Comparative Analysis of Machine Learning Models for Sectoral Volatility Prediction in Financial Markets

Abstract

A crucial aspect of financial markets is volatility forecasting, which enables analysts, investors, and policymakers to assess risk, optimize portfolios, and develop trading plans. This research investigates which machine learning (ML) model is best fit for predicting sectoral volatility. Comparing models like Random Forest, Gradient Boosting, Neural Networks, and Support Vector Regression, we apply historical data from 2012 to 2024 from eleven different sectors or industries including FMCG, Energy, Financial Services, auto etc. The research is unique in terms of holistic view of all Indian sectoral volatility indices and relationship between them. Moreover, no research has identified or compared the best ML models for predicting sectoral volatility. With the greatest R square value of 0.998 and the lowest Mean Absolute Error (MAE) of 0.765, our results demonstrate that Random Forest outperforms the other models. Significant sectoral connections and volatility patterns are also shown in the analysis, especially during the COVID-19 pandemic like sectors more susceptible to economic shocks, such as financial services and fast-moving consumer goods. Heatmaps and time-series visualization techniques demonstrate that sectoral interdependence and volatility clustering. These results highlight the potential of machine learning techniques to improve risk management and volatility pre-diction and provide insightful data for financial analysts, investors, and policymakers. The study recommends applying Random Forest ML models for volatility prediction and investment decision-making, considering limitations such as dataset bias and challenges in predicting extreme market conditions.