When data exhibit cross-sectional variation in scale and regression parameters, pooled regression parameters can exhibit severe biases. It is commonly assumed that normalizing per-share earnings data by a firm's stock price eliminates cross-sectional variation in scale. This study shows that...

We present and estimate a Bayesian Hierarchical model of mutual fund returns. In our model, a fund's alpha reflects not only that fund's return history, but also information from other fund returns. Because parameters are estimated simultaneously for all funds, we can identify common residual...

This paper utilizes a Bayesian hierarchical model to study the distribution of skill (alphas) among mutual funds. The hierarchical model specifies a probability distribution over all parameters and funds, making it possible to formally analyze the variability of alphas across funds. In addition,...

This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by cognitive biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts....