Transfer Learning for Portfolio Optimization
Haoyang Cao Haotian Gu Xin Guo Mathieu Rosenbaum
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.