IDEAS home Printed from https://ideas.repec.org/p/ofr/discus/15-01.html
   My bibliography  Save this paper

Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis

Author

Listed:
  • 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)

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.

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
    as

    Download full text from publisher

    File URL: https://financialresearch.gov/staff-discussion-papers/files/OFRsdp2015-01_LemieuxRahmdelWalkerWongFlood_ClusteringTechniques.pdf
    File Function: First version, 2015
    Download Restriction: no

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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, arXiv.org.
    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, arXiv.org.
    4. Gautier Marti & Philippe Very & Philippe Donnat, 2015. "Toward a generic representation of random variables for machine learning," Working Papers hal-01196883, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ofr:discus:15-01. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gregory Feldberg) The email address of this maintainer does not seem to be valid anymore. Please ask Gregory Feldberg to update the entry or send us the correct email address. General contact details of provider: http://edirc.repec.org/data/ofrgvus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.