Mutual Funds Investment Risk Analysis Using Machine Learning

The involvement of machine learning (ML) in the finance sector has ushered in a new era of investment management, profoundly influencing the mutual funds landscape. It has redefined strategies and optimized financial decision-making. This paper provides a comparative analysis of ML technologies such as AdaBoost Regressor, Support Vector Machines, Gradient Boosting, and Random Forest. The positive results indicated by the paper highlight the pivotal role of ML in revolutionizing mutual funds management in India, reflecting the nation's aspirations for financial innovation and inclusion aligning it with global levels of efficiency in investment strategies. The efficiency of the models was tested with performance metrics such as Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Performance Error (MAPE). Our evaluation concluded Random Forest was the best performer with an R2 Score of 0.925, MSE of 0.246, RMSE of 0.496, MAE of 0.271, and MAPE of 0.096.