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Disentangling Equity Return Regularities 77
is somewhat surprising, in view of the lower tax rate on long-term
versus short-term capital gains during the period studied. Greater
short-term loss-taking might be expected, because it shelters more
income. However, our results are consistent with other empirical
findings.% Furthermore,.irrational investor behavior may offer a
potential explanation; investors are often more averse to admitting
recent mistakes than to admitting older ones [see Shefrin and
Statman (1985)l. The attenuation of non-January retums to our taxloss
measures in their pure forms provides further evidence that
they are sensibly constructed.
Although the difference between January and non-January re
turns is not quite statistically significant for our relative-strength
measure, the average January return is negative while the average
non-January return is significantly positive. The negative returns i
January likely arise from increased profit taking among stocks w
positive relative strength, motivated by a desire to defer gain r
nition until the following tax year. Our tax measures, in contrast, are
designed only to capture rebounds from year-end tax-loss-taking.
There is no solid theoretical explanation for a January season
to yield, size, or any other security characteristic other than to tax
related measures.54 Thus, while our results showing a January seasonal
in yield remain a puzzle, the dissipation of pure January
seasonals for other anomalies, such as small size, is gratifying.
AUTOCORRELATION OF RETURN REGULARITIES
Earlier, we asserted that the evidence presented strongly contradicts
both the weak and semistrong forms of the Em.
A more subtle
test of weak-form efficiency entails an examination of the time
series of returns to equity characteristics for autocorrelation. If returns
between adjacent months are correlated (first-order autocorrelation),
then an optimal prediction for next month’s return
uses the product of the correlation coefficient and the past month’s
return. Past prices alone would have predictive content. The sequence
of first- and higher-order autocorrelations .can be used to
measure the ”memory” of the return-generating process and may
be useful in forecasting.
We examined the time-series properties of the retums to each
anomaly. There is some prior evidence of patterns in these series,