An Empirical Comparison of Cross-Validation Procedures for Portfolio Selection

Abstract

We present the constrained portfolio selection problem as a learning problem requiring hyper-parameter specification. In practice, hyper-parameters are typically selected using a validation procedure, of which there are several widely-used alternatives. However, the performance of different validation procedures is problem dependent and has not been investigated for the portfolio selection problem. This study examines the behaviour of common validation procedures, including holdout, k-fold cross-validation, Monte Carlo cross-validation, and repeated k-fold cross-validation for estimating performance and selecting hyper-parameters for constrained portfolio selection. The results demonstrate that repeated k-fold cross-validation is the best performing procedure and recommend using 5 repetitions with 3 ≤ k ≤ 10 in practice.