Machine learning approaches to market risk forecasting

Abstract

The growing riskiness and instability of financial markets have created a greater demand for sound risk forecasting methods. Machine learning (ML) methods provide an alternative solution to the classical statistical models and allow taking out complex patterns in big data related to the financial sphere and enhance predictive quality. This paper explores various types of ML algorithms such as supervised learning, deep learning, and ensemble models, as well as their application to the market risk prediction. The analysis shows that hybrid and ensemble models are always superior to single-model methods in forecasting the volatility of the market and the possible occurrence of a crisis by substantial analysis of historical market data. Moreover, feature selection and data preprocessing play a great role in improving the performance of the model, indicating the significance of the input variables of high quality. The results highlight the possible benefits of ML-guided constructs to aid in financial decision making, risk control, and law adherence, and suggest practitioners with practical recommendations on how to operate in highly unpredictable financial landscape settings.