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Neural networks principal component analysis for estimating the generative multifactor model of returns under a statistical approach to the arbitrage pricing theory: Evidence from the mexican stock exchange

Author
Ladrón de Guevara, R.; Torra Porras, Salvador; Monte, E.
Type of activity
Journal article
Journal
Computación y sistemas
Date of publication
2019-01-01
Volume
23
Number
2
First page
281
Last page
298
DOI
10.13053/CyS-23-2-3193
Repository
http://hdl.handle.net/2117/168380 Open in new window
URL
http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3193 Open in new window
Abstract
A nonlinear principal component analysis (NLPCA) represents an extension of the standard principal component analysis (PCA) that overcomes the limitation of the PCA’s assumption about the linearity of the model. The NLPCA belongs to the family of nonlinear versions of dimension reduction or the extraction techniques of underlying features, including nonlinear factor analysis and nonlinear independent component analysis, where the principal components are generalized from straight lines to curv...
Citation
Ladrón de Guevara, R.; Torra Porras, S.; Monte, E. Neural networks principal component analysis for estimating the generative multifactor model of returns under a statistical approach to the arbitrage pricing theory: Evidence from the mexican stock exchange. "Computación y sistemas", 1 Gener 2019, vol. 23, núm. 2, p. 281-298.
Keywords
Arbitrage pricing theory, Extraction of underlying risk factors, Mexican stock exchange, Nonlinear principal component analysis
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants

  • Ladrón de Guevara Cortés, Rogelio  (author)
  • Torra Porras, Salvador  (author)
  • Monte Moreno, Enrique  (author)

Attachments