Ritesh Tiku

Papers

Total Citations

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About

Ritesh Tiku 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 how advanced deep learning models—specifically DQN and A2C—are rendering traditional algorithmic trading strategies obsolete. By demonstrating that these reinforcement learning frameworks can outperform conventional financial robots in navigating complex market dynamics, Tiku contributes to a paradigm shift in automated trading. While his citation count is currently modest at 2, the timeliness and practical relevance of his research signal growing influence in the fintech community. His work addresses a pressing need for adaptive, self-learning systems capable of deciphering statistical trading strategies that static algorithms cannot handle. As deep learning continues to reshape financial markets, Tiku’s contributions provide a foundational bridge between cutting-edge AI techniques and real-world investment decision-making, marking him as a promising voice in the next generation of quantitative researchers.