Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

• Published in: Revista Finanzas y Política Económica Vol. 13; no. 2; pp. 513 - 543
• Main Authors: Rogelio, Torra Porras, Salvador, Monte Moreno, Enric
• Format: Journal Article
• Language: English; Portuguese
• Published: Bogota Universidad Católica de Colombia, Facultad de Ciencias Económicas y Administrativas 01.07.2021; Universidad Católica de Colombia
• Subjects: Accuracy; Arbitrage; Biochemistry; Comparative studies; Discriminant analysis; Feature extraction; Neural networks; Principal components analysis; Risk factors; Social sciences; SOCIAL SCIENCES, INTERDISCIPLINARY; Stock exchanges; Mexican Stock Exchange; Factor Analysis; Independent Component Analysis; Neural Networks Principal Component Analysis; Bolsa Mexicana de Valores; Análisis de Componentes Principales; Análisis de Componentes Principales basado en Redes Neuronales; Principal Component Analysis; Análisis de Componentes Independientes; Análisis Factorial
• ISSN: 2248-6046; 2011-7663
• DOI: 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. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.