Analysis of day-effect in the Colombian stock market using self-organizing maps
Keywords:
Colombian stock market, daily return, fortnight, self-organizing maps
Abstract
In this article is presented a model of Kohonen’s Self-Organizing Map (SOM), used to find a relation between weekday in the first and the second fortnight with the daily return of index COLCAP, the reference index of Colombian stock market. In addition it is described data used, the configuration used in the SOM and its training results. Using visualizations by SOM components it is revealed graphically, regarding weekday on each fortnight, the existing predominances in the return value of COLCAP index.
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References
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[7] C. Hensel, W. Ziemba, “Investment Results from Exploiting Turn-of-the-Month Effects,” Journal of Portfolio Management, vol. 22, no. 3, pp. 43-45, 1996.
[8] J. Ogden, “Turn-of-Month Evaluations of Liquid Profits and Stock Returns: A Common Explanation for the Monthly and January Effects,” The Journal of Finance, vol. 45, no. 4, pp. 1259-1272, 1990.
[9] R. Ariel, “A monthly effect in stock returns,” Journal of Financial Economics, vol. 18, pp. 161-174, 1987.
[10] J. F. Jaffe, R. Westerfield, C. Ma, “A twist on the monday effect in stock prices: Evidence from the U.S. and foreign stock markets,” Journal of Banking & Finance, vol. 13, pp. 641-650, 1989.
[11] T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no.9, pp. 1464-1480, 1990.
[12] T. Kohonen, “The self-organizing map,” Neurocomputing, vol. 21, 1998.
[13] T. Kohonen, “Essentials of the self-organizing map,” Neural Networks, vol. 37, pp. 52-65, 2013.
[14] G. Barreto, “Time Series Prediction with the Self-Organizing Map: A Review,” Perspectives on Neural-Symbolic Integration, vol. 77, pp.135-158, 2007.
[15] B. Silva, N. Marques, “Clustering stock markets values with a Self-Organized featured Map,” en 15th Portuguese Conference on Artificial Intelligence, 2011, pp. 520-534.
[16] B. Silva, N. Marques, “Feature clustering with self-organizing maps and an application to financial time-series portfolio selection,” en International Conference on Neural Computation, 2010, pp. 301-309.
[17] G. Forero, “Contrastación de paradigmas de las finanzas: Normalidad e hipótesis del mercado eficiente. Aplicaciones en MATLAB,” Observatorio de Economía y Operaciones Numéricas, vol. 5, pp. 167-227, 2011.
[18] E. Caicedo, J. López, Una aproximación práctica a las redes neuronales artificiales. Cali, Colombia: Programa Editorial Universidad del Valle, 2009.
[19] G. Deboeck, “Financial applications of Self-Organizing Maps,” American Heuristics Electronic Newsletter, 1998.
[20] A. Engelbrecht, Computational Intelligence: An introduction, 2da edición. Chichester, Inglaterra: Wiley Publishing, 2007, p. 71.
[21] B. Martín Del Brío, A. Sanz, Redes neuronales y sistemas borrosos, 3ra edición. México D.F., México: Alfaomega, 2007.
[22] T. Eklund, B. Back, H. Vanharanta, A. Visa, “Assessing the feasibility of self-organizing maps for data mining financial information,” en Proceedings of the Xth European Conference on Information Systems, Gdansk, Polonia, 2002, pp. 528-537.
[23] Bolsa de Valores de Colombia, Metodología para el cálculo del índice COLCAP, abril de 2013.
[24] Bolsa de Valores de Colombia, “Índices”. [Online]. Available: www.bvc.com.co
[25] J. Vesanto, J. Himberg, E. Alhoniemi, J. Parhankangas, SOM toolbox for Matlab, Helsinki University of Technology, Helsinki, Finlandia, 2000.
[26] Universidad Tecnológica de Helsinki, Laboratorio de Computación y Ciencia de la Información, “SOM Toolbox”, 2005. [Online]. Available: www.cis.hut.fi/projects/somtoolbox/download/
[2] H. Kiymaz, H. Berument, “The day of the week effect on stock market volatility and volume: International evidence,” Review of Financial Economics, vol. 13, pp. 363-380, 2003.
[3] J. Villalobos, J. Mendoza, “Efecto día en el mercado accionario colombiano: Una aproximación no paramétrica,” Borradores de economía, Banco de la República, no. 585, pp. 1-18, 2010.
[4] A. Montenegro, “El efecto día en la Bolsa de Valores de Colombia,” Documentos de Economía, Pontificia Universidad Javeriana, Facultad de Ciencias Económicas y Administrativas, no. 9, 2007.
[5] D. Rivera, “Modelación del efecto del día de la semana para los índices accionarios de Colombia mediante un modelo STAR GARCH,” Revista de economía del Rosario, vol. 12, no. 1, pp. 1-24, 2009.
[6] C. Bahi, Modelos de medición de la volatilidad en los mercados de valores: Aplicación al mercado bursátil argentino, Universidad Nacional de Cuyo, Facultad de Ciencias Económicas, 2007.
[7] C. Hensel, W. Ziemba, “Investment Results from Exploiting Turn-of-the-Month Effects,” Journal of Portfolio Management, vol. 22, no. 3, pp. 43-45, 1996.
[8] J. Ogden, “Turn-of-Month Evaluations of Liquid Profits and Stock Returns: A Common Explanation for the Monthly and January Effects,” The Journal of Finance, vol. 45, no. 4, pp. 1259-1272, 1990.
[9] R. Ariel, “A monthly effect in stock returns,” Journal of Financial Economics, vol. 18, pp. 161-174, 1987.
[10] J. F. Jaffe, R. Westerfield, C. Ma, “A twist on the monday effect in stock prices: Evidence from the U.S. and foreign stock markets,” Journal of Banking & Finance, vol. 13, pp. 641-650, 1989.
[11] T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no.9, pp. 1464-1480, 1990.
[12] T. Kohonen, “The self-organizing map,” Neurocomputing, vol. 21, 1998.
[13] T. Kohonen, “Essentials of the self-organizing map,” Neural Networks, vol. 37, pp. 52-65, 2013.
[14] G. Barreto, “Time Series Prediction with the Self-Organizing Map: A Review,” Perspectives on Neural-Symbolic Integration, vol. 77, pp.135-158, 2007.
[15] B. Silva, N. Marques, “Clustering stock markets values with a Self-Organized featured Map,” en 15th Portuguese Conference on Artificial Intelligence, 2011, pp. 520-534.
[16] B. Silva, N. Marques, “Feature clustering with self-organizing maps and an application to financial time-series portfolio selection,” en International Conference on Neural Computation, 2010, pp. 301-309.
[17] G. Forero, “Contrastación de paradigmas de las finanzas: Normalidad e hipótesis del mercado eficiente. Aplicaciones en MATLAB,” Observatorio de Economía y Operaciones Numéricas, vol. 5, pp. 167-227, 2011.
[18] E. Caicedo, J. López, Una aproximación práctica a las redes neuronales artificiales. Cali, Colombia: Programa Editorial Universidad del Valle, 2009.
[19] G. Deboeck, “Financial applications of Self-Organizing Maps,” American Heuristics Electronic Newsletter, 1998.
[20] A. Engelbrecht, Computational Intelligence: An introduction, 2da edición. Chichester, Inglaterra: Wiley Publishing, 2007, p. 71.
[21] B. Martín Del Brío, A. Sanz, Redes neuronales y sistemas borrosos, 3ra edición. México D.F., México: Alfaomega, 2007.
[22] T. Eklund, B. Back, H. Vanharanta, A. Visa, “Assessing the feasibility of self-organizing maps for data mining financial information,” en Proceedings of the Xth European Conference on Information Systems, Gdansk, Polonia, 2002, pp. 528-537.
[23] Bolsa de Valores de Colombia, Metodología para el cálculo del índice COLCAP, abril de 2013.
[24] Bolsa de Valores de Colombia, “Índices”. [Online]. Available: www.bvc.com.co
[25] J. Vesanto, J. Himberg, E. Alhoniemi, J. Parhankangas, SOM toolbox for Matlab, Helsinki University of Technology, Helsinki, Finlandia, 2000.
[26] Universidad Tecnológica de Helsinki, Laboratorio de Computación y Ciencia de la Información, “SOM Toolbox”, 2005. [Online]. Available: www.cis.hut.fi/projects/somtoolbox/download/
Published
2015-06-16
How to Cite
Peña-Cuéllar, D., Ortiz-Sandoval, J., & Espitia-Cuchango, H. (2015). Analysis of day-effect in the Colombian stock market using self-organizing maps. ITECKNE, 12(1), 84-94. https://doi.org/https://doi.org/10.15332/iteckne.v12i1.825
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Case Study
