Services on Demand
Journal
Article
Indicators
Cited by SciELO
Access statistics
Related links
Similars in SciELO
Share
Revista mexicana de economía y finanzas
On-line version ISSN 2448-6795Print version ISSN 1665-5346
Abstract
LADRON DE GUEVARA CORTES, Rogelio; TORRA PORRAS, Salvador and MONTE MORENO, Enric. Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities. Rev. mex. econ. finanz [online]. 2021, vol.16, n.spe, e697. Epub Sep 05, 2022. ISSN 2448-6795. https://doi.org/10.21919/remef.v16i0.697.
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.
Keywords : Neural Networks Principal Component Analysis; Independent Component Analysis; Factor Analysis; Principal Component Analysis; Mexican Stock Exchange.