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Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 814769, 11 pages
Stock Price Prediction Based on Procedural Neural Networks
Department of Electrical Engineering, Jiangnan University, Wuxi 214122, China
Received 11 January 2011; Revised 28 March 2011; Accepted 6 April 2011
Academic Editor: Songcan Chen
Copyright © 2011 Jiuzhen Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- H. White, “Economic prediction using neural networks: the case of IBM daily stock returns,” in Proceedings of the IEEE International Conference on Neural Networks, San Diego, Calif, USA, 1988.
- E. W. Saad, D. V. Prokhorov, and D. C. Wunsch II, “Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks,” IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1456–1470, 1998.
- R. J. Kuo, C. H. Chen, and Y. C. Hwang, “An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network,” Fuzzy Sets and Systems, vol. 118, no. 1, pp. 21–45, 2001.
- K.-J. Kim and I. Han, “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” Expert Systems with Applications, vol. 19, no. 2, pp. 125–132, 2000.
- L. Cao and F. E. H. Tay, “Financial forecasting using Support Vector Machines,” Neural Computing and Applications, vol. 10, no. 2, pp. 184–192, 2001.
- R. K. Wolfe, “Turning point identification and Bayesian forecasting of a volatile time series,” Computers and Industrial Engineering, vol. 15, pp. 378–386, 1988.
- M. R. Hassan and B. Nath, “Stock market forecasting using hidden Markov model: a new approach,” in Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, pp. 192–196, Wroclaw, Poland, September 2005.
- Y.-Q. Zhang, S. Akkaladevi, G. Vachtsevanos, and T. Y. Lin, “Granular neural web agents for stock prediction,” Soft Computing, vol. 6, pp. 406–413, 2002.
- W. Dekker, “The fractal geometry of the European eel stock,” ICES Journal of Marine Science, vol. 57, no. 1, pp. 109–121, 2000.
- P. Oswiecimka, J. Kwapien, S. Drozdz, and R. Rak, “Investigating nultifractality of stock market fluctuations using wavelet and detrending fluctuation methods,” Acta Physica Polonica B, vol. 36, pp. 2447–2457, 2005.
- J. Z. Liang, J. Q. Zhou, and X. G. He, “Procedure neural networks with supervised learning,” in Proceedings of the 9th International Conference on Neural Information Processing, pp. 523–527, orchid country club, singapore, November 2002.
- J. Z. Liang and X. G. He, “Function approximation of fuzzy neural network with its research on learning algorithm,” , Ph.D. dissertation, BeiHang University, 2001.
- J. Z. Liang, “Functional procedure neural network,” Dynamic of Continuous Discrete and Impulsive Systems-Series B, pp. 27–31, 2005.
- R. Ball, “The development, accomplishments and limitations of the theory of stock market efficiency,” Managerial Finance, vol. 20, no. 2, pp. 3–48, 1994.
- L. R. Rabiner, “Tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, 1989.
- M. R. Hassan, “A combination of hidden Markov model and fuzzy model for stock market forecasting,” Neurocomputing, vol. 72, no. 16, pp. 3439–3446, 2009.
- L. S. Kuei, W. S. Yun, and T.P. Ling, “Application of hidden Markov switching moving average model in the stock markets: theory and empirical evidence,” International Review of Economics and Finance, vol. 18, no. 2, pp. 306–317, 2009.
- P. Smyth, D. Heckerman, and M. I. Jordan, “Probabilistic independence networks for hidden Markov probability models,” Neural Computation, vol. 9, no. 2, pp. 227–269, 1997.
- R. Elliott, L. Aggoun, and J. Moore, Hidden Markov models: estimation and Control, Springer, Berlin, Germany, 1994.
- R. Elliott and P. Kopp, Mathematics of Financial Markets, Springer, Berlin, Germany, 2004.
- L. Aggoun and R. Elliott, Measure Theory and Filtering: Introduction and Applications, Cambridge University Press, Cambridge, UK, 2004.
- J. Buffington and R. Elliott, “Regime switching and European options,” in Stochastic Theory and Control: Proceedings of a Workshop held in Lawrence, Kansas, B. Pasik-Duncan, Ed., pp. 73–81, Springer, Berlin, Germany, 2002.
- J. Buffington and R. Elliott, “American options with regime switching,” International Journal of Theoretical and Applied Finance, vol. 5, pp. 497–514, 2002.
- L. L. Ghezzi and C. Piccardi, “Stock valuation along a Markov chain,” Applied Mathematics and Computation, vol. 141, no. 2, pp. 385–393, 2003.
- R. Elliott, L. Chan, and T. Siu, “Option pricing and Esscher transform under regime switching,” Annals of Finance, vol. 1, no. 4, pp. 423–432, 2005.
- M. R. Hassan, B. Nath, and M. Kirley, “A fusion model of HMM, ANN and GA for stock market forecasting,” Expert Systems with Applications, vol. 33, no. 1, pp. 171–180, 2007.
- Y. Wang, K. K. Wong, X. F. Liao, and G. Chen, “A new chaos-based fast image encryption algorithm,” Applied Soft Computing, vol. 11, no. 1, pp. 514–522, 2011.
- M. J. Pring, Analisi Tecnica dei Mercati Finanziari, McGraw Hill Italia, Milano, Italy, 2002.
- W. C. Clide and C. L. Osler, “Charting: chaos theory in disguise?” Journal of Futures Markets, vol. 17, no. 5, pp. 489–514, 1997.
- T. Schreimber, “Interdisciplinary application of nonlinear time series methods,” Physics Reports, vol. 308, pp. 1–64, 1998.
- H. O. Peitgen, H. Jurgens, and D. Saupe, Chaos and Fractals: New Frontiers of Science, Springer, Berlin, Germany, 2004.
- D. A. Hsieh, “Chaos and nonlinear dynamics: application to financial markets,” Journal of Finance, vol. 46, pp. 1839–1877, 1991.
- C. Brown, Chaos and Catastrophe Theories, SAGE publications, Thousand Oaks, Calif, USA, 1995.
- R. L. Devaney, Caos e Frattali, Addison-Wesley Published Company, Milano, Italy, 1990.
- “A review of applications in science,” in Gli Oggetti Frattali, B. B. Mandelbrot, Ed., Giulio Einaudi, Milano, Italy, 1987.
- L. Y. Su, “Prediction of multivariate chaotic time series with local polynomial fitting,” Computers and Mathematics with Applications, vol. 59, no. 2, pp. 737–744, 2010.
- L. C. Jain and N. M. Martin, Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications, CRC Press, New York, NY, USA, 1999.
- A. Abraham, B. Nath, and P. K. Mahanti, “Hybrid intelligent systems for stock market analysis,” in Proceedings of the International Conference on Computational Science, V. N. Alexandrov, et al., Ed., pp. 337–345, Springer, San Francisco, Calif, USA, May 2001.
- D. Y. Chiu and P. J. Chen, “Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm,” Expert Systems with Applications, vol. 36, no. 2, pp. 1240–1248, 2009.
- L. A. Zadeh, in Foreword of the Proceedings of the Second International Conference on Fuzzy Logic and Neural Networks, pp. xiii–xiv, Iizuka, Japan, 1992.
- M. M. Mostafa, “Forecasting stock exchange movements using neural networks: empirical evidence from Kuwait,” Expert Systems with Applications, vol. 37, no. 9, pp. 6302–6309, 2010.
- S. Duerson, F. S. Khan, V. Kovalev, and A. H. Malik, “Reinforcement learning in online stock trading systems,” http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.5299.
- W. Huang, Y. Nakamori, and S.-Y. Wang, “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research, vol. 32, no. 10, pp. 2513–2522, 2005.
- J. Z. Liang, “Support functional machines,” in Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning, pp. 1–9, Springer, Birmingham, UK, December 2007, LNCS4881.
- X. G. He and J. Z. Liang, “Procedure neural networks,” in Proceedings of Conference on Intelligent Information Processing, Z. Shi, Ed., 16th World Computer Congress 2000, pp. 143–146, Publishing House of Electronics Industry, China, 2000.
- X. G. He and J. Z. Liang, “Some theoretic problems of procedure neural network,” Engineering Science in China, vol. 2, no. 12, pp. 40–44, 2000.
- X. G. He, J. Z. Liang, and S. H. Xu, “Training and application of procedure neural network,” Engineering Science in China, vol. 3, no. 4, pp. 31–45, 2001.
- J. Z. Liang and J. M. Han, “Complex number procedure neural networks,” in Proceedings of the 1st International Conference on Natural Computation, pp. 336–339, Springer, Changsha, China, August 2005, Part I, LNCS3610.
- J. Z. Liang and X. H. Wu, “Segment procedure neural networks,” in Proceedings of the IEEE International Conference on Granular Computing, pp. 526–529, Beijing, China, July 2005.
- 2007, http://finance.yahoo.com/q/hp?s=GE&a=00&b=1&c=2007&d=06&e=26&f=2007&g=d.