Abstract: 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 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.
Bio: Haoyang Cao is an assistant professor in the Department of Applied Mathematics and Statistics and a member of the Data Science and Artificial Intelligence Institute at Johns Hopkins University. Cao’s research interests span two major directions. One involves stochastic controls, stochastic differential games, and mean-field games, especially those concerning modeling problems with impulse controls and singular controls. The other involves theoretical foundation of machine learning. Her interest in machine learning was motivated by the need to develop computational tools to solve high-dimensional stochastic games with large populations. In return, her studies on stochastic controls and games have enriched the theoretical understanding of many machine learning paradigms including generative models, (inverse) reinforcement learning, meta learning and transfer learning. In addition, she is working on applying her skill set of stochastic analysis, modeling, and machine learning techniques to applications in financial mathematics, inventory control, health care, and beyond. Before joining the department, Cao was a postdoctoral researcher at Centre de Mathematiques Appliquees, Ecole Polytechnique supervised by Mathieu Rosenbaum. Before that, she was a machine learning in finance research associate at the Alan-Turing Institute supervised by Lukasz Szpruch from the University of Edinburgh and Samuel N. Cohen from the University of Oxford. Haoyang received her PhD in 2020 from the Department of Industrial Engineering and Operations Research at the University of California, Berkeley, under the supervision of Xin Guo. She obtained her bachelor’s degree in mathematics from the University of Hong Kong in 2015.
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