Transfer Learning for Portfolio Optimization

TL;DR

This paper introduces transfer risk as a new indicator for transfer learning effectiveness in portfolio optimization, demonstrating its correlation with performance and utility in selecting source tasks across various transfer scenarios.

Contribution

The paper proposes the novel concept of transfer risk, linking it to transfer learning success and providing a practical method for source task selection in portfolio optimization.

Findings

Transfer risk correlates strongly with transfer learning performance.

Transfer risk efficiently identifies suitable source tasks.

Numerical experiments yield new insights for portfolio management.

Abstract

In this work, we explore the possibility of utilizing transfer learning techniques to address the financial portfolio optimization problem. We introduce a novel concept called "transfer risk", within the optimization framework of transfer learning. A series of numerical experiments are conducted from three categories: cross-continent transfer, cross-sector transfer, and cross-frequency transfer. In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of "transferability"; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings.