Price improvement is the difference between the execution price of an order and the quoted bid or ask when the order was submitted. We show that expected price improvement falls off dramatically as the size of the order approaches the quoted depth, and becomes negative for larger orders. This is particularly important for small firms because the quoted depths are low. Using quoted spreads and depths and our estimate of expected price improvement, we show that trading strategies that attempt to exploit the weekly predictability of small-firm returns would be swamped by transaction costs.

We use a robust regression estimator to analyze the risk premia on size and book‐to‐market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.

We use a robust regression estimator to analyze the risk premia on size and book-to-market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1% most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.

In this article, we measure and interpret the common “factors” that describe money market returns. Results are presented for both three‐and four‐factor models. We find that the three‐factor model explains, on average, 86 percent of the total variation in most money market returns while the four‐factor model explains, on average, 90 percent of this variation. Using mimicking portfolios, we provide an interpretation of the systematic risks represented by these factors.

Any admissible portfolio performance measure should satisfy four minimal conditions: it assigns zero performance to each reference portfolio and it is linear, continuous, and nontrivial. Such an admissible measure exists if and only if the securities market obeys the law of one price. A positive admissible measure exists if and only if there is no arbitrage. This article characterizes the (infinite) set of admissible performance measures. It is shown that performance evaluation is generally quite arbitrary. A mutual fund data set is also used to demonstrate how the measurement method developed here can be applied.