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

المصدر: https://revfinypolecon.ucatolica.edu.co/article/view/3740Test.

مصطلحات موضوعية: 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

وصف الملف: text/html; application/pdf; text/xml

العلاقة: https://revfinypolecon.ucatolica.edu.co/article/download/3740/4018Test; https://revfinypolecon.ucatolica.edu.co/article/download/3740/3933Test; https://revfinypolecon.ucatolica.edu.co/article/download/3740/4253Test; Núm. 2 , Año 2021 : Vol. 13 Núm. 2 (2021); 543; 513; 13; Revista Finanzas y Política Económica; 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.2021.100378Test; 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.2020.01.005Test; 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/S0129065797000458Test; 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/S0219024903002201Test; 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.2016.02.006Test; 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.2005.1491117Test; 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.2017.11.023Test; 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/t0192Test; 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.shtmlTest [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/2117/131355Test; 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.2004.1380933Test; 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-23-2-3193Test; 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-22-4-3083Test; 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/S0186-1042Test(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.1999.771118Test; 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/10.1155/2011/382659Test; 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.xTest; 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.2019.01.012Test; 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.pdfTest; 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.geocitiesTest. 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/10.1016/0022-0531Test(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.v2i1.1300Test; Scholz, M. (2006a). Approaches to analyzing and interpret biological profile data. [Unpublished Ph.D. Dissertation]. Postdam University. Retrieved from: https://publishup.uni-potsdamTest.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/matlabTest. [8 September 2008].; Scikit-Learn (2021, July 12). Manifold Learning. https://scikit-learn.org/stable/modules/manifold.htmlTest#; 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_4Test; 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.2000.861471Test; https://hdl.handle.net/10983/29450Test; https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9Test

الإتاحة: https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9Test
https://doi.org/10.1016/j.inffus.2020.01.005Test
https://doi.org/10.1016/j.eswa.2016.02.006Test
https://doi.org/10.1016/j.eswa.2019.01.012Test
https://doi.org/10.1016/0022-0531Test(76)90046-6
https://doi.org/10.11114/afa.v2i1.1300Test
https://hdl.handle.net/10983/29450Test

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

المصدر: Revista Finanzas y Política Económica; Vol. 13 No. 2 (2021); 513-543 ; Revista Finanzas y Política Económica; Vol. 13 Núm. 2 (2021); 513-543 ; Revista Finanzas y Política Económica; v. 13 n. 2 (2021); 513-543 ; 2011-7663 ; 2248-6046 ; 10.14718/revfinanzpolitecon.v13.n2.2021

مصطلحات موضوعية: 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

وصف الملف: text/html; application/pdf

العلاقة: https://revfinypolecon.ucatolica.edu.co/article/view/3740/4018Test; https://revfinypolecon.ucatolica.edu.co/article/view/3740/3933Test; https://revfinypolecon.ucatolica.edu.co/article/view/3740Test

الإتاحة: https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9Test
https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021Test
https://revfinypolecon.ucatolica.edu.co/article/view/3740Test

المؤلفون: Salvador Torra Porras, Rogelio, Enric Monte Moreno

المساهمون: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla

المصدر: Revista Finanzas y Política Económica, Volume: 13, Issue: 2, Pages: 513-543, Published: 12 APR 2022

مصطلحات موضوعية: Economics and Econometrics, Análisis de Componentes Principales basado en Redes Neuronales, Computer science, Feature extraction, Principal component analysis, Independent component analysis, Neural networks (Computer science), Informàtica::Aplicacions de la informàtica [Àrees temàtiques de la UPC], Empirical research, Stock exchange, Economia i organització d'empreses [Àrees temàtiques de la UPC], Systematic risk, Arbitrage pricing theory, Econometrics, Xarxes neuronals (Informàtica), Neural networks principal component analysis, Análisis factorial, Dimensionality reduction, Análisis de componentes principales, Bolsa mexicana de valores, Análisis de componentes principales basado redes neuronales, Stock exchanges, Análisis de componentes independientes, Mexican stock exchange, Factor analysis, Finance, Borsa de valors

وصف الملف: application/pdf; text/html

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9418d5c8becde871113508e54c742eeTest
https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9Test

المؤلفون: Ladrón de Guevara Cortés, Rogelio, Torra Porras, Salvador, Monte Moreno, Enrique|||0000-0002-4907-0494

المصدر: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

مصطلحات موضوعية: Análisis de componentes principales, Principal component analysis, Independent component analysis, Bolsa mexicana de valores, Análisis de componentes principales basado redes neuronales, Stock exchanges, Neural networks (Computer science), Informàtica::Aplicacions de la informàtica [Àrees temàtiques de la UPC], Análisis de componentes independientes, Análisis de componentes principales basado en redes neuronales, Mexican stock exchange, Economia i organització d'empreses [Àrees temàtiques de la UPC], Xarxes neuronals (Informàtica), Factor analysis, Neural networks principal component analysis, Borsa de valors, Análisis factorial

وصف الملف: text/html; application/pdf; text/xml

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a310b98fa9590f33932a1bfd51991999Test
https://hdl.handle.net/10983/29450Test

المؤلفون: Ladrón de Guevara Cortés, Rogelio, Torra Porras, Salvador, Monte, Enric

المصدر: Revista Finanzas y Política Económica, ISSN 2248-6046, Vol. 13, Nº. 2, 2021, pags. 513-543

مصطلحات موضوعية: 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

وصف الملف: application/pdf

العلاقة: https://dialnet.unirioja.es/servlet/oaiart?codigo=8049444Test; (Revista) ISSN 2248-6046

الإتاحة: https://dialnet.unirioja.es/servlet/oaiart?codigo=8049444Test

مصطلحات الفهرس: 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, Artículo de revista

URL: https://hdl.handle.net/10983/29450Test
https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4018Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/3933Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4253Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4018Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/3933Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4253Test
Núm. 2 , Año 2021 : Vol. 13 Núm. 2 (2021)
543
2
513
13
Revista Finanzas y Política Económica
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.2021.100378Test
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.2020.01.005Test
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/S0129065797000458Test
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/S0219024903002201Test
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.2016.02.006Test
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.2005.1491117Test
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.2017.11.023Test
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/t0192Test
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.shtmlTest [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/2117/131355Test
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.2004.1380933Test
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-23-2-3193Test
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-22-4-3083Test
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/S0186-1042Test(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.1999.771118Test
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/10.1155/2011/382659Test
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.xTest
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.2019.01.012Test
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.pdfTest
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.geocitiesTest. 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/10.1016/0022-0531Test(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.v2i1.1300Test
Scholz, M. (2006a). Approaches to analyzing and interpret biological profile data. [Unpublished Ph.D. Dissertation]. Postdam University. Retrieved from: https://publishup.uni-potsdamTest.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/matlabTest. [8 September 2008].
Scikit-Learn (2021, July 12). Manifold Learning. https://scikit-learn.org/stable/modules/manifold.htmlTest#
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_4Test
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.2000.861471Test

مصطلحات الفهرس: 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, Artículo de revista

URL: https://hdl.handle.net/10983/29450Test
https://doi.org/10.14718/revfinanzpolitecon.v13.n2.2021.9Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4018Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/3933Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4253Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4018Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/3933Test
https://revfinypolecon.ucatolica.edu.co/article/download/3740/4253Test
Núm. 2 , Año 2021 : Vol. 13 Núm. 2 (2021)
543
2
513
13
Revista Finanzas y Política Económica
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.2021.100378Test
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.2020.01.005Test
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/S0129065797000458Test
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/S0219024903002201Test
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.2016.02.006Test
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.2005.1491117Test
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.2017.11.023Test
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/t0192Test
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.shtmlTest [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/2117/131355Test
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.2004.1380933Test
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المصدر: Revista Finanzas y Política Económica, ISSN 2248-6046, Vol. 13, Nº. 2, 2021, pags. 513-543

مصطلحات الفهرس: 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, text (article)

URL: https://dialnet.unirioja.es/servlet/oaiart?codigo=8049444Test

المصدر: Revista Finanzas y Política Económica, ISSN 2248-6046, Vol. 13, Nº. 2, 2021, pags. 513-543

مصطلحات الفهرس: 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, text (article)

URL: https://dialnet.unirioja.es/servlet/oaiart?codigo=8049444Test