical results show no evidence of
significant Monday effect in the Egyptian Stock Market. Standard deviation of Monday returns
i.e. 1.0606 is greater than the standard deviations of rest of the week returns i.e. 0.6426 and
difference in the difference-of-variance test shows that there is a significant difference. The
consistency of positive Monday returns is with the fact that returns on Monday for CMA Index
are more volatile than returns on the days of the rest of the week. Thus, Returns on the
Egyptian Stock Market are consistent with the weak form of Efficient Market hypothesis.
Table 12 and 13 of the study by Georgantopaulos et al. (2011) displays the return on indices of
various developed stock markets of Europe. This includes Germany (DAX Index), France (CAC 40
Index), Austria (ATX Index), Portugal (BVL Index) and Greece (ASE Index).

7. 6
The estimates of the day of the week effects, are rejected Germany, France, Austria and
Portugal. Whereas, there are great evidences of this effect in Greece because estimate
coefficient for Monday is negative and statistically significant (-0.0042) and is positive with high
level of confidence for Friday (0.0034) (table 12).
Hence, this study shows the presence of this anomaly in Portugal and in Greece where variance
is high on Monday than Thursday and Friday (table 13).
Anomaly 5: HOLIDAY EFFECT
Consistencyin the pattern of the Weekend effect suggests thatsuch patterns should be prevalent
around any gap in the trading business. Pragmatic Studies of developed nations such as the US
and other countries have shown to report high rates of return just prior to the holiday season
(Lakonishok and Smidt, 1988) while others argue that the highest rates of return are obtained on
the last trading day of December. (Roll,1983). Researchers have provided that the existence of
the holiday effect is due to the relationship it has with other calendar anomalies.(Menue and
Pardo,2003) The second explanation of existence is due to the relationship between the pre-
holiday effect and the small firm effect (Pentengill,1989) The last effect is based on a set of
different and systematic trading patterns (Keim, 1983)(refer note 8).
From figure 1 the inter-holiday returns are higher and significantly positive in most countries.
While we do not observe the returns preceding all the holidays, the average daily returns in the
second half of December do provide some evidence on the study conducted by Ariel, Lakonishok
and Smidt.
This year-end effect in the developed markets of USA is occasionally accredited to window
dressing and inventory alterations by money managers (Lakonishok and Smidt, 1988) While this
explanation is consistent with the results in mature capital markets, it is tricky to offer in
emerging capital markets such as Brazil, Mexico and New Zealand. In these countries, financial
establishments such as mutual funds and private pension funds, which are probable to have
superior incentives for window-dressing, were not yet main players in the market arena in the
seventies and the early eighties. In addition, their capital markets have been exceedingly
regulated. As a result, the window-dressing argument seems to be an inadequate account of
the year-end effect, at least in some nations.
Investment strategy
Investment strategy, which is a process used to make investments in the stock markets, mainly
focuses on return-risk trade-off for possible investors. Some investors tend to be risk takers
while others are risk avoiders.

8. 7
According to EMH theory all stock prices fully reflect all the information that in in the market
which thus makes it tough to harness anomalies as a part of an investment strategy.
The January effect is said to predict the stock market happenings in the short run but not
successful in the long run. According to the study conducted by (Lounsbury, 2010) for the time
periods of 1929-2009, 1969-2009 and 1989-2009 the January effect shows consistency with
only 34% showing low over the 81 years of study. This 34% shows that even though investors
can predict the markets, there are times when events such as a war of recession could make
the stock market act in a negative way.
Investment in Time of the Month Strategy might not be a great option since the returns vary for
different countries in the same time periods.
Investment in Turn of the month may yield high profits but this anomaly is not fully present in a
few countries. Also the returns vary according to the different markets the securities are being
traded in.
Investment Strategy for day of the week would be to buy on Mondays and sell it off on Fridays.
Yet again, the cost of trading might minimize the net realizable value of returns.
Going by the regularity phenomenon, stock returns have a tendency to be higher before and
after holidays. Making holiday effect a great opportunity for investors. For making post holidays
investment decisions investors need to assemble information of things occurring during the
holiday to predict the outcomes of the market when it starts again. However, harnessing this
anomaly as a part of an investment strategy is difficult because returns vary from country to
country in different magnitudes.
Conclusion:
Overall, using market anomalies to form an investment strategy appears to be a risky affair. The
research shows that the anomalies fluctuate between time to time and market to market.
Moreover, the indicators of a bad news are hard to forecast. To overcome this, an investor
needs to respond to this news quicker than the rest of traders which a difficult task.
Also, each anomaly comes with a certain risk, thus, it is for the investor to decide if he is willing
to take the risk in order to make the gains.
Long term investments in these anomalies are not possible due to the fact that, if once, the
strategy of a successful trader is leaked, it will be wiped off from the market in no time. The
investor keeping the strategy a secret could also be charged guilty of having insiders’
information.
Thus no one can beat the market consistently and therefore, harnessing market anomalies as
part of an investment strategy is not likely to produce better returns than the market.

9. 8
References:
 Ariel,R.(1987). Monthlyeffectsinstockreturns,Journal of Financial Economics,18,161- 174.
 Agrawal,A.,Tandon,K.1994. Anomaliesorillusions?Evidence fromstockmarketsineighteen
countries. Journalof internationalMoney and Finance,13 (1),pp.83--106. Availableat:
http://bama.ua.edu/~aagrawal/jimf94.pdf(Accessed10th
march 2014)
 Bodie,Z.A.Kane.A.J.Marcus.2007. Essentialsof investments,6thedition,McGraw- Hill /Irwin
 F.fama,E. (1970). "EfficientCapital Markets:A Review of TheoryandEmpirical Work."Journal of
Finance 2, 383-417
 Georgantopoulos,A.Getal. 2011. CALENDARANOMALIESIN EMERGING BALKAN EQUITY
MARKETS. InternationalEconomicsand FinanceJournal,6.
 Georgantopoulos,A.G.andTsamis,A.D. 2012. CalendarAnomaliesinDevelopedEUStock
Markets. IJE, 6 (1),pp.1--15.
 Haugen,R. A.and Jorion,P.1996. The January Effect- Stillthere after all these Yeats.[e-book]
Financial AnalystsJournal.Available through:Jstor.org
http://www.jstor.org/discover/10.2307/4479893?uid=3738784&uid=2&uid=4&sid=2110379853
4897 [Accessed:7march 2014].
 Hensel,C.R.and Ziemba,W.T. 1996. Investmentresultsfromexploitingturn-of-the-month
effects. TheJournalof Portfolio Management,22(3),pp. 17—23
 Keim,D.B.1983. “Size-RelatedAnomaliesandStockReturnSeasonality:Further
Empirical Evidence.”Journalof FinancialEconomics 12 (June):13-32.
 Lakonishok,J.,&Smidt,S.(1988). Are seasonal anomaliesreal?A ninety-yearperspective,
Reviewof Financial Studies,1,403-425.
 Latif,M., Arshad,S.,Fatima,M. andFarooq, S.2012. Market efficiency,marketanomalies,
causes,evidences,and some behavioral aspectsof marketanomalies. Research Journalof
Financeand Accounting,2 (9-10),pp. 1--13.
 Lounsbury,J.2010. DataSaysDon'tIgnorethe January Effect.[online] Available at:
http://www.thestreet.com/story/10675232/1/data-says-dont-ignore-the-january-effect.html
(Accessed:20th2014)
 Meneu.V.and A.Pardo.2003. “Pre-holidayEffect,Large TradesandSmall
InvestorBehavior.”Journalof EmpiricalFinance.
 Mcconnell,J.J. andXu, W. 2008. EquityReturnsat the Turn of the Month. FinancialAnalysts
Journal,pp.49--64.
 Penman,S.(1987). The distributionof earningsnewsovertime andseasonalitiesinaggregate
stock returns,Journal of Financial Economics,18,199-228.
 Pettengill,G.,&Jordan,B. (1988). A comprehensiveexaminationof volumeeffectsand
seasonalityof dailysecurityreturns,Journalof Financial Research,11,57-70
 Pettengill,G.N.1989. “HolidayClosingsandSecurityReturns.” Journalof
FinancialResearch 12: 57-67
 Prabhu,S.2001 LongtermCapital Management:The dangersof Leverage (pdf) availableat
http://econ.duke.edu/uploads/assets/dje/2001/prabhu.pdf (Accessed:5th
march 2014)
 Odgen,J.(1990). Turn-of-the-monthevaluationsof liquidprofitsandstockreturns:A common
explanationforthe monthlyandJanuaryeffects,Journal of Finance,45, 1259-1272

10. 9
 Roll,R.,‘VasistDas? The Turn- of- the-YearEffectandthe ReturnPremiaof Small Firms’ Journal
of Portfolio Management,Winter1983, 9: 18-28
 Folliott,T.2006. The January Effect- A globalperspective.[e-book] [Accessed:20thmarch Apr
2014].
Appendices:
1. Table 1: Evidence of the January Effect inthe Emerging EconomiesinMean Terms
2. Table 2: Evidence of the January Effect inEmerging Economiesin VolatilityTerms

11. 10
3. Table 3: evidence ofJanuary effectinDevelopedNations
Note1 for table3: the stock marketsusedinthisstudyare monthlyreturnsfrommarket
capitalization(value) weightedMSCIStandardindex seriesfor16 countries. Andthe CRSPequal-
weightedindex forUSstockmarketsfrom January1970 to December2006.

12. 11
4. Table 4: Evidence ofTime of the Month Effect inMean Terms for Developingcountries
5. Table 5: Evidence of Time ofthe Month Effectin VolatilityTerms for Developingcountries
6. Table 6: Summary Statistics for DevelopedCountries
Note2 for table 6: Amongthese,GermanyFrance and Austriaare the three more developed
economiesinthe Europe rankinginthe listof twelve richestcountriesinthe worldintermsof GDP.
Conversely,Portugal andGreece are recentlydevelopedcountriesshowingtheirimpressive

13. 12
macroeconomictransformationsandattainingsignificantgrowthratesinthe periodof 2000-08
excludingholidays
7. Table 7: Time of the MonthEvidence in Mean Termsfor DevelopedCountries
Note 3 for table 7: showsthe time of the montheffectduringthe period2000-08 displayingthe results
inmean
βo isfirstthird,β1 secondthirdandβ2 isthe lastthirdof the month.
8. Table 8: Time of the MonthEvidence in VolatilityTerms for DevelopedCountries

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9. Table 9: Evidence ofTurn of the Month Effect in34 Non-USCountries

15. 14
Table 9 Continued:

16. 15
Table 9 Continued:
Note 4 for table 9: Day –1 isthe last tradingdayof the prior month.Days+1, +2, and+3 are the first
three tradingdaysof the month.Day (–1, +3) is the interval beginningwiththe lasttradingdayof the
monthand endingwiththe thirdtradingdayof the followingmonth.“OtherDays”referstotradingDay
–10 throughDay –2 before the endof the monthand tradingDay +4 throughDay +10 after the
beginningof the month.The t-statisticteststhe hypothesisthatthe average returninthe row above the
t-statisticisnotsignificantlydifferentfromzero.“Positive(%)”isthe percentage of observationsin
whichthe dailyreturninthe top row of the panel (orsubpanel) isgreaterthanzero.

17. 16
10. Table 10: OLS results for Evidence ofDay of the WeekEffectin EmergingEgyptian Stock
Market
Note 5 for table 10: The EgyptianStock Market isan emergingcapital market.Itsmajorstock
marketindex isthe Capital Market AuthorityIndex (CMA).Thisreportwill investigate the daily
stock marketanomaliesinthismarketusingCMA highlightingthe efficiencyof thismarket(Ally
et al.,2004
11. Table 11: Monday Returns VersusReturns during the rest of the weekfor EmergingEgyptian
Stock market
Note 6 for table 11: In orderto assessthe presence of thisanomalyinthe Egyptianstockmarket,a
difference-of-meanstestof null hypothesisisperformedsothatthe meanreturnon Monday equalsto
the meanreturn onrest of the days of the week
12. Table 12: Day of the WeekEffectfor DevelopedEuropeanMarkets in MeanTerms

18. 17
13. Table 13: Evidence ofDay ofthe WeekIn VolatilityTermsfor DevelopedEuropeanMarkets
Note 7 for table 13: estimatesof GARCH(1,1) coefficient.
Note 8: In the financial arena a holiday is seen to characterize as a day when trading should have
normally taken place but did not.(Lakonishok and Smidt, 1988) The days are further classified as
pre-holiday, post holiday or regular days. Pre holidays are those holidays that have at least one
preceding day as a trading day, but at least one succeeding day as a holiday. Post Holidays are
those which have at least one preceding day as a holiday and one succeeding day as a trading day.

19. 18
Figure 1
Note 9 for figure1 :
The Figure below examines the average daily returns for each of the three intervals of the
second half of December; the pre-Christmas period, the inter-holiday period and the pre-
holiday period to be positive and considerable in seven nations.