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Factor Analysis and Independent Component Analysis in Presence of High Idiosyncratic Risks

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  • Thierry Vessereau

Abstract

This paper addresses the case when stock market returns are assumed being generated through a factorial structure. High levels of idiosyncratic risk are shown to exist for most stocks on the US market, when CAPM or APT are used for the estimation of diversifiable risks. The presence of these high idiosyncratic risks may not allow a correct estimation of the generating factors when using a classic factor analysis method. The Independent Component Analysis is introduced as an adequate method for factor estimation; using neural networks, this method allows taking into account the information contained in higher moments. Through simulations of markets with various assumptions on the kind of processes followed by the generating factors, this method is shown to strongly improve the factors estimation, especially when high idiosyncratic risks are present. In the latter case, a traditional factor analysis, such as the Principal Component Analysis, may fail to estimate the generating factors. Cet article traite le cas d'un marché d'actions dont les rendements sont susceptibles d'être expliqués par une structure factorielle. Sur le marché américain, il est montré que des risques idiosyncratiques élevés existent pour la plupart des actions quelque soit le modèle d'évaluation utilisé (CAPM ou APT). La présence de ces risques idiosyncratiques élevés peut empêcher une évaluation correcte des facteurs générant les rendements, lorsqu'une méthode d'analyse factorielle classique est utilisée. Il est ici proposé d'utiliser la méthode de l'Analyse en Composantes Indépendantes (INCA), reposant sur les réseaux neuronaux, pour parvenir à une évaluation correcte des facteurs; cette méthode permet de prendre en compte la majeure partie de l'information contenue dans les distributions des rendements des actions, en utilisant les moments d'ordre élevé de ces distributions. ¸ l'aide de simulations de marchés artificiels, pour lesquels différentes hypothèses des processus de générations des rendements sont retenus, il est montré que la méthode de l'INCA permet une amélioration significative de l'estimation de la structure factorielle, en particulier lorsque des composantes idiosyncratiques élevées sont présents dans les les rendements des actions. Dans ce dernier cas, une méthode classique d'analyse factorielle, comme l'Analyse en Composantes Principales, peut échouer totalement dans l'estimation des facteurs.

Suggested Citation

  • Thierry Vessereau, 2000. "Factor Analysis and Independent Component Analysis in Presence of High Idiosyncratic Risks," CIRANO Working Papers 2000s-46, CIRANO.
    • Handle: RePEc:cir:cirwor:2000s-46
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    File URL: https://cirano.qc.ca/files/publications/2000s-46.pdf
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    References listed on IDEAS

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    Cited by:

    1. Maximilian Vermorken & Ariane Szafarz & Hugues Pirotte, 2008. "Sector classification through non-Gaussian similarity," Working Papers CEB 08-032.RS, ULB -- Universite Libre de Bruxelles.
    2. Fabio Bellini & Ernesto Salinelli, 2003. "Independent Component Analysis and Immunization: An Exploratory Study," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 6(07), pages 721-738.

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