For the popular mean-variance portfolio choice problem in the case without a risk-free asset, we develop a new portfolio strategy to mitigate estimation risk. We show that in both calibrations and real datasets, optimally combining the sample global minimum variance portfolio with a sample...

In this paper, we provide the asymptotic theory for the widely used Fama and MacBeth (1973) two-pass regression in the usual case of a large number of assets. We find that the convergence of the OLS two-pass estimator depends critically on the time series sample size in addition to the number of...

We propose a novel modification to the popular principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of predictors, our scaled PCA, sPCA, puts more weights on those predictors that...

We propose an extension of the Fama-MacBeth regression by allowing stock returns to respond differently to firm characteristics. This heterogeneous model can potentially capture non-linearity and interactions. Empirical, applying it to a common set of fifteen firm characteristics, we find that...