Vishnureddy Prashant Muskawar

Papers

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About

Vishnureddy Prashant Muskawar is a rising researcher at the intersection of artificial intelligence and quantitative finance, with a primary focus on deep reinforcement learning for portfolio management. His most-cited work, "Portfolio Management using Deep Reinforcement Learning" (2024), critically examines the limitations of traditional algorithmic trading strategies in the face of modern deep learning technologies. By benchmarking models like DQN and A2C, Muskawar demonstrates how these advanced architectures can uncover complex, non-linear patterns in market data, outperforming conventional financial robots. His contributions provide a rigorous framework for integrating reinforcement learning into real-world asset allocation, addressing both the promise and pitfalls of AI-driven trading. With 2 citations in a nascent field, his work is already sparking discussion among practitioners and academics. Muskawar’s research is particularly notable for its practical orientation—bridging the gap between theoretical model performance and the volatile realities of stock markets. As deep learning continues to reshape finance, his findings offer a crucial roadmap for developing more adaptive, intelligent trading systems that can navigate increasingly complex statistical environments.