English, Article, Journal or magazine article edition: Sector Classification through non-Gaussian Similarity Maximilian Vermorken; Ariane Szafarz; Hugues Pirotte

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Bookmark: http://trove.nla.gov.au/version/110949
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Sector Classification through non-Gaussian Similarity
Author
  • Maximilian Vermorken
  • Ariane Szafarz
  • Hugues Pirotte
Physical Description
  • preprint
Notes
  • 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.
  • equity sectors, industry classification, portfolio management
  • RePEc:sol:wpaper:08-032
Language
  • English
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