Federated reinforcement learning for portfolio management
Publication Type
Book
Publication Date
7-2022
Abstract
Financial portfolio management involves the constant redistribution of wealth over a set of financial assets and can, by its sequential nature, be modelled using reinforcement learning (RL). Federated learning allows traders to jointly train models without revealing their private data. We show on S&P500 market data how personalized, robust federated reinforcement learning using Fed+ produces trading policies that offer higher annual returns and Sharpe ratios than other methods.
Discipline
Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
First Page
467
Last Page
482
ISBN
9783030968960
Identifier
10.1007/978-3-030-96896-0_21
Publisher
Springer
City or Country
Cham
Citation
YU, Pengqian; WYNTER, Laura; and LIM, Shiau Hong.
Federated reinforcement learning for portfolio management. (2022). 467-482.
Available at: https://ink.library.smu.edu.sg/sis_research/10343
Additional URL
https://doi.org/10.1007/978-3-030-96896-0_21