Hierarchical Bayes methods for multifactor model estimation and portfolio selection

The factor model is an important construct for both portfolio managers and researchers in modern finance. For practitioners, factor model coefficients are used to guide the construction of optimal portfolios. For academicians , factor model parameters play a fundamental role in explaining equilibrium asset prices and other market phenomena. This paper presents a hierarchica l modeling procedure that can substantially improve the accuracy of factor model parameter estimates through incorporation of cross-sectional informat ion. It is shown that this improvement in parameter estimation accuracy tra nslates into substantial improvement in portfolio performance. Expressions are derived that characterize the sensitivity of portfolio performance to p arameter estimation error. Evidence with NYSE data suggests that the hierar chical estimation technique leads to superior out-of-sample portfolio perfo rmance when compared to alternative estimation approaches.