Abstract
We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Afolabi, M.O., Olude, O.: Predicting stock prices using a hybrid kohonen self organizing map (som). In: Proc. of the 40th Annual Hawaii International Conference on System Sciences, p. 48. IEEE Press, New York (2007)
Ao, S.I.: Automating stock prediction with neural network and evolutionary computation. In: Liu, J., Cheung, Y., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 203–210. Springer, Heidelberg (2003)
Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural networks. In: IJCNN: International Joint Conference on Neural Networks, pp. 1–6. IEEE Press, New York (1990)
Chang, P.C., Liu, C.H.: A tsk type fuzzy rule based system for stock price prediction. Expert Syst. Appl. 34, 135–144 (2008)
Fu, J., Lum, K.S., Nguyen, M.N., Shi, J.: Stock prediction using fcmac-byy. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 346–351. Springer, Heidelberg (2007)
Lin, G.F., Wanga, C.M.: Performing cluster analysis and discrimination analysis of hydrological factors in one step. Adv. Water Resour. 29, 1573–1585 (2006)
Huang, W., Wang, S., Yu, L., Bao, Y., Wang, L.: A new computational method of input selection for stock market forecasting with neural networks. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3994, pp. 308–315. Springer, Heidelberg (2006)
Kaboudan, M.A.: Genetic programming prediction of stock prices. Comput. Econ. 16, 207–236 (2000)
Kim, K.j., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications 19, 125–132 (2000)
Kwon, Y.K., Choi, S.S., Moon, B.R.: Stock prediction based on financial correlation. In: Proc. of Genetic and Evolutionary Computation Conference, pp. 2061–2066. ACM, Washington (2005)
Kwon, Y.K., Moon, B.R.: Daily stock prediction using neuro-genetic hybrids. In: Proc. of Genetic and Evolutionary Computation Conference, pp. 2203–2214. ACM, Washington (2003)
Kwon, Y.K., Moon, B.R.: Evolutionary ensemble for stock prediction. In: Proc. of Genetic and Evolutionary Computation Conference, pp. 1102–1113. ACM, Washington (2004)
Liao, P.Y., Chen, J.S.: Dynamic trading strategy learning model using learning classifier systems. In: Proc. of the 2001 IEEE Congress on Evolutionary Computation, pp. 783–789. IEEE Press, New York (2001)
Lu, H., Han, J., Feng, L.: Stock movement prediction and n-dimensional inter-transaction association rule. In: Proc. of the ACM SIGMOD Workshop on IB Issues on Data Mining and Knowledge Discovery, pp. 1–7. ACM, Washington (1998)
Hall, M.A., Smith, L.A.: Feature subset selection: a correlation based filter approach. In: Proc. of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pp. 855–858. Springer, Heidelberg (1997)
Sugunsil, P., Somhom, S.: Short term stock prediction using som. In: Yang, J., et al. (eds.) UNISCON 2009. LNBIP, vol. 20, pp. 262–267. Springer, Heidelberg (2009)
Tino, P., Schittenkopf, C., Dorffner, G.: Financial Volatility Trading using Recurrent Neural Networks. In: Proc. of IEEE Transactions on Neural Networks, pp. 865–874. IEEE Press, New York (2001)
Wang, Y.F.: Predicting stock price using fuzzy grey prediction system. Expert Syst. Appl. 22, 33–38 (2002)
Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: Feature selection for support vector machines. In: Advances in Neural Information Processing Systems, vol. 13, pp. 668–674. MIT Press, Cambridge (2001)
Wolfe, K.R.: Turning point identification and bayesian forecasting of a volatile time series. Comput. Ind. Eng. 15, 378–386 (1988)
Zhang, G.P.: Neural Networks in Business Forecasting. Information Resources Press, VA (2003)
Zorin, A.V.: Stock Price Prediction: Kohonen Versus Backpropagation. In: Proceeding of International Conference on Modelling and Simulation of Business Systems, pp. 115–119. IEEE Press, New York (2003)
LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Advances in Neural Information Processing Systems, pp. 598–605. Morgan Kaufmann, San Francisco (1989)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Lendasse, A., Verleysen, M., Bodt, E.: Forecasting Time-Series by Kohonen Classification. In: Proceedings of European Symposium on Artificial Neural Networks, pp. 221–226. D-Facto public., Bruges (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sugunnasil, P., Somhom, S. (2010). Feature Selection for Neural Network Based Stock Prediction. In: Papasratorn, B., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds) Advances in Information Technology. IAIT 2010. Communications in Computer and Information Science, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16699-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-16699-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16698-3
Online ISBN: 978-3-642-16699-0
eBook Packages: Computer ScienceComputer Science (R0)