Enhancing Financial Market Risk Forecasting through Hybrid K-means and Support Vector Machine Models

Abstract

Financial market risk forecasting is crucial for investors, financial institutions, and policymakers to make informed decisions and manage their portfolios effectively. Traditional methods often struggle to capture the complex and dynamic nature of financial markets, leading to inaccurate predictions and heightened uncertainty. In response, this study proposes a novel approach that combines the strengths of K-means clustering and Support Vector Machine (SVM) models to enhance the accuracy and reliability of financial market risk forecasting. The proposed hybrid model begins with K-means clustering, a powerful unsupervised learning technique, to identify distinct clusters or groups within historical financial market data. By partitioning the data into meaningful clusters, the model aims to capture underlying patterns and relationships that may affect future market movements. Each cluster represents a unique market regime characterized by specific risk factors and dynamics. Following the clustering phase, the study employs Support Vector Machine (SVM), a robust supervised learning algorithm, to build predictive models for each identified cluster. This study contributes to the advancement of financial market risk forecasting by introducing a novel hybrid approach that integrates clustering and SVM techniques. The proposed model offers a more comprehensive and reliable framework for identifying and predicting market risk, thereby assisting investors and financial practitioners in making more informed decisions and mitigating potential losses.