Optimizing Risk Parity Portfolios via Hidden Markov Model Empowered Deep Reinforcement Learning under Macroeconomic Regime Switching Scenarios
Abstract
The stabilization of institutional investment portfolios against the backdrop of volatile macroeconomic shifts remains a primary challenge in financial engineering and socio-technical systems design [1]. Traditional risk parity frameworks, while effective in distributing risk exposure across asset classes, often fail to account for latent structural breaks and rapid regime transitions within global markets [4]. This paper explores a novel architectural synthesis that integrates Hidden Markov Models with Deep Reinforcement Learning to optimize risk parity allocations in environments characterized by macroeconomic regime switching. By utilizing the probabilistic state-identification capabilities of Markovian modeling, the proposed system provides the reinforcement learning agent with a high-fidelity representation of market context [9], enabling more resilient policy derivation. The research focuses on the system-level trade-offs between computational complexity and portfolio robustness, addressing the infrastructure requirements necessary for deploying such models within high-stakes institutional environments [16]. Furthermore, the discussion extends to the governance of algorithmic financial systems, examining the policy implications of automated asset allocation and the ethical considerations of systemic stability in an era of AI-driven finance [22]. The findings suggest that a hybrid state-aware architecture significantly improves the risk-adjusted returns and drawdown profiles of diversified portfolios compared to static or purely reactive allocation strategies [31].
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