Portfolio Optimization via Transfer Learning

TL;DR

This paper introduces a transfer learning-based portfolio strategy that leverages cross-market information to improve investment performance, asymptotically maximizing the Sharpe ratio through selective data utilization.

Contribution

It develops a novel transfer learning approach for portfolio optimization that asymptotically identifies and uses informative datasets while discarding misleading data.

Findings

Achieves asymptotic maximum Sharpe ratio.

Demonstrates promising numerical and case study performance.

Effectively leverages cross-market information for portfolio enhancement.

Abstract

Recognizing that asset markets generally exhibit shared informational characteristics, we develop a portfolio strategy based on transfer learning that leverages cross-market information to enhance the investment performance in the market of interest by forward validation. Our strategy asymptotically identifies and utilizes the informative datasets, selectively incorporating valid information while discarding the misleading information. This enables our strategy to achieve the maximum Sharpe ratio asymptotically. The promising performance is demonstrated by numerical studies and case studies of two portfolios: one consisting of stocks dual-listed in A-shares and H-shares, and another comprising equities from various industries of the United States.