This article studies momentum in stock returns, focusing on the role of industry, size, and book-to-market (B/M) factors. Size and B/M portfolios exhibit momentum as strong as that in individual stocks and industries. The size and B/M portfolios are well diversified, so momentum cannot be attributed to firm- or industry-specific returns. Further, industry, size, and B/M portfolios are negatively autocorrelated and cross-serially correlated over intermediate horizons. The evidence suggests that stocks covary “too strongly” with each other. I argue that excess covariance, not underreaction, explains momentum in the portfolios.

This article studies momentum in stock returns, focusing on the role of industry, size, and book-to-market (B/M) factors. Size and B/M portfolios exhibit momentum as strong as that in individual stocks and industries. The size and B/M portfolios are well diversified, so momentum cannot be attributed to firm- or industry-specific returns. Further, industry, size, and B/M portfolios are negatively autocorrelated and cross-serially correlated over intermediate horizons. The evidence suggests that stocks covary "too strongly" with each other. I argue that excess covariance, not underreaction, explains momentum in the portfolios.

ABSTRACT This paper studies the asset‐pricing implications of parameter uncertainty. We show that, when investors must learn about expected cash flows, empirical tests can find patterns in the data that differ from those perceived by rational investors. Returns might appear predictable to an econometrician, or appear to deviate from the Capital Asset Pricing Model, but investors can neither perceive nor exploit this predictability. Returns may also appear excessively volatile even though prices react efficiently to cash‐flow news. We conclude that parameter uncertainty can be important for characterizing and testing market efficiency.