Timing profit in that trade. The academic

and Seasonality in the Stock Market

Seasonality in general markets refers to a varied collection
of results concerning calendar ”abnormalities” in returns on assets. When combined
they 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 seen
in debt markets, futures markets, forex rates, and even non-US exchanges.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

As we learned in class, the weak form of the Efficient Market
Hypothesis states that all past financial information is already reflected in
current stock market prices or returns. Therefore, these seasonality effects dispute
the Efficient Market Hypothesis because they assert that, if no transaction
costs exist, excess returns can be attained with no extra risk by merely
knowing what day of the week it is, whether its September, if it’s the last day
of the month, and so on. In addition, any persistence of such a “seasonality
effect” is an additional challenge to the Efficient Market Hypothesis because
in an efficient market, once an inefficiency comes to light (especially if it
is repeated consistently) it should immediately disappear. This is because once
investors recognize the pattern or inefficiency, they will trade accordingly
the next time in expectation that it will occur, removing any potential profit
in that trade. The academic articles that I will be covering in my paper focus on
two distinct “seasonality effects”, the postschool holiday effect and the
Monday effect. These papers have all been published within the last seven years
and even more recent evidence suggests that these effects still persist today.

In the postschool holiday effect, a novel idea first
suggested in June 2017, the authors state that there is a strong link in certain
regions between the month following their school holidays and the market returns
from equities in those regions. In fact, it appears that stock market returns across
the globe are 0.6-1% lower in the month after major school holidays than in other
months. In the United States, this helps to explain the “September effect”
where over the past century, the average return for the Dow Jones industrial
average has been -1.09% while all other months have seen a return of +.75%. While
many have been long puzzled as to why this phenomenon exists, this paper
asserts 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% reduction
in trading volume in the major school holiday months and then a comparable increase
in volume in the month following, suggesting that investors may not be as
interested or active in the market when they are on vacation with their
families or in their summer homes. As to why we see a decline in prices as
opposed to flat or increased prices following these school holidays, the
article cites other reputable papers in saying that negative information on
stocks is more difficult to process than positive news and requires more
attention and research, resources that investors lack while on vacation.
Naturally, upon many investors returning from their breaks, they are better
able to process the negative news and appropriately sell off the equities.

To support their hypothesis in the United States outside of
the well-established “September effect”, they looked at six states whose school
years 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 multiple
times in this paper, we know that local investors are biased heavily towards holding
local companies in their portfolios, which means that anything affecting the broader
trading mentality of those local investors will in some way affect the returns
on those equities. In line with the hypothesis, when we look at returns in August,
the postschool holiday month, for companies that are headquartered in those
states we see a statistically significant drop in equity prices on average over
the long run. We still see a drop in prices of those equities in September, but
it is comparably smaller and likely attributed to non-local investors whose “postschool
holiday 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 Hong
Kong in the month following the Chinese New Year, the most culturally important
holiday in Chinese tradition. Although the holiday is short (often only a few
days), many businesses and individuals often unofficially take extra days off,
putting it within the definition of a “major school holiday”. As such, we would
expect to see a similar sell-off in equities after the holiday in these regions
where 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 “postschool
holiday effect” in any of these countries.

Overall, the evidence presented in this paper strongly supports
the idea that returns are lower than average in the long run when compared to
other parts of the year. This effect is seen across the globe and at least
partially explains the “September effect” that has mystified investors over the
past century. The authors in this paper suggest that this phenomenon is a
result of investor inattention over the holidays that reduces their ability to
process negative information. When investors return, they realize the true
impact of the negative information and sell off their equities accordingly. Once
again, this directly violates the weak form of the Efficient Market Hypothesis
as not only should equity prices always reflect the true scope of information
as soon as its released, but investors should always recognize this pattern and
be able to trade away the inefficiency.

The next paper focuses on investor sentiment, or “mood”, in the
appearance of the “Monday effect” in the 1970’s and its almost total disappearance
in the 1990’s-2000’s. This appears to be one instance in which investors
realized a market inefficiency and, over time, adjusted their trading strategies
to profit off of and eventually eliminate it. Since Cross first observed regular
negative returns on the stock market on Mondays in 1973, many other studies
have been done on the “Monday effect” and other “day-of-the-week effects” (including
one by Kenneth French of the Fama-French Model in 1980). Negative Monday returns
were discovered to be distinct in the long run and affected both US and
international markets all the way up until the early 1990’s, with the effect
still persisting today in small and nano cap stocks. Many explanations were
offered as to why this phenomenon occurred including the timing of corporate
releases after market close on Friday and the comparative advantage of informed
traders after a non-trading period like the weekends.

 However, this paper is
mainly concerned with analyzing the impact of investor “mood” on this effect. Their
hypothesis involves something called the “blue Monday” theory, which states
that investor sentiment is more pessimistic earlier in the week and
particularly on Monday. In order to test this, they used Facebook’s daily mood
index across 20 different countries and their respective stock markets to look
for a correlation between countries’ daily aggregate “moods” and stock market
returns on Mondays across five years. With the invention and prevalence of
Facebook, the authors claim to be the first ones in a long history of research
on this phenomenon to be able to empirically test the idea that it really might
just 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 significantly
weakened in the last decade) does tend to disappear. In some scenarios like within
small capitalization firms and collectivist/risk-averse countries where the Monday
effect is stronger, we see further evidence that mood is the primary driver in
lower 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 correlation
between investor mood and stock returns is statistically significant at the 1%
level, further lending credence to the idea. The culture of a country also
comes into play when exploring this effect as mood is expected to be stronger
in collectivist, high-uncertainty-avoidance countries (a designation popular in
psychology). With this stronger mood, we expect to and do see a tendency to
overreact 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 the
authors, this research helps explain the Monday effect and sets the framework
for more investor-sentiment-based research into the market inefficiencies collectively
referred to as market timing or seasonality.

In conclusion, these two papers definitively challenge the Efficient
Market Hypothesis. Both the postschool holiday effect and the Monday effect
show a strong correlation between investor behavior and abnormal stock returns.
These effects and others like them provide opportunities for intelligent
investors to make greater-than-market level returns with no more risk in the
long run. As the field of behavioral finance continues to develop, we can
expect to see more papers like these published that define and explore these
inefficiencies and for them to eventually diminish as has happened with the
Monday effect.



1.    Fang, L., Lin, C. and Shao, Y. (2017), School
Holidays and Stock Market Seasonality. Financial Management.


2.      Abu Bakar A., Siganos A. and Vagenas-Nanos E. (2014), Does Mood Explain the Monday
Effect?, Journal of Forecasting. 33, pages 409–418. doi: 10.1002/for.2305