This paper provides a simple method to account for heteroskedasticity and cross-sectional dependence in samples with large cross sections and relatively few time-series observations. The method is motivated by cross-sectional regression studies in finance and accounting. Simulation evidence suggests that these estimators are dependable in small samples and may be useful when generalized least squares is infeasible, unreliable, or computationally too burdensome. We also consider efficiency issues and show that, in principle, asymptotic efficiency can be improved using a technique due to Cragg (1983).

Abstract This Paper explores the importance and price implications of style investing by institutional investors in the stock market. To analyze styles, we assign stocks to deciles or segments across three style dimensions: size, value/growth, and sector. we find strong evidence that institutional investors reallocate and sector. We find strong evidence that institutional investors reallocate across style groupings more intensively than across random stock groupings. In addition, we show that own segment style inflows and refurns positively forecast future stock returns, which distant segament style inflows and returns forecast negatively. We argue that behavioral theories play a role in explaining these results.