Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach

TL;DR

This paper introduces RL-BHRP, a reinforcement learning-based hierarchical method for dynamic, risk-balanced portfolio construction that outperforms static and sector benchmarks in U.S. equities from 2012 to 2025.

Contribution

The paper presents a novel two-level reinforcement learning approach that adaptively allocates risk across sectors and stocks, improving long-term portfolio performance.

Findings

Portfolio grew by 120% over the test period

Achieved higher annual growth rate (~15%)

Maintained diversification and low drawdowns

Abstract

We propose a two-level, learning-based portfolio method (RL-BHRP) that spreads risk across sectors and stocks, and adjusts exposures as market conditions change. Using U.S. Equities from 2012 to mid-2025, we design the model using 2012 to 2019 data, and evaluate it out-of-sample from 2020 to 2025 against a sector index built from exchange-traded funds and a static risk-balanced portfolio. Over the test window, the adaptive portfolio compounds wealth by approximately 120 percent, compared with 101 percent for the static comparator and 91 percent for the sector benchmark. The average annual growth is roughly 15 percent, compared to 13 percent and 12 percent, respectively. Gains are achieved without significant deviations from the benchmark and with peak-to-trough losses comparable to those of the alternatives, indicating that the method adds value while remaining diversified and investable. Weight charts show gradual shifts rather than abrupt swings, reflecting disciplined rebalancing and the cost-aware design. Overall, the results support risk-balanced, adaptive allocation as a practical approach to achieving stronger and more stable long-term performance.