How to cite this paper
Lahmiri, S. (2012). An entropy-LVQ system for S & P500 downward shifts forecasting.Management Science Letters , 2(1), 21-28.
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Atsalakis, G.S., & Valavanis, K.P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36, 5932-5941. #
Bensic, M., Sarlija, N., & Zekic-Susac, M. (2005). Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intelligent Systems in Accounting, Finance and Management, 13, 133-150.#
Boyacioglu, M.A., Kara, Y., & Baykan, O.K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36, 3355-3366.#
Brockett, P.L., Golden, L.L., Jang, J., & Yang. C. (2006). A comparison of neural networks, statistical methods, and variable choice for life insurers’ financial distress prediction. The Journal of Risk and Insurance, 2006, 73 (3), 397-419.#
Chang, P.C., & Liu, C.H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34 (1), 135-144.#
Chang, P.C., Liu, C.H., Lin, J.L., Fan, C.Y., & Ng, C.S.P. (2009). A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 36 (3), 6889-6898.#
Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34 (2), 1004-1017.#
Chen, N., Vieira, A., Ribeiro, B., Duarte, J., & Neves, J. (2011). A stable credit rating model based on learning vector quantization. Intelligent Data Analysis, 15, 237-250. #
Chen, Y., Mabu, S., Shimada, K., & Hirasawa, K. (2009). A genetic network programming with learning approach for enhanced stock trading model. Expert Systems with Applications, 36 (10), 12537-12546.#
Dieterle, F., Muller-Hagedorn, S., Liebich, H.M., & Gauglitz, G. (2003). Urinary nucleosides as potential tumor markers evaluated by learning vector quantization. Artificial Intelligence in Medicine, 28, 265-279.#
El-Banna, M., Filev, D., & Chinnam, R.B. (2008). Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding. The International Journal of Advanced Manufacturing, 36, 237-249.#
Gorgulho, A., Neves, R., & Horta, N. (2011). Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert Systems with Applications, 38, 14072-14085. #
Kohonen, T. (1995). Self-organizing Maps. Springer, Berlin, 1995.#
Kosaka, T., Omatu, S., & Fujinaka, T. (2001). Bill Classification by using The LVQ Method. IEEE International Conference on Systems, Man, and Cybernetics, 3, 1430-1435.#
Lee, W.-L., Hsieh K.-S., & Chen Y.-C. (2004). A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis. Biomedical Engineering Applications, Basis, and Communications, 16, 59-67.#
Majhi, R., Panda, G., & Sahoo, G. (2009). Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications, 36 (3 Part 2), 6800-6808.#
Mala, K., Sadasivam, V., & Alagappan, S. (2006). Neural network based texture analysis of liver tumor from computed tomography images. International Journal of Biological and Life Sciences, 2 (1), 33-40.#
March, J.G., & Shapira, Z. (1982). Behavioral decision theory and organizational decision theory. In G. Ungson and D. Braunstein (Eds.), New Directions in Decision Making, Boston, Mass.: Kent Publishing Co. Reprinted in M. Zey (1992) Decision Making: Alternatives to rational choice models, Newbury Park, CA: Sage Publications.#
Mehrara, M., , Moeini A., Ahrari, M., & Ghafari A. (2010). Using technical analysis with neural network for prediction stock price index in Tehran stock exchange. Middle Eastern Finance and Economics, Euro Journals Publishing, Inc. #
Neves, J.C., & Vieira, A. (2006). Improving bankruptcy prediction with hidden layer learning vector quantization. European Accounting Review ,15 (2), 253-271. #
Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26, 25-33. #
Saad, E.W., Prokhorov, D.V., & Wunsch, D.C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions of Neural Network, 9(6), 1456-1470.#
Sanchez-Marono, N., Alonso-Betanzos, A., & Tombilla-Sanroman, M. (2007). Filter methods for feature selection – A comparative Study. Lecture Notes in Computer Science, 4881, 178-187.#
Schierholt K., & Dagli, C.H. (19960. Stock market prediction using different neural network classification architectures. Proceedings of the IEEE Computational Intelligence for Financial Engineering, 72 -78. #
Specht, D.F. (1990). Probabilistic neural networks, Neural Networks, 3, 109-118.#
Stahlbock, R. (2008). Neural classification approach for short term forecast of exchange rate movement with point and figure charts. IEEE International Joint Conference on Neural Networks, 2841-2848. #
Tan, H., Prokhorov, D.V., & Wunsch, D.C. (1995). Conservative thirty calendar day stock prediction using a probabilistic neural network. In Proceedings of computational intelligence for financial engineering, 113-117.#
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems, 17, 203-222. #
Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm. Expert Systems with Applications, 37 (2), 1015-1022.#
Yao, J., & Herbert, J.P. (2009). Financial time-series analysis with rough sets. Applied Soft Computing, 9 (3), 1000-1007.#
Zhu, S., Wang D., Yu, K., Li, T., & Gong, Y. (2010). Feature selection for gene expression using model-based entropy. IEEE Transactions on Computational Biology and Bioinofrmatics, 7, 25-36. #
Bensic, M., Sarlija, N., & Zekic-Susac, M. (2005). Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intelligent Systems in Accounting, Finance and Management, 13, 133-150.#
Boyacioglu, M.A., Kara, Y., & Baykan, O.K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36, 3355-3366.#
Brockett, P.L., Golden, L.L., Jang, J., & Yang. C. (2006). A comparison of neural networks, statistical methods, and variable choice for life insurers’ financial distress prediction. The Journal of Risk and Insurance, 2006, 73 (3), 397-419.#
Chang, P.C., & Liu, C.H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34 (1), 135-144.#
Chang, P.C., Liu, C.H., Lin, J.L., Fan, C.Y., & Ng, C.S.P. (2009). A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 36 (3), 6889-6898.#
Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34 (2), 1004-1017.#
Chen, N., Vieira, A., Ribeiro, B., Duarte, J., & Neves, J. (2011). A stable credit rating model based on learning vector quantization. Intelligent Data Analysis, 15, 237-250. #
Chen, Y., Mabu, S., Shimada, K., & Hirasawa, K. (2009). A genetic network programming with learning approach for enhanced stock trading model. Expert Systems with Applications, 36 (10), 12537-12546.#
Dieterle, F., Muller-Hagedorn, S., Liebich, H.M., & Gauglitz, G. (2003). Urinary nucleosides as potential tumor markers evaluated by learning vector quantization. Artificial Intelligence in Medicine, 28, 265-279.#
El-Banna, M., Filev, D., & Chinnam, R.B. (2008). Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding. The International Journal of Advanced Manufacturing, 36, 237-249.#
Gorgulho, A., Neves, R., & Horta, N. (2011). Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert Systems with Applications, 38, 14072-14085. #
Kohonen, T. (1995). Self-organizing Maps. Springer, Berlin, 1995.#
Kosaka, T., Omatu, S., & Fujinaka, T. (2001). Bill Classification by using The LVQ Method. IEEE International Conference on Systems, Man, and Cybernetics, 3, 1430-1435.#
Lee, W.-L., Hsieh K.-S., & Chen Y.-C. (2004). A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis. Biomedical Engineering Applications, Basis, and Communications, 16, 59-67.#
Majhi, R., Panda, G., & Sahoo, G. (2009). Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications, 36 (3 Part 2), 6800-6808.#
Mala, K., Sadasivam, V., & Alagappan, S. (2006). Neural network based texture analysis of liver tumor from computed tomography images. International Journal of Biological and Life Sciences, 2 (1), 33-40.#
March, J.G., & Shapira, Z. (1982). Behavioral decision theory and organizational decision theory. In G. Ungson and D. Braunstein (Eds.), New Directions in Decision Making, Boston, Mass.: Kent Publishing Co. Reprinted in M. Zey (1992) Decision Making: Alternatives to rational choice models, Newbury Park, CA: Sage Publications.#
Mehrara, M., , Moeini A., Ahrari, M., & Ghafari A. (2010). Using technical analysis with neural network for prediction stock price index in Tehran stock exchange. Middle Eastern Finance and Economics, Euro Journals Publishing, Inc. #
Neves, J.C., & Vieira, A. (2006). Improving bankruptcy prediction with hidden layer learning vector quantization. European Accounting Review ,15 (2), 253-271. #
Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26, 25-33. #
Saad, E.W., Prokhorov, D.V., & Wunsch, D.C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions of Neural Network, 9(6), 1456-1470.#
Sanchez-Marono, N., Alonso-Betanzos, A., & Tombilla-Sanroman, M. (2007). Filter methods for feature selection – A comparative Study. Lecture Notes in Computer Science, 4881, 178-187.#
Schierholt K., & Dagli, C.H. (19960. Stock market prediction using different neural network classification architectures. Proceedings of the IEEE Computational Intelligence for Financial Engineering, 72 -78. #
Specht, D.F. (1990). Probabilistic neural networks, Neural Networks, 3, 109-118.#
Stahlbock, R. (2008). Neural classification approach for short term forecast of exchange rate movement with point and figure charts. IEEE International Joint Conference on Neural Networks, 2841-2848. #
Tan, H., Prokhorov, D.V., & Wunsch, D.C. (1995). Conservative thirty calendar day stock prediction using a probabilistic neural network. In Proceedings of computational intelligence for financial engineering, 113-117.#
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems, 17, 203-222. #
Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm. Expert Systems with Applications, 37 (2), 1015-1022.#
Yao, J., & Herbert, J.P. (2009). Financial time-series analysis with rough sets. Applied Soft Computing, 9 (3), 1000-1007.#
Zhu, S., Wang D., Yu, K., Li, T., & Gong, Y. (2010). Feature selection for gene expression using model-based entropy. IEEE Transactions on Computational Biology and Bioinofrmatics, 7, 25-36. #