Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores

Este artículo compara las técnicas de reducción de dimensionalidad o de extracción de características: Análisis de Componentes Principales, Análisis Factorial, Análisis de Componentes Independientes y Análisis de Componentes Principales basado en Redes Neuronales, las cuales son usadas para extraer...

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Autores:: Ladrón de Guevara-Cortés, Rogelio
Torra-Porras, Salvador
Monte-Moreno, Enric

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Católica de Colombia

Repositorio:: RIUCaC - Repositorio U. Católica

Idioma:: eng

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dc.title.spa.fl_str_mv	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
dc.title.translated.eng.fl_str_mv	Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange
title	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
spellingShingle	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores Neural networks principal component analysis Independent component analysis Factor analysis Principal component analysis Mexican stock exchange Análisis de componentes principales basado en redes neuronales Análisis de componentes independientes Análisis factorial Análisis de componentes principales Bolsa mexicana de valores
title_short	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
title_full	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
title_fullStr	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
title_full_unstemmed	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
title_sort	Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores
dc.creator.fl_str_mv	Ladrón de Guevara-Cortés, Rogelio Torra-Porras, Salvador Monte-Moreno, Enric
dc.contributor.author.spa.fl_str_mv	Ladrón de Guevara-Cortés, Rogelio Torra-Porras, Salvador Monte-Moreno, Enric
dc.subject.eng.fl_str_mv	Neural networks principal component analysis Independent component analysis Factor analysis Principal component analysis Mexican stock exchange
topic	Neural networks principal component analysis Independent component analysis Factor analysis Principal component analysis Mexican stock exchange Análisis de componentes principales basado en redes neuronales Análisis de componentes independientes Análisis factorial Análisis de componentes principales Bolsa mexicana de valores
dc.subject.spa.fl_str_mv	Análisis de componentes principales basado en redes neuronales Análisis de componentes independientes Análisis factorial Análisis de componentes principales Bolsa mexicana de valores
description	Este artículo compara las técnicas de reducción de dimensionalidad o de extracción de características: Análisis de Componentes Principales, Análisis Factorial, Análisis de Componentes Independientes y Análisis de Componentes Principales basado en Redes Neuronales, las cuales son usadas para extraer los factores de riesgo sistemático subyacentes que generan los rendimientos de las acciones de la Bolsa Mexicana de Valores, bajo un enfoque estadístico de la Teoría de Valoración por Arbitraje. Llevamos a cabo nuestra investigación de acuerdo a dos diferentes perspectivas. Primero, las evaluamos desde una perspectiva teórica y matricial, haciendo un paralelismo entre los particulares procesos de mezcla y separación de cada método. En segundo lugar, efectuamos un estudio empírico con el fin de medir el nivel de precisión en la reconstrucción de las variables originales.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-09-08 00:00:00 2023-01-23T16:16:09Z
dc.date.available.none.fl_str_mv	2021-09-08 00:00:00 2023-01-23T16:16:09Z
dc.date.issued.none.fl_str_mv	2021-09-08
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.references.eng.fl_str_mv	Anowar, F., Sadaoui, S., & Selim, B. (2021). A conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40 (5), p.p. 1000378-. https://doi.org/10.1016/j.cosrev.2021.100378 Ayesha, S., Hanif, M. K., Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion, 59 (July 2020), p.p. 44-58. https://doi.org/10.1016/j.inffus.2020.01.005 Back, A. & Weigend, A. (1997). A first application of independent component analysis to extracting structure from stock returns. International Journal of Neural Systems, 8 (4), p.p. 473-484. https://doi.org/10.1142/S0129065797000458 Bellini, F. & Salinelli, E. (2003). Independent Component Analysis and Immunization: An exploratory study. International Journal of Theoretical and Applied Finance, 6 (7), p.p. 721-738. https://doi.org/10.1142/S0219024903002201 Cavalcante, R.C., Brasileiro, R.C., Souza, L.F., Nobrega, J.P., Oliveira, A.L.I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55 (15 August 2016), p.p. 194-211. https://doi.org/10.1016/j.eswa.2016.02.006 Coli, M., Di Nisio, R., & Ippoliti, L. (2005). Exploratory analysis of financial time series using independent component analysis. In: Proceedings of the 27th international conference on information technology interfaces, p.p. 169-174. Zagreb: IEEE. https://doi.org/10.1109/ITI.2005.1491117 Corominas, Ll., Garrido-Baserba, M., Villez, K., Olson, G., Cortés, U., & Poch, M. (2018). Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques. Environmental Modelling & Software, 106 (Agosto 2018), p.p. 89-103. https://doi.org/10.1016/j.envsoft.2017.11.023 Diebold, F.X. & Lopez, J.A. (1996). Forecast evaluation and combination. In: G.S. Madala & C.R. Rao (eds.), Handbook of statistics, Vol.14. Statistical Methods in Finance, p.p. 241-268. Amsterdam: Elsevier. https://doi.org/10.3386/t0192 Himberg, J. & Hyvärinen, A. (2005). Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. Retrieved from at: http://www.cis.hut.fi/projects/ica/icasso/about+download.shtml [2 February 2009]. Ibraimova, M. (2019). Predicting Financial Distress Through Machine Learning (Publication No. 139967) [Unpublished Master’s Thesis]. Universitat Politécnica de Catalunya. Retrieved from: http://hdl.handle.net/2117/131355 Ince, H. & Trafalis, T. B. (2007). Kernel principal component analysis and support vector machines for stock price prediction. IIE Transactions 39(6): p.p. 629-637. https://doi.org/10.1109/IJCNN.2004.1380933 Ladrón de Guevara-Cortés, R., Torra-Porras, S. & Monte-Moreno, E. (2019). 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, 23 (2), p.p. 281-298. http://dx.doi.org/10.13053/CyS-23-2-3193 Ladrón de Guevara-Cortés, R., Torra-Porras, S. & Monte-Moreno, E. (2018). Extraction of the underlying structure of systematic risk from Non-Gaussian multivariate financial time series using Independent Component Analysis. Evidence from the Mexican Stock Exchange. Computación y Sistemas, 22 (4), p.p. 1049-1064 http://dx.doi.org/10.13053/CyS-22-4-3083 Ladrón de Guevara Cortés, R., & Torra Porras, S. (2014). Estimation of the underlying structure of systematic risk using Principal Component Analysis and Factor Analysis. Contaduría y Administración, 59 (3), p.p. 197-234. http://dx.doi.org/10.1016/S0186-1042(14)71270-7 Lesch, R., Caille, Y., & Lowe, D. (1999). Component analysis in financial time series. In: Proceedings of the 1999 Conference on Computational intelligence for financial engineering, p.p. 183-190. New York: IEEE/IAFE. http://dx.doi.org/10.1109/CIFER.1999.771118 Lui, H. & Wan, J. (2011). Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market. Mathematical Problems in Engineering, 2011, p.p. 1-15. https://doi.org/10.1155/2011/382659 Lizieri, C., Satchell, S. Satchell & Zhang, Q. (2007). The underlying return-generating factors for REIT returns: An application of independent component analysis. Real Estate Economics, 35 (4): p.p. 569-598. https://doi.org/10.1111/j.1540-6229.2007.00201.x Miranda-Henrique, B., Amorin-Sobreiro, V., Kimura, H. (2019). Experts Systems with Applications, 124 (15 jun 2019), p.p. 226-251. https://doi.org/10.1016/j.eswa.2019.01.012 Pérez, J.V. & Torra, S. (2001). Diversas formas de dependencia no lineal y contrastes de selección de modelos en la predicción de los rendimientos del Ibex35. Estudios sobre la Economía Española 94 (marzo, 2001), p.p. 1-42. Retrieved from: http://documentos.fedea.net/pubs/eee/eee94.pdf Rojas, S., & Moody, J. (2001). Cross-sectional analysis of the returns of iShares MSCI index funds using Independent Component Analysis. CSE610 Internal Report, Oregon Graduate Institute of Science and Technology. Retrieved from: http://www.geocities. ws/rr_sergio/Projects/cse610_report.pdf Ross, S.A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory 13 (3): p.p. 341-360. https://doi.org/10.1016/0022-0531(76)90046-6 Sayah, M. (2016). Analyzing and Comparing Basel III Sensitivity Based Approach for the Interest Rate Risk in the Trading Book. Applied Finance and Accounting, 2 (1), p.p. 101-118. https://doi.org/10.11114/afa.v2i1.1300 Scholz, M. (2006a). Approaches to analyzing and interpret biological profile data. [Unpublished Ph.D. Dissertation]. Postdam University. Retrieved from: https://publishup.uni-potsdam.de/opus4-ubp/frontdoor/deliver/index/docId/696/file/scholz_diss.pdf Scholz, M. (2006b). Nonlinear PCA toolbox for Matlab®. Retrieved from: http://www.nlpca.org/matlab. [8 September 2008]. Scikit-Learn (2021, July 12). Manifold Learning. https://scikit-learn.org/stable/modules/manifold.html# Wei, Z., Jin, L. & Jin, Y. (2005). Independent Component Analysis. Working Paper. Department of Statistics. Stanford University. Weigang, L., Rodrigues, A. Lihua, S. & Yukuhiro, R. (2007). Nonlinear Principal Component Analysis for withdrawal from the employment time guarantee fund. In: S. Chen, P. Wang & T. Kuo (eds.), Computational Intelligence in Economics and Finance. Vol. II, p.p. 75-92. Berlin: Springer-Verlag. https://doi.org/10.1007/978-3-540-72821-4_4 Yip, F. & Xu, L. (2000). An application of independent component analysis in the arbitrage pricing theory. In: S. Amari et al. (eds.) Proceedings of the International Joint Conference on Neural Networks, p.p. 279-284. Los Alamitos: IEEE. https://doi.org/10.1109/IJCNN.2000.861471
dc.rights.eng.fl_str_mv	Rogelio, Salvador Torra Porras, Enric Monte Moreno - 2021
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spelling	Ladrón de Guevara-Cortés, Rogelio9809f92c-0876-4cb6-a7f5-0c16c98470b2Torra-Porras, Salvador4ff08da5-093f-4e52-a897-bf80629495edMonte-Moreno, Enric6e096a8e-5ad6-4d4f-8b3e-f6ca23dae2832021-09-08 00:00:002023-01-23T16:16:09Z2021-09-08 00:00:002023-01-23T16:16:09Z2021-09-08Este artículo compara las técnicas de reducción de dimensionalidad o de extracción de características: Análisis de Componentes Principales, Análisis Factorial, Análisis de Componentes Independientes y Análisis de Componentes Principales basado en Redes Neuronales, las cuales son usadas para extraer los factores de riesgo sistemático subyacentes que generan los rendimientos de las acciones de la Bolsa Mexicana de Valores, bajo un enfoque estadístico de la Teoría de Valoración por Arbitraje. Llevamos a cabo nuestra investigación de acuerdo a dos diferentes perspectivas. Primero, las evaluamos desde una perspectiva teórica y matricial, haciendo un paralelismo entre los particulares procesos de mezcla y separación de cada método. En segundo lugar, efectuamos un estudio empírico con el fin de medir el nivel de precisión en la reconstrucción de las variables originales.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.text/htmlapplication/pdftext/xml10.14718/revfinanzpolitecon.v13.n2.2021.92011-76632248-6046https://hdl.handle.net/10983/29450https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9engUniversidad Católica de Colombiahttps://revfinypolecon.ucatolica.edu.co/article/download/3740/4018https://revfinypolecon.ucatolica.edu.co/article/download/3740/3933https://revfinypolecon.ucatolica.edu.co/article/download/3740/4253Núm. 2 , Año 2021 : Vol. 13 Núm. 2 (2021)543251313Revista Finanzas y Política EconómicaAnowar, F., Sadaoui, S., & Selim, B. (2021). A conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40 (5), p.p. 1000378-. https://doi.org/10.1016/j.cosrev.2021.100378Ayesha, S., Hanif, M. K., Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion, 59 (July 2020), p.p. 44-58. https://doi.org/10.1016/j.inffus.2020.01.005Back, A. & Weigend, A. (1997). A first application of independent component analysis to extracting structure from stock returns. International Journal of Neural Systems, 8 (4), p.p. 473-484. https://doi.org/10.1142/S0129065797000458Bellini, F. & Salinelli, E. (2003). Independent Component Analysis and Immunization: An exploratory study. International Journal of Theoretical and Applied Finance, 6 (7), p.p. 721-738. https://doi.org/10.1142/S0219024903002201Cavalcante, R.C., Brasileiro, R.C., Souza, L.F., Nobrega, J.P., Oliveira, A.L.I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55 (15 August 2016), p.p. 194-211. https://doi.org/10.1016/j.eswa.2016.02.006Coli, M., Di Nisio, R., & Ippoliti, L. (2005). Exploratory analysis of financial time series using independent component analysis. In: Proceedings of the 27th international conference on information technology interfaces, p.p. 169-174. Zagreb: IEEE. https://doi.org/10.1109/ITI.2005.1491117Corominas, Ll., Garrido-Baserba, M., Villez, K., Olson, G., Cortés, U., & Poch, M. (2018). Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques. Environmental Modelling & Software, 106 (Agosto 2018), p.p. 89-103. https://doi.org/10.1016/j.envsoft.2017.11.023Diebold, F.X. & Lopez, J.A. (1996). Forecast evaluation and combination. In: G.S. Madala & C.R. Rao (eds.), Handbook of statistics, Vol.14. Statistical Methods in Finance, p.p. 241-268. Amsterdam: Elsevier. https://doi.org/10.3386/t0192Himberg, J. & Hyvärinen, A. (2005). Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. Retrieved from at: http://www.cis.hut.fi/projects/ica/icasso/about+download.shtml [2 February 2009].Ibraimova, M. (2019). Predicting Financial Distress Through Machine Learning (Publication No. 139967) [Unpublished Master’s Thesis]. Universitat Politécnica de Catalunya. Retrieved from: http://hdl.handle.net/2117/131355Ince, H. & Trafalis, T. B. (2007). Kernel principal component analysis and support vector machines for stock price prediction. IIE Transactions 39(6): p.p. 629-637. https://doi.org/10.1109/IJCNN.2004.1380933Ladrón de Guevara-Cortés, R., Torra-Porras, S. & Monte-Moreno, E. (2019). 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, 23 (2), p.p. 281-298. http://dx.doi.org/10.13053/CyS-23-2-3193Ladrón de Guevara-Cortés, R., Torra-Porras, S. & Monte-Moreno, E. (2018). Extraction of the underlying structure of systematic risk from Non-Gaussian multivariate financial time series using Independent Component Analysis. Evidence from the Mexican Stock Exchange. Computación y Sistemas, 22 (4), p.p. 1049-1064 http://dx.doi.org/10.13053/CyS-22-4-3083Ladrón de Guevara Cortés, R., & Torra Porras, S. (2014). Estimation of the underlying structure of systematic risk using Principal Component Analysis and Factor Analysis. Contaduría y Administración, 59 (3), p.p. 197-234. http://dx.doi.org/10.1016/S0186-1042(14)71270-7Lesch, R., Caille, Y., & Lowe, D. (1999). Component analysis in financial time series. In: Proceedings of the 1999 Conference on Computational intelligence for financial engineering, p.p. 183-190. New York: IEEE/IAFE. http://dx.doi.org/10.1109/CIFER.1999.771118Lui, H. & Wan, J. (2011). Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market. Mathematical Problems in Engineering, 2011, p.p. 1-15. https://doi.org/10.1155/2011/382659Lizieri, C., Satchell, S. Satchell & Zhang, Q. (2007). The underlying return-generating factors for REIT returns: An application of independent component analysis. Real Estate Economics, 35 (4): p.p. 569-598. https://doi.org/10.1111/j.1540-6229.2007.00201.xMiranda-Henrique, B., Amorin-Sobreiro, V., Kimura, H. (2019). Experts Systems with Applications, 124 (15 jun 2019), p.p. 226-251. https://doi.org/10.1016/j.eswa.2019.01.012Pérez, J.V. & Torra, S. (2001). Diversas formas de dependencia no lineal y contrastes de selección de modelos en la predicción de los rendimientos del Ibex35. Estudios sobre la Economía Española 94 (marzo, 2001), p.p. 1-42. Retrieved from: http://documentos.fedea.net/pubs/eee/eee94.pdfRojas, S., & Moody, J. (2001). Cross-sectional analysis of the returns of iShares MSCI index funds using Independent Component Analysis. CSE610 Internal Report, Oregon Graduate Institute of Science and Technology. Retrieved from: http://www.geocities. ws/rr_sergio/Projects/cse610_report.pdfRoss, S.A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory 13 (3): p.p. 341-360. https://doi.org/10.1016/0022-0531(76)90046-6Sayah, M. (2016). Analyzing and Comparing Basel III Sensitivity Based Approach for the Interest Rate Risk in the Trading Book. Applied Finance and Accounting, 2 (1), p.p. 101-118. https://doi.org/10.11114/afa.v2i1.1300Scholz, M. (2006a). Approaches to analyzing and interpret biological profile data. [Unpublished Ph.D. Dissertation]. Postdam University. Retrieved from: https://publishup.uni-potsdam.de/opus4-ubp/frontdoor/deliver/index/docId/696/file/scholz_diss.pdfScholz, M. (2006b). Nonlinear PCA toolbox for Matlab®. Retrieved from: http://www.nlpca.org/matlab. [8 September 2008].Scikit-Learn (2021, July 12). Manifold Learning. https://scikit-learn.org/stable/modules/manifold.html#Wei, Z., Jin, L. & Jin, Y. (2005). Independent Component Analysis. Working Paper. Department of Statistics. Stanford University.Weigang, L., Rodrigues, A. Lihua, S. & Yukuhiro, R. (2007). Nonlinear Principal Component Analysis for withdrawal from the employment time guarantee fund. In: S. Chen, P. Wang & T. Kuo (eds.), Computational Intelligence in Economics and Finance. Vol. II, p.p. 75-92. Berlin: Springer-Verlag. https://doi.org/10.1007/978-3-540-72821-4_4Yip, F. & Xu, L. (2000). An application of independent component analysis in the arbitrage pricing theory. In: S. Amari et al. (eds.) Proceedings of the International Joint Conference on Neural Networks, p.p. 279-284. Los Alamitos: IEEE. https://doi.org/10.1109/IJCNN.2000.861471Rogelio, Salvador Torra Porras, Enric Monte Moreno - 2021info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.https://creativecommons.org/licenses/by-nc-sa/4.0https://revfinypolecon.ucatolica.edu.co/article/view/3740Neural networks principal component analysisIndependent component analysisFactor analysisPrincipal component analysisMexican stock exchangeAnálisis de componentes principales basado en redes neuronalesAnálisis de componentes independientesAnálisis factorialAnálisis de componentes principalesBolsa mexicana de valoresTécnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de ValoresStatistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock ExchangeArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2821https://repository.ucatolica.edu.co/bitstreams/d12169d9-4ca3-49d6-89ba-8fa3633d3328/downloadcd9b8da5bbedc9fa29040c3b2c1f937dMD5110983/29450oai:repository.ucatolica.edu.co:10983/294502023-03-24 17:36:47.105https://creativecommons.org/licenses/by-nc-sa/4.0Rogelio, Salvador Torra Porras, Enric Monte Moreno - 2021https://repository.ucatolica.edu.coRepositorio Institucional Universidad Católica de Colombia - RIUCaCbdigital@metabiblioteca.com

Técnicas estadísticas y computacionales para extraer factores de riesgo sistemático subyacentes: un estudio comparativo en la Bolsa Mexicana de Valores

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