1. day-of-the week effect and the holiday

1. Intro:

The January effect can be explained as a financial anomaly
where the returns of stocks particularly small stocks in January tend to be
much higher than any other month. Rozeff and Kinney (1976) was the first to
start off this research on seasonal anomalies, which created an epidemic for
other researchers to go on and study this effect in more detail. Market
anomalies are events that can be exploited to earn abnormal returns, imply
market inefficiency. The January effect is a market anomaly, and it is viewed
as controversial because till this date, none has a clear explanation of what
it is. Research countries will be the UK and Germany, with FTSE 100 and FTSE
small caps being the UK indexes and DAX 30 and SDAX being Germany’s indexes.
With these index’s, I will examine whether there is a clear indication of
January performing better than all other months and also see whether there is a
difference in the small indices compared to the larger index’s. I will also be
outlining other calendar anomalies related to time like the day-of-the week
effect and the holiday effect.  The data
will be taken from Bloomberg from which the results will be able to tell me whether
the behaviour of an investor changes once they know they’ll gain more. So the
main question I am answering will be: “is the January effect real or is it down
to coincidence?  Empirical research and
literature reviews will help me prominently to answer this question. I hope to
find a difference between January and all other months because it will then
cause further research to be done as it will be so contradicting to the famous
efficient market hypothesis which is believed by so many today.

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2. Literature review:

2.1 Efficient market
hypothesis:

This anomaly is proposes that it is impossible to beat the
market because stock market efficient causes existing share prices to always
incorporate and reflect all relevant information. Essentially EMH believes that
you cannot get higher returns than what the market returns are. Most academics
have supported EMH while most practitioners have not. This is changing though.  The term efficient market was first introduced
to us by Fama who then went on to extend and refine the concept of efficient
market hypothesis by defining three forms of market efficiency (Fama 1970); the
weak form, the semi-strong form and lastly, the strong form. The weak form is
where stock prices reflect all information regarding past prices. Semi-strong
form efficiency is when stock prices embrace publicly information as well as
past prices. Strong form is where stocks prices incorporate all of those
characteristics so; private information, public information and past prices.

Kendall (1953) built on the efficient market hypothesis to come
up with the random walk theory which was heavily built on Regnault (1863)
model. Kendall (1953) observed the random walks that stock prices tend to have.
However, if this random walks theory is supposedly true, how can we explain the
market getting beaten by Warren Buffett? EMH argument can be refuted by the
relevant contradictory studies which we can find from Basu (1972) who found
evidence to suggest a price/earnings ratio study which shows EMH not to work
correctly.

2.2 The January
effect:

The January effect is a seasonal effect where there are
increases in stock prices during the month of January. Studies have shown that
returns in January are significantly higher than returns in any other month of
the year.  Sidney Wachtel (1942) was the
first to notice this effect and he was sure to mention that Harvard Committee
on Economic research 1919 and Richard Owens and Charles Hardy 1920s studies
which was done before him on seasonality in stock prices showed no evidence of
seasonal tendencies (Wachtel, 1942). Other studies like Rozeff and Kinney
(1976) who studied the New York stock exchange to find average return to be
more than 50% higher than other months and Gultekin et al (1983) who sought to
find the January effect present in 15 different countries. Gultekin et al study
should be enough to prove the January effect as he done it in several different
countries outlining that it may a global phenomenon. These studies are able back
up the possible explanation behind the January effect which is a pattern
exhibited by stocks where there is a drop in price in December because investors
engage in tax-loss then the stocks recover quickly in the first week of trading
in January and enables investors to gain higher profits (Teall, 2012).

However, it has been acclaimed that the January effect is no
longer prominent coming in to the 20th century. Personally, I agree
with this because I believe that it will no longer down to the tax hypothesis
but instead it will be due to behavioural finance. Even before the 21st
century, researchers like Roll (1983) who did not find evidence regarding the
tax-loss selling hypothesis. Also, Pearce and Wilson (1987) believe that the
January effect existed before the introduction of a tax system so that
explanation is void.

2.3 Day of the week:

Stocks normally exhibiting larger returns on Fridays in
comparison to Mondays: Cross (1973) was the first to observe that, on average,
Monday’s stock returns are negatives and Fridays stock returns are positive.
This was then backed up by Harris (1986) who has found that the first trading
hour on Monday is characterized by negative returns, while returns are positive
for the same time period on other days. Others go on to expand the day of the
week by linking it in with the January (Rogalski 1984) report that the Monday
and weekend effect are different in January than over the other months. They
find that Monday’s returns are, on average, positive in January and negative
for the rest of the year. However, just like all other seasonality anomalies,
there have been other studies which have studied this anomaly and rejected it.
Gibbons and Hess (1981) suggest that this effect is not the result of
measurement errors in recorded prices (Gibbons and Hess 1981)

2.4 The holiday
effect:

The holiday effect is the tendency for a stock market to
gain on the final trading day before an exchanged-mandated long weekend or
holiday such as Christmas. The holiday effect was first introduced by Fields
(1934) which is the earliest proclaimed seasonality effect of this paper. The
holiday effect is very much to do with behavioural finance as it could be
proven that the behaviour of investors will influence their buying and selling
of shares. Studies by Brockhman and Michayluk (1998) suggest that investors
will tend to buy shares before holidays due to the ‘high spirits’.  In my opinion, this seasonality effect is the
most difficult to test because essentially you’ll have to find qualitative data
which shows behaviour of investors increase due to the holidays rather than any
other reason which is harder to prove than getting simple quantitative data. Research
done by Vergin and McGinnis (1999) shows how the holiday effect has disappeared
for the large companies but are still available for the smaller companies which
is similar to the January effect.

3. Methodology:

My methodology will include using Bloomberg and excel. I
will need to find data regarding FTSE 100, FTSE small caps, DAX 30 and SDAX. The
data period needs to be large enough in order to get a fair representation and
reliable results so data is from 1975 to 2016; even though there was a
financial crisis in the 2000s, it will be interesting to this whether this had
an effect on the January effect. I choose FTSE 100 because it is an index on
the London Stock exchange featuring 100 companies with the highest market
capitalisation. FTSE small cap index is an index of small market
capitalisation. DAX 30 is a market index of the top 30 German companies who
trade on the Frankfurt Stock Exchange (FSE). SDAX on the other hand is an index
on the FSE consisting of 50 small to medium sized companies. The necessary data
from Bloomberg will be extracted to excel and from excel, I will create an
excel regression output. Lastly, I have to bear in mind that Germany end of
financial year is in September.

My data will be an average of the years from 1975 to 2016.
There should be a positive difference between the return for January and the
return for the other 11 months of the year. I will be using the monthly price
returns formula of the index:

Eq (1)                                                     
             Rit =
Oi
+ ?idit +
Eit

 

Rit: Monthly return on the stock market index and t is
equal to the time

Oi: Overall average of the 11 months

?i: Return difference between January and the 11 other
months

dit: Dummy variable; January is i=1

Eit: Error term

We hope that the intercept and slope is equal to the mean
return of January.

4. Further research:

My research study merely focussed on two indexes from the UK
stock market and two index’s for Germany’s stock market. In my opinion, a fair
representation for further research of this topic would be if the researcher
took a minimum of three different countries from each continent in the world
and measures that index return for both small cap companies, medium cap
companies and large cap companies. In doing this, it will interesting to see if
January effect is a real phenomenon which happens in all stock markets
regardless of the size of the company or whether it just happens in the UK with
small cap companies.

It would also be fascinating to see whether there are any
other effects which affect the January effect mainly focussing on social or political
effects. I did not get to go into enough detail about the behavioural finance
of the January effect which would be interesting to see how the cognitive bias
assumes people make predictable mistakes when assessing information could heavily
affect the January effect.

5. Conclusion

My expectation of this project is to find whether the January
effect still holds till this day and if not, when did this effect seemed to die
out.  Due to Reinganum (1983) study on
market behaviour of small firms in January which showed results of smaller
firms receiving larger returns and January especially in the first trading days
in January. I myself will be hoping to find effects which have never been
studied. If it is true what they say about the January effect being cause by tax-loss,
we should find returns in January significantly higher than all other months
and for Germany, we should find returns to be significantly higher in October
due to their end of tax year being in September.

The main hypothesis of the January effect which is the
tax-loss hypothesis may not be the only reason the January effect takes
place.  There could be a number of
different reasons why the January effect happens and as psychology has got
bigger of the year and the number of researchers have increased there is
potential for psychology researchers to have a go at explaining the psychological
effects investors have which may in fact lead to the January effect.  However, some may argue that this will take
away the while meaning of the January effect being down to tax-loss.

All in all, there is enough evidence to show the January
effect exists however; there is also enough research to say the January effect
in fact, does not exist. Whilst I do my final dissertation and collect more evidence
to agree or refute against this seasonal anomaly and I am able to follow
through my methodology, I will able to get a more definite answer of whether I
think the January effect still exists.