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Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis


  • Victoria Lemieux

    () (University of British Columbia)

  • Payam S. Rahmdel

    () (University of British Columbia)

  • Rick Walker

    () (Middlesex University London)

  • B.L. William Wong

    () (Middlesex University London)

  • Mark D. Flood

    () (Office of Financial Research)


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.

Suggested Citation

  • Victoria Lemieux & Payam S. Rahmdel & Rick Walker & B.L. William Wong & Mark D. Flood, 2015. "Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis," Staff Discussion Papers 15-01, Office of Financial Research, US Department of the Treasury.
  • Handle: RePEc:ofr:discus:15-01

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    References listed on IDEAS

    1. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    2. Dimitrios Bisias & Mark Flood & Andrew W. Lo & Stavros Valavanis, 2012. "A Survey of Systemic Risk Analytics," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 255-296, October.
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    Cited by:

    1. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822,
    2. Flood, Mark D. & Lemieux, Victoria L. & Varga, Margaret & William Wong, B.L., 2016. "The application of visual analytics to financial stability monitoring," Journal of Financial Stability, Elsevier, vol. 27(C), pages 180-197.
    3. Gautier Marti & Philippe Very & Philippe Donnat & Frank Nielsen, 2015. "A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series," Papers 1509.05475,
    4. Gautier Marti & Philippe Very & Philippe Donnat, 2015. "Toward a generic representation of random variables for machine learning," Working Papers hal-01196883, HAL.


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