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.

This paper examines the price effect of institutional stock trading, using a unique data set that reports the transactions (large and small) of 37 large institutional money management firms. The direction of each trade and the identity of the management firm behind each trade are known. Although institutional trades are associated with some price pressure, we find that the average effect is small. There is also a marked asymmetry between the price impact of buys versus sells. We relate our findings to various hypotheses on the elasticity of demand for stocks, the cost of executing transactions, and the determinants of market impact. Although market capitalization and relative trade size influence the market impact of a trade, the dominant influence is the identity of the money manager behind the trade.

All trades executed by thirty-seven large investment management firms from July 1986 to December 1988 are used to study the price impact and execution cost of the entire sequence('package') of trades that the authors interpret as an order. The authors find that market impact and trading cost are related to firm capitalization, relative package size, and, most importantly, to the identity of the management firm behind the trade. Money managers with high demands for immediacy tend to be associated with larger market impact.

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We evaluate the performance of models for the covariance structure of stock returns, focusing on their use for optimal portfolio selection. We compare the models' forecasts of future covariances and the optimized portfolios' out-of-sample performance. A few factors capture the general covariance structure. Portfolio optimization helps for risk control, and a three-factor model is adequate for selecting the minimum-variance portfolio. Under a tracking error volatility criterion, which is widely used in practice, larger differences emerge across the models. In general more factors are necessary when the objective is to minimize tracking error volatility.

This paper relates cross‐sectional differences in returns on Japanese stocks to the underlying behavior of four variables: earnings yield, size, book to market ratio, and cash flow yield. Alternative statistical specifications and various estimation methods are applied to a comprehensive, high‐quality data set that extends from 1971 to 1988. The sample includes both manufacturing and nonmanufacturing firms, companies from both sections of the Tokyo Stock Exchange, and also delisted securities. Our findings reveal a significant relationship between these variables and expected returns in the Japanese market. Of the four variables considered, the book to market ratio and cash flow yield have the most significant positive impact on expected returns.