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Risk of Transfer Learning and its Applications in Finance

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  • Haoyang Cao
  • Haotian Gu
  • Xin Guo
  • Mathieu Rosenbaum

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 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.

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  • Haoyang Cao & Haotian Gu & Xin Guo & Mathieu Rosenbaum, 2023. "Risk of Transfer Learning and its Applications in Finance," Papers 2311.03283, arXiv.org.
    • Handle: RePEc:arx:papers:2311.03283
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    References listed on IDEAS

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    1. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    2. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
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    Cited by:

    1. Marius George Popa, 2024. "Romania'S Post-Eu Accession State Aid Policy," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 185-206, August.

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