  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.
