Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities

The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities Mexican Market. methodology compares results estimation produced by Principal Component Analysis (PCA), Factor (FA), Independent (ICA), and Neural Networks (NNPCA) under three different perspectives. showed that in general: PCA, FA, ICA similar betas; NNPCA greatest number fully accepted models econometric contrast; and, interpretation across was not constant. Additional research testing alternative extraction techniques, contrast, methodologies are recommended, considering limitations derived from scope work. originality main contribution lie comparison these both financial contexts. conclusion depending purpose analysis, one technique will be more suitable than another.