The pattern of correlations described in Proposition 1 is potentially testable
by examining whether long-run reversals following days with public news
events are smaller than reversals on days without such events. The price be-
havior around public announcements has implications for corporate event
studies (see subsection B.3).
B. 2 unconditional serial correlations and volatilityReturn autocorrelations in well-known studies of momentum and reversal
are calculated without conditioning on the arrival of a public information
signal. To calculate a return autocorrelation that does not condition on
whether private versus public information has arrived, consider an experi-
ment where the econometrician picks consecutive dates for price changes
randomly (dates 1 and 2, versus dates 2 and 3). The date 2 and 3 price
changes are positively correlated, but the date 1 and 2 price changes are neg-
atively correlated. Suppose that the econometrician is equally likely to pick
either pair of consecutive dates. Then the overall autocorrelation is negative:
Proposition 2.If investors are overconfident, price changes are uncon-
ditionally negatively autocorrelated at both short and long lags.Thus, the constant-confidence model accords with long-run reversals (nega-
tive long-lag autocorrelations) but not with short-term momentum (posi-
tive short-lag autocorrelation). However, the short-lag autocorrelation will
INVESTOR PSYCHOLOGY 469
Figure 13.1. Average price as a function of time with overconfident investors. This
figure shows price as a function of time for the dynamic model of section 3 with
(solid line) and without (dashed line) self-attribution bias.