Abstract
Financial time series forecast has been classified as standard problem in forecasting due to its high non-linearity and high volatility in data. Statistical methods such as GARCH, GJR, EGARCH and Artificial Neural Networks (ANNs) based on standard learning algorithms such as backpropagation have been widely used for forecasting time series volatility of various fields. In this paper, we propose hybrid model of statistical methods with ANNs. Statistical methods require assumptions about the market, they do not reflect all market variables and they may not capture the non-linearity. Shortcoming of ANNs is their process of identifying inputs insignificantly through which network produces output. The attempt for hybrid system is to outperform the forecast results and overcome the shortcomings by extracting input variables from statistical methods and include them in ANNs learning process. Further genetic algorithm is used for evolution of proposed hybrid models. Experimental results confirm the lesser root mean square error (RMSE) results obtained from proposed evolutionary hybrid ANN models EANN-GARCH, EANN-GJR, EANN-EGARCH than conventional ANNs and statistical methods.
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References
Yixin, Z., Zhang, J.: Stock Data Analysis Based on BP Neural Network. In: Second International Conference on Communication Software and Networks, ICCSN, pp. 396–399 (2010)
Marzi, H., Turnbull, M., Marzi, E.: Use of neural networks in forecasting financial market. In: IEEE Conference on Soft Computing in Industrial Applications, SMCia 2008, June 25-27, pp. 240–245 (2008)
Eng, M.H., Li, Y., Wang, Q.-G., Lee, T.H.: Forecast Forex with ANN Using Fundamental Data. In: Information Management, Innovation Management and Industrial Engineering, ICIII 2008, December 19-21, vol. 1, pp. 279–282 (2008)
Ram Kumar, P., Ramana Murthy, M.V., Eashwar, D., Venkatdas, M.: Time Series Modeling using Artificial Neural Networks. Journal of Theoretical and Applied Information Technology, JATIT 4(12), 1259–1264 (2005)
Zhao, Z., Xin, H., Ren, Y., Guo, X.: Application and Comparison of BP Neural Network Algorithm in MATLAB. In: International Conference on Measuring Technology and Mechatronics Automation 2010, March 13-14, vol. 1, pp. 590–593 (2010)
Zhao, H., Zhou, Y.: A joint model of chaos and GARCH effect in China’s stock markets. In: 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS), December 19-20, vol. 3, pp. 220–223 (2009)
Ning, L., Dong-ping, H.: Emipirical Research on Term Structure of Buy-Back Rates Based on EGARCH Model. In: Second International Symposium on Electronic Commerce and Security, ISECS 2009, May 22-24, vol. 1, pp. 112–115 (2009)
Etemadi, H., Rostamy, A.A.A., Dehkordi, H.F.: A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications 36(2), part 2, 3199–3207 (2009)
Yang, C.-X., Zhu, Y.-F.: Using genetic algorithms for time series prediction. In: Sixth International Conference on Natural Computation (ICNC), August 10-12, vol. 8, pp. 4405–4409 (2010)
Shukla, A., Tiwari, R., Kala, R.: Real Life Application of Soft Computing. CRC Press, Boca Raton (2010)
Lee, T.-L.: Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor. Engineering Applications of Artificial Intelligence 21(1), 63–67 (2008)
Shopova, E.G., Vaklieva-Bancheva, N.G.: BASIC–A genetic algorithm for engineering problems solution. Computers & Chemical Engineering 30(8), 1293–1309 (2006)
Altunkaynak, A.: Sediment load prediction by genetic algorithms. Advances in Engineering Software 40(9), 928–934 (2009)
Basrak, B., Davis, R.A., Mikosch, T.: Regular variation of GARCH processes. Stochastic Processes and their Applications 99(1), 95–115 (2002)
Wang, J., Wu, X., Zhong, M.: Empirical Analysis of the Market Risk of Chinese Open-Ended Funds Based on GARCH-VaR Models. In: International Joint Conference on Computational Sciences and Optimization, April 24-26, vol. 1, pp. 979–982 (2009)
Shamiri, A., Hassan, A.: Modeling and Forecasting Volatility of the Malaysian and the Singaporean stock indices using Asymmetric GARCH models and Non-normal Densities. Econometrics 0509015, EconWPA (2005)
Hipel, McLeod: Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam (1994)
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Tarsauliya, A., Kala, R., Tiwari, R., Shukla, A. (2011). Financial Time Series Volatility Forecast Using Evolutionary Hybrid Artificial Neural Network. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Network Security and Applications. CNSA 2011. Communications in Computer and Information Science, vol 196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22540-6_44
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DOI: https://doi.org/10.1007/978-3-642-22540-6_44
Publisher Name: Springer, Berlin, Heidelberg
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