Predicting Systemic Risk in Financial Markets Using Machine Learning

Abstract

This research delves into the use of Support Vector Machines (SVM) to predict systemic risk in the complex and interconnected realm of financial markets, employing SVM's ability to handle high-dimensional data and adapt to diverse data distributions. This approach aims to surpass traditional financial analysis tools by providing a more detailed understanding of systemic risk. The results anticipate a significant enhancement in risk assessment and a substantial contribution to financial risk management, aiming to bolster the precision and timeliness of insights for financial institutions and regulators. This study not only introduces SVM as an innovative analytical tool in financial risk analysis, potentially spurring further methodological advancements and the adoption of other machine learning techniques, but also seeks to offer deeper insights into the dynamics of systemic risk. The findings hold considerable educational and practical value, effectively bridging the gap between academic theory and real-world application for both scholars and industry professionals. Conclusively, the research represents a meaningful step in methodological innovation and lays a groundwork for future exploration, underscoring SVM's effectiveness in systemic risk prediction and advocating for the integration of machine learning with traditional financial analysis, thereby aiding the evolution of financial risk assessment practices.