Jennifer Bautista Alexandra Stone Rosa Stoveld

 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.

 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 year- to-year abnormal returns attributable to size.

 This study examines the month-to-month stability of the size anomaly over the period from 1963 to  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.

 In this study the anomalous negative relation between firm size and abnormal risk- adjusted returns are investigated.  Beta estimates that adjust for non- synchronous trading and trading infrequency in the computation of abnormal returns are employed.

 The data for this study are drawn from the CRSP daily stock, from 1963 to  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.

 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.

 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 PortfolioAverage excess returnMarket value of equityOLS betaScholes-Williams betaDimson beta 1st order autocorrelation of excess return Smallest ( ) (-4.82) ( ) (-6.74) ( ) Largest (-7.19)

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

 CRSP daily stock files  From  Firms listed on the NYSE or AMEX ◦ 1,500 (1960’s) to 2,400 firms (late 1970s)

 Common stock prices follow a multiplicative random walk  = the random portfolio return  μ = expected return for the info set  = iid random variable with mean 0  Implies: portfolio returns are time invariant  Further Research:  Accounts for months  Results: large monthly returns in January

 Keim (1982)  Plot the negative relation between abnormal return and firm size for each month  Clear difference for January

 January average size effect = 15%  Other months = 2.5%

 Data : 10 market portfolios of NYSE-AMEX stocks from  Null Hypothesis: expected abnormal returns for each month are equal  R t - average daily CRSP excess return for day t  D it - dummy variable indicating month ex = February  a i - the excess return for the month  If Null Hypothesis is true, then: ◦ a i ’s should be close to zero ◦ F-statistic measuring the joint significance of the dummy variables should be insignificant

 Null Hypothesis: mean abnormal returns for the largest and smallest portfolios are the same  Results: Reject H o  Smallest firm: ◦ F-stat = ; significant at any level  Largest firm: F-stat = ◦ Abnormal returns were negative and lower all other months  Observed size premium in January is positive and significantly larger

 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  First Five Trading Days ◦ Ave = 8% ◦ Accounts for 26.3% of the annual size effect

PeriodAve Jan diff in monthly % abnormal returns btw smallest and largest portfolios T-statistic – large firms outperformed small firms (except 1971)

 Null Hypothesis – non-stationary mean abnormal returns may cause autocorrelation  Use an ordinary least squares method of regression  R t (mean-adjusted abnormal return) is used to compute autocorrelations

 Not significantly different from zero  Reject Null Hypothesis Small Portfolio Large Portfolio All observations W/o Jan observations

 Tax loss selling hypothesis  Information hypotheses  Neither hypotheses has been linked theoretically or empirically to the January effect

 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

 Investors are motivated to reduce their year- end 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

 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

 Not related to economics  Possibly due to: ◦ Outliers ◦ Concentration of listings and delistings at year-end ◦ Data base errors

 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 were caused by abnormal returns during the month of January ◦ More than 50% of January premium was caused by large abnormal returns during the 1 st week of January (majority occurring on the 1 st day)