Gaussian Process Priors for Bayesian Portfolio Selection

Abstract

This article shows how asset characteristics can be incorporated into the Bayesian portfolio selection framework. We use Gaussian process priors to model the belief that assets with similar characteristics are likely to have similar expected returns. The resulting Bayesian shrinkage estimator biases expected return estimates of assets with similar characteristics towards each other. A closed-form solution of the optimal portfolio weights in the Gaussian process prior framework is derived. Our simulation results and our empirical analysis suggest that portfolio selection with Gaussian process priors is a competitive alternative to benchmark portfolio selection approaches from the literature.