Deep Reinforcement Learning for Financial Portfolios: A New Approach to Adaptive Strategy Development

In today’s world of rapidly fluctuating financial markets, static portfolio management approaches prove to be inadequate to deal with complex conditions and fluctuations. This paper aims to understand the practical implications of deep reinforcement learning (DRL) for building smart and self-sufficient trading strategies for the financial portfolio. Thus, we employ and examine three well-known DRL methods, namely DQN, PPO, and TD3 for enhancing the portfolio’s performance. For performance evaluation, we use a comprehensive dataset of historical price data and other financial ratios accumulated over 10 years. This study shows that DRL models have higher performance as compared to conventional methods: Markowitz mean-variance optimization and simple benchmark equal-weighted portfolio, and Machine learning techniques: SVM and RF. Among the DRL models considered here, TD3 recorded the highest total return of 145.8%, the highest risk-adjusted returns with a Sharpe ratio of 1.40, the maximum drawdown of 11.8%, and the lowest volatility of 11.5%. These outcomes reveal the efficiency and stability of the application of DRL approaches in financial portfolio management. This research adds to the expanding literature in financial technology, which presents a new and efficient strategy for improving portfolio management for investors and financial institutions using better computational algorithms.