Statistical clustering methods, Application to financial data

Abstract

The note focuses on statistical clustering methods in financial data analysis, leveraging algorithms such as k-means, hierarchical clustering, and Gaussian mixture models to cluster data points based .on statistical similarities These techniques play an essential role in many financial applications, including portfolio optimization, market segmentation, risk management, and anomaly detection. For example, in portfolio optimization, clustering helps diversify investments by grouping assets with similar risk and return profiles, thereby reducing overall portfolio volatility which aids in hedging strategies and mitigates market risk. Moreover, clustering methods help in detecting anomalies. in financial transactions, enhancing fraud detection and error prevention, improving decision-making processes, and optimizing financial strategies by systematically analyzing .complex data patterns and relationships