AI RESEARCH
Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution
arXiv CS.LG
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ArXi:2410.14927v2 Announce Type: replace-cross Automated equity trading requires converting noisy market and news signals into executable portfolio decisions under risk, turnover, and transaction costs. We propose Hierarchical Reinforced Trader (HRT), a bi-level reinforcement learning framework for text-aware portfolio management in multi-asset equity markets.