Jennifer Bautista
Alexandra Stone
Rosa Stoveld
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The January effect is a calendar-related anomaly
in the financial market where financial securities
prices increase in the month of January. This
creates an opportunity for investors to buy stock
for lower prices before January and sell them
after their value increases.
The January Effect was first observed in, or
before, 1942 by investment banker Sidney B.
Wachtel. It is the observed phenomenon that
since 1925, small stocks have outperformed the
broader market in the month of January, with
most of the disparity occurring before the middle
of the month.
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Banz and Reinganum (1981) report a
significant negative relationship between
abnormal returns and market value of
common equity. They assume that the
negative relation between abnormal and
returns and size is stable.
Brown, Kleidon and Marsh (1983) report a
reversal of the size anomaly for certain years
and reject the hypothesis of stationary yearto-year abnormal returns attributable to size.
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This study examines the month-to-month
stability of the size anomaly over the period from
1963 to 1979.
The evidence indicates that nearly 50% of the
average magnitude of the risk-adjusted premium
of small firms relative to large firms over this
period is due to anomalous January abnormal
effects.
26% of the size premium is attributable to large
abnormal returns during the first week of trading
in the year and almost 11% is attributable to the
first trading day.
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In this study the anomalous negative relation
between firm size and abnormal riskadjusted returns are investigated.
Beta estimates that adjust for nonsynchronous trading and trading infrequency
in the computation of abnormal returns are
employed.
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The data for this study are drawn from the
CRSP daily stock, from 1963 to 1979.
That sample consists of firms which were
listed on the NYSE or AMEX and had returns
on the CRSP files during the entire calendar
year under consideration.
Each year all sample firms are ranked based
on the market value of their common equity
and then they are divided into ten portfolios.
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Rolls (1981) maintains that since the shares of
small firms are generally the most infrequently
traded and the shares of large firms are the most
frequently traded, the betas for small firms are
downward biased while the betas of large firms
are upward biased.
Reinganum (1982) reports that, while the
direction of the bias in beta estimation is
consistent with these adjusted betas will still
exhibit a pronounced negative relation to firm
size.
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There is no distinguishable relation between the
OLS estimates of beta and firm size measured by
market value of equity.
Although the Scholes-Williams beta estimates for
smaller firms are generally higher than the
corresponding OLS estimates, there is still no
distinct ordering of the betas according to firm
size.
The Dimson beta estimate for the portfolio of
smallest firms is significantly larger than the
largest firm portfolio beta, and there is a near
monotone declining relationship between firm
size and Dimson beta.
Average daily excess returns (in percent), size measured by market value of equity, beta estimates and autocorrelations
of excess returns for ten portfolios constructed from firms on the NYSE
Portfolio
Average excess return
Smallest
0.08
Market value of equity
OLS beta
Scholes-Williams beta
1st order
autocorrelation of
excess return
Dimson beta
4.40
0.76
0.92
1.47
0.22
10.50
0.87
1.01
1.47
0.13
18.90
0.91
1.03
1.43
0.07
30.30
0.93
1.08
1.42
0.03
46.70
0.99
1.08
1.42
0.03
73.40
0.98
1.08
1.30
0.10
118.10
0.95
1.03
1.27
0.18
210.20
0.97
1.04
1.22
0.28
433.00
0.96
1.02
1.12
0.35
1092.10
0.96
0.97
0.98
0.35
-10.38
2
0.03
-5.83
3
0.02
-3.88
4
0.00
-0.97
5
-0.01
( - 2.24)
6
-0.01
(-4.82)
7
-0.02
( - 6.40)
8
-0.02
(-6.74)
9
-0.03
( - 7.20)
Largest
-0.04
(-7.19)
0.10
0.08
Percentage Abnormal Return
0.06
0.04
0.02
0.00
Smallest
-0.02
-0.04
-0.06
2
3
4
5
6
7
8
9
Largest
3 Tests
1. Seasonality of the monthly average return
2. Seasonality in observations in the smallest
and largest portfolios
3. Non-stationary mean abnormal returns and
autocorrelation
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CRSP daily stock files
From 1963-1979
Firms listed on the NYSE or AMEX
◦ 1,500 (1960’s) to 2,400 firms (late 1970s)
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Common stock prices follow a multiplicative
random walk
Rt    e t
Rt = the random portfolio return
μ = expected return for the info set
e t = iid random variable with mean 0
Implies: portfolio returns are time invariant
Further Research:
Accounts for months Rt    m  e t
Results: large monthly returns in January
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Keim (1982)
Plot the negative
relation between
abnormal return and
firm size for each
month
Clear difference for
January
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January average size effect = 15%
Other months = 2.5%
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Data : 10 market portfolios of NYSE-AMEX stocks
from 1963-1979
Null Hypothesis: expected abnormal returns for
each month are equal
Rt  a1  a2 D2t  a3 D3t  ... a12 D12t  et
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Rt - average daily CRSP excess return for day t
Dit - dummy variable indicating month ex =
February
ai - the excess return for the month
If Null Hypothesis is true, then:
◦ ai’s should be close to zero
◦ F-statistic measuring the joint significance of the
dummy variables should be insignificant
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Null Hypothesis: mean abnormal returns for
the largest and smallest portfolios are the
same
Results: Reject Ho
Smallest firm:
◦ F-stat = 14.59 ; significant at any level
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Largest firm: F-stat = 17.63
◦ Abnormal returns were negative and lower all other
months
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Observed size premium in January is positive
and significantly larger
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First Trading Day
◦ Difference in abnormal returns between the largest
and smallest portfolio average = 3.2% ; st dev = 2%
◦ Positive every year
◦ Accounts for 10.5% of the annual size effect
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First Five Trading Days
◦ Ave = 8%
◦ Accounts for 26.3% of the annual size effect
• 1969-1973 – large firms outperformed small firms (except 1971)
Period
Ave Jan diff in monthly T-statistic
% abnormal returns
btw smallest and
largest portfolios
1963-1968
9.7
10.2
1969-1973
11.3
9.1
1974-1979
23.1
13.0
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Null Hypothesis – non-stationary mean
abnormal returns may cause autocorrelation
Rt  a1  a2 D2t  a3 D3t  ... a12 D12t  et
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Use an ordinary least squares method of
regression
Rt (mean-adjusted abnormal return) is used
to compute autocorrelations
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Not significantly different from zero
Reject Null Hypothesis
Small
Portfolio
Large Portfolio
All observations
0.189
0.320
W/o Jan
observations
0.187
0.274
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Tax loss selling hypothesis
Information hypotheses
Neither hypotheses has been linked
theoretically or empirically to the January
effect
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January returns are larger than normal due to
year-end tax loss selling of shares that have lost
value during the previous year
Small firms are more likely to participate in tax
loss selling
Not supported theoretically or empirically
◦ Theoretically: arbitrage possibilities in non-segmented
markets with non-taxable investors
◦ Empirically: Magnitude of January effect should vary with
level of personal income tax rates
 Example – January effect should have been smaller after WWII
because personal tax rates were low, but it was actually
larger
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Investors are motivated to reduce their yearend tax liability by selling stocks that have
declined in value and use the realized losses
to offset capital gains
This causes value of stock to decrease
because of selling pressure at the end of the
year and to return to equilibrium in January
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January is a month of uncertainty because of
the expected release of previous year
performance
The release of financial information during
the month of January can have greater effects
on small sized firms versus large firms
because large firms are able to collect data
more quickly and effectively
This hypothesis can be tested by event (fiscal
year-end) versus calendar date
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Not related to economics
Possibly due to:
◦ Outliers
◦ Concentration of listings and delistings at year-end
◦ Data base errors
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Investigations of CAPM show the existence of
anomalous abnormal returns that are negatively
related to size
Daily abnormal return distributions in January have
larger means in relation to the other 11 months
Relation between abnormal returns and size are
always more negative and pronounced than the other
11 months
50% of the average magnitude of the size anomaly
from 1963-1979 were caused by abnormal returns
during the month of January
◦ More than 50% of January premium was caused by large
abnormal returns during the 1st week of January (majority
occurring on the 1st day)