DERL, a dynamic-embedding RL framework, beats value/equal-weighted portfolios and an MLP predict-then-optimize baseline on 1993-2022 U.S. large-cap returns, chiefly in high-volatility periods.
Proceedings of the 34th International Conference on Machine Learning - Volume 70, 214–223, ICML'17 (JMLR.org)
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Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information
DERL, a dynamic-embedding RL framework, beats value/equal-weighted portfolios and an MLP predict-then-optimize baseline on 1993-2022 U.S. large-cap returns, chiefly in high-volatility periods.