Abstract

GUEVAR-CORTES, Rogelio Ladrón de; TORRA-PORRAS, Salvador  and  MONTE-MORENO, Enric. Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange. Finanz. polit. econ. [online]. 2021, vol.13, n.2, pp.513-543.  Epub Apr 12, 2022. ISSN 2248-6046.  https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9.

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis, and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. This research is carried out according to two different perspectives. First, an evaluation from a theoretical and matrix scope is done, making parallelism among their particular mixing and demixing processes, as well as the at-tributes of the factors extracted by each method. Secondly, an empirical study to measure the level of accuracy in the reconstruction of the original variables is accomplished. In general, the results of this research point to Neural Networks Principal Component Analysis as the best technique from both theoretical and empirical standpoints.

JEL Classification: G12, G15, C45.

Keywords : Neural Networks Principal Component Analysis; Independent Component Analysis; Factor Analysis; Principal Component Analysis; Mexican Stock Exchange.

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