Sector classification through non-Gaussian similarity

Maximilian Vermorken (), Ariane Szafarz and Hugues Pirotte Speder

No 08-032.RS, Working Papers CEB from ULB -- Universite Libre de Bruxelles

Abstract: Standard sector classification frameworks present drawbacks that might hinder portfolio manager. This paper introduces a new non-parametric 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 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 2-digit GICS system for U.S. blue chip companies. It is shown that specific relations between stocks are not captured by the GICS framework. The method is applied on two different samples and tested for robustness.

Keywords: equity sectors; industry classification; portfolio management (search for similar items in EconPapers)
JEL-codes: G11 G19 (search for similar items in EconPapers)
Pages: 48 p.
Date: 2008-10
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (5)

Published by: Université Libre de Bruxelles, Solvay Business School, Centre Emile Bernheim (CEB)

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Related works:
Journal Article: Sector classification through non-Gaussian similarity (2010)
Working Paper: Sector Classification through non-Gaussian Similarity (2010)
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