Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis

Victoria Lemieux (), Payam S. Rahmdel (), Rick Walker (), B.L. William Wong () and Mark Flood
Additional contact information
Payam S. Rahmdel: University of British Columbia
Rick Walker: Middlesex University London
B.L. William Wong: Office of Financial Research

No 15-01, Staff Discussion Papers from Office of Financial Research, US Department of the Treasury

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 significant 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 methodology 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 (search for similar items in EconPapers)
Pages: 6 pages
Date: 2015-01-23
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Citations: View citations in EconPapers (7)

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