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.

The authors use a robust regression estimator to analyze the risk premia on size and book-to-market. They find that the risk premium on size that was estimated by Eugene F. Fama and Kenneth R. French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. The authors also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the sixteen months with the most extreme coefficients. They 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.