Out-of-sample Performance-based Estimation of Expected Returns for Portfolio Selection

This paper provides a framework for obtaining the estimator of expected asset returns for portfolio selection. The framework relies on a linear model where the expected returns are the coefficients to be estimated. The model is fitted to a synthetic dataset by Bayesian regression. The estimator is computed using a Gibbs sampler; it is consistent and asymptotically efficient when the size of the synthetic dataset grows to infinity. An empirical study shows that, under appropriate conditions, mean-variance portfolios constructed using this estimator yield better out-of-sample average returns and Sharpe ratios than benchmark portfolios, with or without a norm constraint