Time Matters: Portfolio Optimization using Deep Reinforcement Learning with Sequential Memory

Portfolio optimization in Indian Exchange-Traded Funds (ETFs) presents a compelling domain for exploring the application of Deep Reinforcement Learning (DRL) techniques. This study delves into the intersection of ETF markets and DRL, with a focus on leveraging sequential memory to enhance portfolio management strategies. By employing DRL methodologies, particularly Long Short-Term Memory (LSTM) networks, we investigate the efficacy of dynamic asset allocation strategies that adapt to the evolving dynamics of Indian ETF markets over time. This empirical analysis encompasses a diverse range of ETF offerings in the Indian financial landscape, characterized by unique market dynamics and regulatory considerations. Through extensive experimentation, we evaluate the performance of DRLbased portfolio optimization approaches, shedding light on their ability to capture and exploit temporal patterns in ETF prices and market conditions. The study contributes to the growing body of research at the intersection of finance and artificial intelligence, offering insights into the applicability of DRL techniques in the context of Indian ETF markets and their potential to revolutionize portfolio management practices in dynamic financial environments.