In this paper, the authors document regularities in trading patterns of individual and institutional investors related to the day of the week. They find a relative increase in trading activity by individuals on Mondays. In addition, there is a tendency for individuals to increase the number of sell transactions relative to buy transactions, which might explain at least part of the weekend effect.

This paper reports anomalous price behavior around repurchase tender offers. Buying shares before the expiration date of a repurchase tender offer and tendering to the firm produces, on average, abnormal returns of more than 9 percent over a period shorter than one week. In addition, the authors find that repurchasing companies experience economically and statistically significant abnormal returns in the two years after the repurchase. The upward price drift is mainly caused by the behavior of the small firms in the sample.

In this paper, we document regularities in trading patterns of individual and institutional investors related to the day of the week. We find a relative increase in trading activity by individuals on Mondays. In addition, there is a tendency for individuals to increase the number of sell transactions relative to buy transactions, which might explain at least part of the weekend effect.

Capital gains taxes create incentives to trade. Our major finding is that turnover is higher for winners (stocks, the prices of which have increased) than for losers, which is not consistent with the tax prediction. However, the turnover in December and January is evidence of tax‐motivated trading; there is a relatively high turnover for losers in December and for winners in January. We conclude that taxes influence turnover, but other motives for trading are more important. We were unable to find evidence that changing the length of the holding period required to qualify for long‐term capital gains treatment affected turnover.

Many empirical studies find that the distribution of stock returns departs from normality. In such cases, it is desirable to employ a statistical estimation procedure that may be more efficient than ordinary least squares. This paper describes various robust methods, which have attracted increasing attention in the statistical literature, in the context of estimating beta risk. The empirical analysis documents the potential efficiency gains from using robust methods as an alternative to ordinary least squares, based on both simulated and actual returns data.