PERFORMANCE EVALUATION OF DIFFERENT TYPES OF MUTUAL FUNDS SELECTED

Abstract

Mutual funds have become an integral part of the investment strategy for both retail and institutional investors in India. With numerous options available across equity, debt, hybrid, and index categories, it becomes imperative to evaluate their performance effectively to make informed investment decisions. This research focuses on the comparative performance evaluation of mutual funds using both traditional financial metrics and modern machine learning (ML) techniques. The study extends beyond basic return analysis by applying ML models like Random Forest, K-Means Clustering, and XG Boost to classify and predict fund performance. Using historical NAV data and associated financial indicators, the research builds a predictive framework and an intelligent classification system. The results not only highlight the efficiency of ML in fund evaluation but also open the door for automated advisory systems in mutual fund investments.