Document type
ArticleVersion
Published versionPublication date
Publication license
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/184702
Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities
Journal Title
Director/Tutor
Journal ISSN
Volume Title
Related resource
Abstract
The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market. The methodology used compares the results of estimation produced by Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Neural Networks Principal Component Analysis (NNPCA) under three different perspectives. The results showed that in general: PCA, FA, and ICA produced similar systematic risk factors and betas; NNPCA and ICA produced the greatest number of fully accepted models in the econometric contrast; and, the interpretation of systematic risk factors across the four techniques was not constant. Additional research testing alternative extraction techniques, econometric contrast, and interpretation methodologies are recommended, considering the limitations derived from the scope of this work. The originality and main contribution of this paper lie in the comparison of these four techniques in both the financial and Mexican contexts. The main conclusion is that depending on the purpose of the analysis, one technique will be more suitable than another.
Subject
Subject (English)
Citation
Citation
LADRÓN DE GUEVARA CORTÉS, Rogelio, TORRA PORRAS, Salvador and MONTE MORENO, Enric. Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities. Revista Mexicana de Economía y Finanzas. 2021. Vol. 16, num. e697. ISSN 2448-6795. [consulted: 11 of August of 2026]. Available at: https://hdl.handle.net/2445/184702