Reinforcement Learning-Based Portfolio Management with Financial Time-Series Forecasting
Keywords:
Reinforcement Learning, Portfolio Management, Financial Time-Series, Systems Engineering, Algorithmic Governance, Socio-Technical Infrastructure, Sustainability.Abstract
The integration of Reinforcement Learning (RL) into portfolio management signifies a shift from passive predictive modeling to autonomous, goal-oriented decision-making within complex financial ecosystems. Traditional portfolio optimization strategies often separate the forecasting of financial time-series from the act of capital allocation, leading to structural inefficiencies and a failure to account for transaction costs, market impact, and temporal dependencies. This paper presents a systemic investigation into a unified Reinforcement Learning framework that treats portfolio management as a continuous control problem. We examine the architectural trade-offs between value-based and policy-gradient methods, emphasizing the importance of state-space representation in capturing non-stationary market dynamics. The research explores the socio-technical infrastructures required for large-scale deployment, addressing the physical requirements of low-latency compute and the data governance frameworks necessary for institutional adoption. Furthermore, we analyze the critical dimensions of environmental sustainability in high-compute financial AI, the ethical imperatives of fairness in automated asset allocation, and the policy implications of algorithmic market convergence. By synthesizing perspectives from systems engineering, behavioral finance, and public policy, this work provides a comprehensive roadmap for developing resilient and socially responsible RL-based investment systems. We conclude that while Reinforcement Learning offers a superior capacity for navigating volatile markets, its success is contingent upon a holistic approach to system robustness and a commitment to transparent algorithmic governance.
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