Svm in Financial Time Series Forecasting

Neurocomputing 55 (2003) 307 – 319 www.elsevier.com/locate/neucom

Financial time series forecasting using support vector machines
Kyoung-jae Kim∗
Department of Information Systems, College of Business Administration, Dongguk University, 3-26, Pil-dong, Chung-gu, Seoul 100715, South Korea Received 28 February 2002; accepted 13 March 2003

Abstract Support vector machines (SVMs) are promising methods for the prediction of ÿnancial timeseries because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in ÿnancial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction. c 2003 Elsevier B.V. All rights reserved.
Keywords: Support vector machines; Back-propagation neural networks; Case-based reasoning; Financial time series

1. Introduction Stock market prediction is regarded as a challenging task of ÿnancial time-series prediction. There have been many studies using artiÿcial neural networks (ANNs) in this area. A large number of successful applications have shown that ANN can be a very useful tool for time-series modeling and forecasting [24]. The early days of these studies focused on application of ANNs to stock market prediction (for instance [2,6,11,13,19,23]). Recent research tends to hybridize several artiÿcial intelligence (AI) techniques (for instance [10,22]). Some researchers tend to include novel factors in the learning process. Kohara et al. [14] incorporated prior knowledge to improve the
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0925-2312/03/$ - see front matter c 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0925-2312(03)00372-2

References: [1] S.B. Achelis, Technical Analysis from A to Z, Probus Publishing, Chicago, 1995. [2] H. Ahmadi, Testability of the arbitrage pricing theory by neural networks, in: Proceedings of the International Conference on Neural Networks, San Diego, CA, 1990, pp. 385 –393. [3] J. Chang, Y. Jung, K. Yeon, J. Jun, D. Shin, H. Kim, Technical Indicators and Analysis Methods, Jinritamgu Publishing, Seoul, 1996. [4] C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, 2001, Available at http://www.csie.edu.tw/∼cjlin/papers/libsvm.pdf. [5] J. Choi, Technical Indicators, Jinritamgu Publishing, Seoul, 1995. [6] J.H. Choi, M.K. Lee, M.W. Rhee, Trading S& P 500 stock index futures using a neural network, in: Proceedings of the Annual International Conference on Artiÿcial Intelligence Applications on Wall Street, New York, 1995, pp. 63–72. [7] D.R. Cooper, C.W. Emory, Business Research Methods, Irwin, Chicago, 1995. [8] H. Drucker, D. Wu, V.N. Vapnik, Support vector machines for spam categorization, IEEE Trans. Neural Networks 10 (5) (1999) 1048–1054. [9] E. Gi ord, Investor’s Guide to Technical Analysis: Predicting Price Action in the Markets, Pitman Publishing, London, 1995. [10] Y. Hiemstra, Modeling structured nonlinear knowledge to predict stock market returns, in: R.R. Trippi (Ed.), Chaos & Nonlinear Dynamics in the Financial Markets: Theory, Evidence and Applications, Irwin, Chicago, IL, 1995, pp. 163–175. [11] K. Kamijo, T. Tanigawa, Stock price pattern recognition: a recurrent neural network approach, in: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, 1990, pp. 215 –221. K.-j. Kim / Neurocomputing 55 (2003) 307 – 319 319 [12] K. Kim, I. Han, Genetic algorithms approach to feature discretization in artiÿcial neural networks for the prediction of stock price index, Expert Syst. Appl. 19 (2) (2000) 125–132. [13] T. Kimoto, K. Asakawa, M. Yoda, M. Takeoka, Stock market prediction system with modular neural network, in: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, 1990, pp. 1– 6. [14] K. Kohara, T. Ishikawa, Y. Fukuhara, Y. Nakamura, Stock price prediction using prior knowledge and neural networks, Int. J. Intell. Syst. Accounting Finance Manage. 6 (1) (1997) 11–22. [15] S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using support vector machines, in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, FL, 1997, pp. 511–520. [16] J.J. Murphy, Technical Analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications, Prentice-Hall, New York, 1986. [17] T.-S. Quah, B. Srinivasan, Improving returns on stock investment through neural network selection, Expert Syst. Appl. 17 (1999) 295–301. [18] F.E.H. Tay, L. Cao, Application of support vector machines in ÿnancial time series forecasting, Omega 29 (2001) 309–317. [19] R.R. Trippi, D. DeSieno, Trading equity index futures with a neural network, J. Portfolio Manage. 19 (1992) 27–33. [20] R. Tsaih, Y. Hsu, C.C. Lai, Forecasting S& P 500 stock index futures with a hybrid AI system, Decision Support Syst. 23 (2) (1998) 161–174. [21] V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998. [22] I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, San Francisco, CA, 2000. [23] Y. Yoon, G. Swales, Predicting stock price performance: a neural network approach, in: Proceedings of the 24th Annual Hawaii International Conference on System Sciences, Hawaii, 1991, pp. 156 –162. [24] G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artiÿcial neural networks: the state of the art, Int. J. Forecasting 14 (1998) 35–62. Kyoung-jae Kim received his M.S. and Ph.D. degrees in Management Information Systems from the Graduate School of Management at the Korea Advanced Institute of Science and Technology and his B.A. degree from the Chung-Ang University. He is currently a faculty member of the Department of Information Systems at the Dongguk University. His research interests include data mining, knowledge management, and intelligent agents.