Paper Contents
Abstract
This study examines the transformative role of Artificial Intelligence (AI) in quantitative stock market prediction, automated trading, and mutual fund management. By analyzing advanced Deep Reinforcement Learning (DRL) architectures, multimodal data integration, and Generative AI (GenAI)–based portfolio optimization, the research highlights AI’s superior capabilities in systematic risk management, pattern extraction, and data-driven decision-making. Comparative performance analysis across bear, recovery, and bull market regimes reveals that while AI delivers superior downside protection and stable risk-adjusted returns in volatile environments, human fund managers outperform in bull markets due to qualitative judgment and intuitive opportunity capture. The study further evaluates critical limitations—market unpredictability, overfitting, data quality constraints, algorithmic bias—and underscores the rising need for Explainable AI (XAI) within regulatory frameworks such as the EU AI Act. Findings suggest that an integrated hybrid model combining AI’s consistency with human adaptability offers the most resilient approach to modern asset management. Keywords: Artificial Intelligence in Finance, Deep Reinforcement Learning, Portfolio Optimization, Explainable AI (XAI), Algorithmic Trading, Systemic Financial Risk.
Copyright
Copyright © 2026 Dr Chilukuri Venkat Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
