Noise-proofing Universal Portfolio Shrinkage

TL;DR

This paper improves the robustness and efficiency of the Universal Portfolio Shrinkage Approximator (UPSA) in financial portfolio optimization by introducing methods to reduce estimation noise and covariate shift effects, leading to significantly better performance.

Contribution

It proposes two novel techniques—time-averaging penalty weights and using Average Oracle correlation eigenvalues—to enhance UPSA's robustness and outperform the original method.

Findings

Combined methods outperform UPSA in most scenarios

Robustness to estimation noise is significantly improved

Portfolio performance is enhanced with new techniques

Abstract

We enhance the Universal Portfolio Shrinkage Approximator (UPSA) of Kelly et al. (2023) by making it more robust with respect to estimation noise and covariate shift. UPSA optimizes the realized Sharpe ratio using a relatively small calibration window, leveraging ridge penalties and cross-validation to yield better portfolios. Yet, it still suffers from the staggering amount of noise in financial data. We propose two methods to make UPSA more robust and improve its efficiency: time-averaging of the optimal penalty weights and using the Average Oracle correlation eigenvalues to make covariance matrices less noisy and more robust to covariate shift. Combining these two long-term averages outperforms UPSA by a large margin in most specifications.