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Sector classification through non-Gaussian similarity

  • M. Vermorken
  • A. Szafarz
  • H. Pirotte

Standard sector classification frameworks present drawbacks that might hinder portfolio managers. This article introduces a new nonparametric approach to equity classification. Returns are decomposed into their fundamental drivers through Independent Component Analysis (ICA). Stocks are then classified according to the relative importance of the identified fundamental drivers for their returns. A method is developed permitting the quantification of these dependencies, using a similarity index. Hierarchical clustering allows for grouping the stocks into new classes. The resulting classes are compared with those from the two-digit Global Industry Classification System (GICS) for US blue chip companies. It is shown that specific relations between stocks are not captured by the GICS framework. The method is tested for robustness and successfully applied to portfolio management.

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Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 20 (2010)
Issue (Month): 11 ()
Pages: 861-878

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Handle: RePEc:taf:apfiec:v:20:y:2010:i:11:p:861-878
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  1. Andrew J. Patton, 2004. "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 130-168.
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  8. Thierry Vessereau, 2000. "Factor Analysis and Independent Component Analysis in Presence of High Idiosyncratic Risks," CIRANO Working Papers 2000s-46, CIRANO.
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