Abstract
In this paper, a new scheme based on chaotic neural network for stock index prediction is proposed. The data from a Chinese stock market, Shenzhen stock market, are applied as a case study. The chaotic neural network is used to learn the non-linear stochastic and chaotic patterns in the stock system and forecast a new index with former indexes. The validity of the scheme is analyzed theoretically, and the simulation results show that it has a good performance.
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References
Yao, J.T., Tan, C.L.: Time dependent directional profit model for financial time series forecasting. In: Proceeding of the IJCNN, Como, vol. 5, pp. 291–296 (2000)
Khoa, N.L.D., Sakakikara, K., Nishikawa, I.: Stock price forecasting using back propagation neural networks with time and profit based adjusted weight factors. In: SICE-ICASE International Joint Conference 2006, Busan, pp. 5484–5488 (2006)
Quah, T.S.: DJIA stock selection assisted by neural network. Expert Systems with Applications 35, 50–58 (2008)
Zhang, L., Zhong, C.Q., Zhang, L.Y., Ma, F., Zhang, L.: Application of innovations feedback neural networks in the prediction of ups and downs balue of stock market. In: Proceedings of the 6th World Congress on Intelligent Controland Automation, Dalian, vol. 1, pp. 4162–4166 (2006)
Tseng, C.H., Cheng, S.T., Wang, Y.H., Peng, J.T.: Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices. Physica A 387, 3192–3200 (2008)
Chen, Y.H., Yang, B., Abraham, A.: Flexible neural trees ensemble for stock index modeling. Neurocomputing 70, 697–703 (2007)
Adachi, M., Aihara, K.: Associative dynamics in a chaotic neural network. Neural Networks 10, 83–98 (1997)
Aihara, K., Takabe, T., Toyoda, M.: Chaotic neural networks. Physics Letters A 144, 333–340 (1990)
Ren, Q.S., Wang, J., Meng, H.L., Zhao, J.Y.: An adaptive radar target signal processing scheme based on AMTI filter and chaotic neural networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 88–95. Springer, Heidelberg (2007)
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Ning, B., Wu, J., Peng, H., Zhao, J. (2009). Using Chaotic Neural Network to Forecast Stock Index. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_98
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DOI: https://doi.org/10.1007/978-3-642-01507-6_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
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