Timing profit in that trade. The academic

Timingand Seasonality in the Stock MarketSeasonality in general markets refers to a varied collectionof results concerning calendar ”abnormalities” in returns on assets. When combinedthey show that returns are consistently better in some months of the year,certain periods of the months, or even individual days throughout the year.These patterns are not only limited to the US stock market, but are also seenin debt markets, futures markets, forex rates, and even non-US exchanges. As we learned in class, the weak form of the Efficient MarketHypothesis states that all past financial information is already reflected incurrent stock market prices or returns. Therefore, these seasonality effects disputethe Efficient Market Hypothesis because they assert that, if no transactioncosts exist, excess returns can be attained with no extra risk by merelyknowing what day of the week it is, whether its September, if it’s the last dayof the month, and so on. In addition, any persistence of such a “seasonalityeffect” is an additional challenge to the Efficient Market Hypothesis becausein an efficient market, once an inefficiency comes to light (especially if itis repeated consistently) it should immediately disappear.

This is because onceinvestors recognize the pattern or inefficiency, they will trade accordinglythe next time in expectation that it will occur, removing any potential profitin that trade. The academic articles that I will be covering in my paper focus ontwo distinct “seasonality effects”, the postschool holiday effect and theMonday effect. These papers have all been published within the last seven yearsand even more recent evidence suggests that these effects still persist today.In the postschool holiday effect, a novel idea firstsuggested in June 2017, the authors state that there is a strong link in certainregions between the month following their school holidays and the market returnsfrom equities in those regions. In fact, it appears that stock market returns acrossthe globe are 0.6-1% lower in the month after major school holidays than in othermonths.

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In the United States, this helps to explain the “September effect”where over the past century, the average return for the Dow Jones industrialaverage has been -1.09% while all other months have seen a return of +.75%. Whilemany have been long puzzled as to why this phenomenon exists, this paperasserts that it is due to investor inattention during the school holiday months(in this case summer break) that lead to the slow incorporation of negative news.One way this idea of “inattention” is supported is that we also see a 7.8% reductionin trading volume in the major school holiday months and then a comparable increasein volume in the month following, suggesting that investors may not be asinterested or active in the market when they are on vacation with theirfamilies or in their summer homes. As to why we see a decline in prices asopposed to flat or increased prices following these school holidays, thearticle cites other reputable papers in saying that negative information onstocks is more difficult to process than positive news and requires moreattention and research, resources that investors lack while on vacation.Naturally, upon many investors returning from their breaks, they are betterable to process the negative news and appropriately sell off the equities.

To support their hypothesis in the United States outside ofthe well-established “September effect”, they looked at six states whose schoolyears traditionally start one month earlier in August, as opposed to September,due to agricultural reasons. As we have learned in class and as is cited multipletimes in this paper, we know that local investors are biased heavily towards holdinglocal companies in their portfolios, which means that anything affecting the broadertrading mentality of those local investors will in some way affect the returnson those equities. In line with the hypothesis, when we look at returns in August,the postschool holiday month, for companies that are headquartered in thosestates we see a statistically significant drop in equity prices on average overthe long run.

We still see a drop in prices of those equities in September, butit is comparably smaller and likely attributed to non-local investors whose “postschoolholiday effect” begins in that month. To support their hypothesis outside of the United States,they looked at the market return data for China, Taiwan, Singapore, and HongKong in the month following the Chinese New Year, the most culturally importantholiday in Chinese tradition. Although the holiday is short (often only a fewdays), many businesses and individuals often unofficially take extra days off,putting it within the definition of a “major school holiday”. As such, we wouldexpect to see a similar sell-off in equities after the holiday in these regionswhere the Chinese New Year is celebrated. Again, in line with the hypothesis,we do see a statistically significant drop in local equity prices, falling 1.8-1.9%on average over 40 years when compared to similar time spans throughout the year.

This effect is in fact much larger and more significant than any other “postschoolholiday effect” in any of these countries. Overall, the evidence presented in this paper strongly supportsthe idea that returns are lower than average in the long run when compared toother parts of the year. This effect is seen across the globe and at leastpartially explains the “September effect” that has mystified investors over thepast century. The authors in this paper suggest that this phenomenon is aresult of investor inattention over the holidays that reduces their ability toprocess negative information.

When investors return, they realize the trueimpact of the negative information and sell off their equities accordingly. Onceagain, this directly violates the weak form of the Efficient Market Hypothesisas not only should equity prices always reflect the true scope of informationas soon as its released, but investors should always recognize this pattern andbe able to trade away the inefficiency.The next paper focuses on investor sentiment, or “mood”, in theappearance of the “Monday effect” in the 1970’s and its almost total disappearancein the 1990’s-2000’s. This appears to be one instance in which investorsrealized a market inefficiency and, over time, adjusted their trading strategiesto profit off of and eventually eliminate it. Since Cross first observed regularnegative returns on the stock market on Mondays in 1973, many other studieshave been done on the “Monday effect” and other “day-of-the-week effects” (includingone by Kenneth French of the Fama-French Model in 1980). Negative Monday returnswere discovered to be distinct in the long run and affected both US andinternational markets all the way up until the early 1990’s, with the effectstill persisting today in small and nano cap stocks. Many explanations wereoffered as to why this phenomenon occurred including the timing of corporatereleases after market close on Friday and the comparative advantage of informedtraders after a non-trading period like the weekends.

 However, this paper ismainly concerned with analyzing the impact of investor “mood” on this effect. Theirhypothesis involves something called the “blue Monday” theory, which statesthat investor sentiment is more pessimistic earlier in the week andparticularly on Monday. In order to test this, they used Facebook’s daily moodindex across 20 different countries and their respective stock markets to lookfor a correlation between countries’ daily aggregate “moods” and stock marketreturns on Mondays across five years. With the invention and prevalence ofFacebook, the authors claim to be the first ones in a long history of researchon this phenomenon to be able to empirically test the idea that it really mightjust be caused by investors “having a case of the Mondays”. In their research,they did in fact find that after adjusting for mood, the Monday effect (though significantlyweakened in the last decade) does tend to disappear. In some scenarios like withinsmall capitalization firms and collectivist/risk-averse countries where the Mondayeffect is stronger, we see further evidence that mood is the primary driver inlower stock returns.

In small cap firms, many traders and investors are themselves”small” (mainly individuals) who are influenced the most by mood as opposed to larger,institutional investors that hold and trade stocks off of fundamentals. The correlationbetween investor mood and stock returns is statistically significant at the 1%level, further lending credence to the idea. The culture of a country alsocomes into play when exploring this effect as mood is expected to be strongerin collectivist, high-uncertainty-avoidance countries (a designation popular inpsychology). With this stronger mood, we expect to and do see a tendency tooverreact to news among investors in addition to a stronger negative mood on Monday.This result is also statistically significant at the 1% level. According to theauthors, this research helps explain the Monday effect and sets the frameworkfor more investor-sentiment-based research into the market inefficiencies collectivelyreferred to as market timing or seasonality.In conclusion, these two papers definitively challenge the EfficientMarket Hypothesis.

Both the postschool holiday effect and the Monday effectshow a strong correlation between investor behavior and abnormal stock returns.These effects and others like them provide opportunities for intelligentinvestors to make greater-than-market level returns with no more risk in thelong run. As the field of behavioral finance continues to develop, we canexpect to see more papers like these published that define and explore theseinefficiencies and for them to eventually diminish as has happened with theMonday effect.  Citations:1.    Fang, L., Lin, C.

and Shao, Y. (2017), SchoolHolidays and Stock Market Seasonality. Financial Management.doi:10.

1111/fima.12182 2.      Abu Bakar A.

, Siganos A. and Vagenas-Nanos E. (2014), Does Mood Explain the MondayEffect?, Journal of Forecasting. 33, pages 409–418.

doi: 10.1002/for.2305 


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