Transfer learning is an emerging and popular paradigm for utilizing existing
knowledge from previous learning tasks to improve the performance of new ones.
In this paper, we propose a novel concept of transfer risk and and analyze its
properties to evaluate transferability of transfer learning. We apply transfer
learning techniques and this concept of transfer risk to stock return
prediction and portfolio optimization problems. Numerical results demonstrate a
strong correlation between transfer risk and overall transfer learning
performance, where transfer risk provides a computationally efficient way to
identify appropriate source tasks in transfer learning, including
cross-continent, cross-sector, and cross-frequency transfer for portfolio
optimization.

本文提出了一个新的转移风险概念并分析了其性质，将其应用于股票收益预测和投资组合优化问题中，数值结果表明转移风险与整体转移学习性能存在强相关性，转移风险提供了一个计算高效的方法来识别适当的源任务，包括跨大陆、跨行业和跨频率转移用于投资组合优化。

转移学习的风险及其在金融中的应用

Risk of Transfer Learning and its Applications in Finance

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.

我们探讨了利用迁移学习技术解决金融投资组合优化问题的可能性，引入了一个名为 “迁移风险” 的新概念，并在迁移学习的优化框架中进行了一系列数字实验，揭示了迁移风险与迁移学习方法整体性能之间的强相关性，验证了迁移风险作为 “可迁移性” 的可行指标的重要性，迁移风险证明提供了一种计算高效的方法来识别迁移学习中适当的源任务，增强了迁移学习方法的效率和效果，此外，数字实验为不同设置下的投资组合管理提供了有价值的新见解。