Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis

Risk can be distributed in complex and unexpected ways across financial markets. Grouping financial assets into broad portfolios is a common practice, but this aggregation tends to hide important nuances of the overall risk profile. For example, large long and short positions may individually be important, but cancel out in the aggregate. This paper introduces the “RiskMapper,” an interactive, visual tool for exploring the benefits of different approaches for aggregating and disaggregating financial portfolios. It describes early-stage research into the strengths, weaknesses, and ramifications of different rules, risk measures, and visualization approaches.

Abstract

This paper explores the application of three different portfolio formation rules using standard clustering techniques - K-means, K-mediods, and hierarchical - to a large financial data set (16 years of daily CRSP stock data) to determine how the choice of clustering technique may affect analysts’ perceptions of the riskiness of different portfolios in the context of a prototype visual analytics system designed for financial stability monitoring. We use a two-phased experimental approach with visualizations to explore the effects of the different clustering techniques. The choice of clustering technique matters. There is signi cant variation among techniques, resulting in different “pictures” of the riskiness of the same underlying data when plotted to the visual analytics tool. This sensitivity to clustering methodolgy has the potential to mislead analysts about the riskiness of portfolios. We conclude that further research into the implications of portfolio formation rules is needed, and that visual analytics tools should not limit analysts to a single clustering technique, but instead should provide the facility to explore the data using different techniques.

Keywords: Clustering Techniques, Financial Stability Monitoring, Visual Analytics