Adaptive Investment Strategies: Deep Reinforcement Learning Approaches for Portfolio Optimization

This paper addresses the role deep reinforcement learning (DRL) plays in portfolio management by making a timely and more accurate market prediction within the finance sector. Using these machines learning the methods, including the DRL algorithm, this program is designed to strengthen while making investment choices beyond the limits of the traditional framework, as well as enhancing the decision-making processes in an environment with many uncertainties and dynamics. The study begins with a literature review that is thorough and covers past works on traditional finance portfolio management, market prediction methods, and recent work on DRL in finance. Leveraging this base, we design and implement DRL-V-based models for asset management and market prediction situations. Empirical research includes the construction of models that use historical financial records about stock indexes and macroeconomic data as the main input. We use some of the performance metrics like cumulative returns, Sharpe ratio, maximum drawdown, and annualized volatility as the KPIs. We compare them with historical data along with the DRL model’s performance and other baseline models as well. The evidence obtained from the experiments proves that DRL-based investment strategies outperform other strategies by their ability to make adjustments for changing market standards and uncovering investment opportunities. Besides the comparative studies, the variety of DRL models used in DFS which perform similar tasks under different market environments has been validated.