A Multi-Agent Artificial Intelligence Framework for Personalized Financial Advisory and Automated Investment Strategies Using NIFTY 50 Market Data

Abstract

Research in the field of financial technology increasingly emphasizes the role of artificial intelligence in assisting investment decision-making. This study proposes a Multi-Agent AI system designed to provide personalized financial advisory and execute automated investment strategies for the Indian stock market, specifically using the NIFTY 50 index. The system integrates a forecasting agent, a risk assessment agent, and a recommendation agent to deliver timely and informed decisions to investors. Experimental evaluation demonstrates that the proposed approach improves predic- tion accuracy, reduces exposure to market volatility, and enhances overall portfolio performance compared to traditional strategies. Backtesting results reveal smoother portfolio growth, reduced drawdowns, and higher risk-adjusted returns. Additionally, the system offers adaptive and user-friendly guidance, supporting both novice and experienced investors in making informed financial decisions. The findings indicate that AI-driven multi-agent systems can bridge the knowledge gap in retail investing and offer a practical framework for intelligent financial advisory. Future enhancements may include reinforcement learning, real-time sentiment analysis, and multi-asset integration to further improve decision quality and system resilience.