Sector classification through non-Gaussian similarity
AbstractStandard 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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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Financial Economics.
Volume (Year): 20 (2010)
Issue (Month): 11 ()
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Other versions of this item:
- Maximilian Vermorken & Ariane Szafarz & Hugues Pirotte, 2008. "Sector classification through non-Gaussian similarity," Working Papers CEB 08-032.RS, ULB -- Universite Libre de Bruxelles.
- Maximilian Vermorken & Ariane Szafarz & Hugues Pirotte, 2010. "Sector Classification through non-Gaussian Similarity," ULB Institutional Repository 2013/95542, ULB -- Universite Libre de Bruxelles.
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G19 - Financial Economics - - General Financial Markets - - - Other
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