Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution

Abstract

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. HRT separates trading into two coordinated decisions: a factorized sparse High-Level Controller (HLC) selects asset-level increase, reduce, or hold directions from compact market and text-derived signals, while a risk-aware Low-Level Controller (LLC) converts these directions into feasible portfolio weight adjustments under turnover, drawdown, and text-risk penalties. This decomposition avoids enumerating the full joint action space and makes selection and execution easier to inspect. We evaluate HRT on an open stock-news benchmark with a fixed 89-stock Nasdaq universe, using 2013--2018 for training, 2019 for validation, and 2020--2023 for final out-of-sample testing; the test horizon is restricted to 2020--2023 due to public benchmark data availability under the same timestamp-clean text-aware protocol. Across market-proxy, same-universe portfolio, alpha-only, flat-RL, and hierarchical ablation baselines, HRT delivers the strongest learning-based return--risk--cost trade-off. The full model improves Sharpe from 1.06 for HRT-Base to 1.24, reduces daily turnover from 0.112 to 0.090, and remains robust under transaction-cost stress. These results suggest that separating sparse directional selection from risk-aware execution is an effective way to incorporate market forecasts and text-derived risk signals into portfolio management.