PERFORMANCE EVALUATION OF MUTUAL FUNDS

Abstract

Mutual funds have emerged as one of the most popular investment instruments, offering diversified exposure and professional management to individual and institutional investors. However, evaluating the performance and risk profile of mutual funds is a complex task, traditionally carried out using financial ratios and historical return data. This study aims to enhance the performance evaluation process by leveraging Machine Learning (ML) and Deep Learning (DL) techniques to analyze, classify, and predict mutual fund behavior more accurately and efficiently.The research involves collecting historical Net Asset Value (NAV) data, risk-return indicators, and fund characteristics for a diverse set of mutual funds. Using ML algorithms such as Random Forest, Support Vector Machines (SVM), and K-Means Clustering, the funds are categorized based on risk levels and past performance. Further, Deep Learning models, particularly Long Short-Term Memory (LSTM) networks, are used for time-series forecasting to predict future NAVs and returns, capturing non-linear patterns and market volatility.This AI-driven approach enables the identification of high-performing funds, estimation of potential future returns, and early detection of underperforming assets. It also offers investors and fund managers valuable insights into risk management, portfolio optimization, and decision-making. The findings demonstrate that integrating ML and DL significantly enhances traditional mutual fund analysis by making it more dynamic, predictive, and data-driven.