Optimizing Mutual Fund Performance: AI-Based Risk and Return Forecasting

Abstract

The increasing complexity of financial markets necessitates advanced tools for risk-return forecasting, particularly in the mutual funds sector. This study investigates the role of Artificial Intelligence (AI) in enhancing the predictive accuracy of mutual fund performance, focusing on risk assessment and return optimization. By leveraging secondary data analysis, the research evaluates how AI-driven models, including machine learning algorithms and predictive analytics, improve upon traditional forecasting methodologies. The findings underscore the advantages of AI-based forecasting techniques over conventional statistical and econometric models, highlighting their ability to process vast amounts of data, identify hidden patterns, and adapt to market fluctuations in real-time. Furthermore, the study examines the implications of AI adoption for investors and fund managers, emphasizing its potential to refine investment strategies, enhance portfolio management, and mitigate risks more effectively. This research contributes to the growing body of knowledge on AI applications in financial markets and offers insights into the future of data-driven investment decision-making.