Portfolio Optimization via Transfer Learning

ArXiv ID: 2511.21221 “View on arXiv”

Authors: Kexin Wang, Xiaomeng Zhang, Xinyu Zhang

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.

Keywords: Transfer Learning, Portfolio Optimization, Cross-Market Information, Sharpe Ratio, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced theoretical mathematics including asymptotic Sharpe ratio optimization, model averaging, and detailed proofs, yet it is grounded in empirical application with real-world case studies (A-shares/H-shares, US industries) and simulation-based validation.

  flowchart TD
    A["Research Goal: Enhance Portfolio Optimization<br>using Cross-Market Information"] --> B["Data Collection<br>A-shares/H-shares & US Equities"]
    B --> C["Methodology: Transfer Learning Framework"]
    C --> D["Computational Process: Asymptotic Identification<br>& Selection of Informative Datasets"]
    D --> E["Process: Discard Misleading Info<br>Retain Valid Cross-Market Signals"]
    E --> F["Outcome: Portfolio Strategy with<br>Maximum Asymptotic Sharpe Ratio"]
    F --> G["Validation: Numerical & Case Studies<br>Confirm Promising Performance"]