ISSN: 2225-8329
Open access
Stock forecasting is an important and interesting topic in time series forecasting. Accurate stock price is regard as a challenging task of the financial time forecasting process. However, there are three major drawbacks in stock market by traditional time-series model (1) some models can not be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance; (3) Subjective selected technical indicators as input variable by personal experience would lead to lower forecasting accuracy. For solving above problems, this paper proposes a hybrid time-series support vector regression (SVR) model based on Empirical mode decomposition (EMD) and technical indicators feature selection to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In verification, this paper employs nine year period of TAIEX stock index, from 1997 to 2005, as experimental datasets, and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics. 31, 307-327.
Box, G., Jenkins, G. (1976). Time series analysis: Forecasting and control, San Francisco: Holden-Day.
Chen, T. L., Cheng, C. H., Teoh, H. J. (2008). High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets, Physica A. 387, 876-888
Chen, S. M. (1996). Forecasting enrollments based on fuzzy time-series, Fuzzy Sets Systems, 81, 311-319.
Chen, S. M., Chung, N. Y. (2006). Forecasting Enrollments Using High-Order Fuzzy Time Series and Genetic Algorithms. International of Intelligent Systems. 21, 485-501.
Cheng, C. H., Chen, T. L., & Wei, L.Y. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting 180, (9), 1610-1629
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimator of the variance of United Kingdom inflation. Econometrica. 50(4), 987-1008.
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc.; 675-701.
Huarng, K. H. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets and Systems. 123, 155-162.
Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers and Operations Research, 32(10), 2513-2522.
Huang, N. E., Shen, Z., Long, S. R. (1999). A new view of nonlinear water waves: the Hilbert spectrum, Annu. Rev. Fluid Mech. 31, 417-457.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, in: Proceedings of the royal society of London series a-mathematical physical and engineering sciences, series A, 454, 903-995.
Huarng, K. H., Yu, T. H. K. (2006). The application of neural networks to forecast fuzzy time series, Physica A. 336, 481-491.
Huarng, K. H., Yu, H. K., Hsu, Y. W. (2007). A Multivariate Heuristic Model for Fuzzy, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 37(4), 836-846.
Kim, K. J. (2003) Financial time series forecasting using support vector machines, Neurocomputing. 55, 307-319.
Kim, K., Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for prediction of stock index. Expert System with Applications. 19, 125-132.
Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M. (1990). Stock market prediction system with modular neural network. In Proceedings of the international joint conference on neural networks, San Diego, California, 1-6.
Nikolopoulos, C., Fellrath, P. (1994). A hybrid expert system for investment advising. Expert Systems. 11(4), 245-250.
Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.
Roh, T. H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications. 33, 916-922.
Song, Q., Chissom, B. S. (1993). Forecasting enrollments with fuzzy time-series Part I, Fuzzy Sets and Systems. 54, 1-10.
Takagi, T., and Sugeno, M. (1983). Derivation of fuzzy control rules from human operator’s control actions, in Proc. IFAC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis. 55-60.
Thawornwong, S., Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks, Neurocomputing. 56, 205-232
Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
Vincent, H. T., Hu, S.-L. J., Hou, Z. (1999). Damage detection using empirical mode decomposition method and a comparison with wavelet analysis, in: Proceedings of the second international workshop on structural health monitoring, Stanford 891-900.
Yu, H. K. (2005). Weighted fuzzy time-series models for TAIEX forecasting, Physica A. 349, 609-624.
Yu, D. J., Cheng, J. S., Yang, Y. (2005). Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings, Mech. Syst. Signal Process. 19 (2), 259-270.
In-Text Citation: (Wei & Tsai, 2022)
To Cite this Article: Wei, L.-Y., & Tsai, C.-H. (2022). A Hybrid Stock Forecasting Model based on EMD -SVR and Technical Indicators Feature Selection. International Journal of Academic Research in Accounting Finance and Management Sciences, 12(1), 184–200.
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