Abstract
This paper suggests an enhanced fuzzy rule-based evolving participatory learning with kernel recursive least squares algorithm for stock market index forecasting. The algorithm combines an incremental clustering algorithm to learn the antecedent part of functional fuzzy rules, and a kernel recursive least squares method to compute the parameters of the consequents of the rules. The algorithm uses a small number of user-defined parameters to enhance its autonomy. Computational experiments concerning one-step-ahead forecasts of the S&P 500 stock market index from January 2010 to December 2017 is conducted to compare the algorithm with traditional forecasting and state-of-the-art evolving fuzzy algorithms. Accuracy and computational effort evaluation indicate the high potential of the kernel recursive participatory learning algorithm for stock market index time series forecasting.
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References
Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Comparison of arima and artificial neural network models for stock price prediction. J. Appl. Math. 1–7 (2014)
Agrawal, J., Chourasia, V., Mittra, A.: State-of-the-art in stock prediction techniques. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2(4), 1360–1366 (2013)
Angelov, P., Filev, D.: An approach to online identification of Takagi-Sugeno fuzzy models. Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 484–498 (2004)
Angelov, P., Filev, D.P., Kasabov, N.: Evolving Intelligent Systems: Methodology and Applications. Wiley, Hoboken (2010)
Bacchetta, P., Mertens, E., Van Wincoop, E.: Predictability in financial markets: what do survey expectations tell us? J. Int. Money Finan. 28(3), 406–426 (2009)
Bollerslev, T., Marrone, J., Xu, L., Zhou, H.: Stock return predictability and variance risk premia: statistical inference and international evidence. J. Financ. Quant. Anal. 49(3), 633–661 (2014)
Engel, Y., Mannor, S., Meir, R.: The kernel recursive least-squares algorithm. Trans. Sig. Process. 52(8), 2275–2285 (2004)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finan. 25(2), 383–417 (1970)
Kim, Y., Enke, D.: Using neural networks to forecast volatility for an asset allocation strategy based on the target volatility. Proced. Comput. Sci. 95, 281–286 (2016)
Komijani, M., Lucas, C., Araabi, B.N., Kalhor, A.: Introducing evolving Takagi-Sugeno method based on local least squares support vector machine. Evolv. Syst. 3(2), 81–93 (2012)
Lima, E., Hell, M., Ballini, R., Gomide, F.: Evolving fuzzy modeling using participatory learning. Evol. Intell. Syst.: Methodol. Appl. 67–86 (2010)
Liu, W., Principe, J.C., Haykin, S.: Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley, Hoboken (2011)
Lughofer, E.: Evolving Fuzzy Systems: Methodologies, Advances Concepts and Applications. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18087-3
Maciel, L., Gomide, F., Ballini, R.: Enhanced evolving participatory learning fuzzy modeling: an application for asset returns volatility forecasting. Evol. Syst. 5(2), 75–88 (2013)
Ngia, L.S.H., Sjoberg, J., Viberg, M.: Adaptive neural nets filter using a recursive Levenberg-Marquardt search direction. In: 32th IEEE Conference on Signals, Systems and Computers. pp. 697–701 (1998)
Phan, D.H.B., Sharma, S.S., Narayan, P.K.: Stock return forecasting: some new evidence. Int. Rev. Financ. Anal. 40, 38–51 (2015)
Rather, A.M., Agarwal, A., Sastry, V.: Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst. Appl. 42(6), 3234–3241 (2015)
Richard, C., Bermudez, J.C.M., Honeine, P.: Online prediction of time series data with kernels. Trans. Sig. Process. 57(3), 1058–1067 (2009)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Shafieezadeh-Abadeh, S., Kalhor, A.: Evolving takagi-sugeno model based on online gustafson-kessel algorithm and kernel recursive least square method. Evol. Syst. 7(1), 1–14 (2016)
Silva, L.R.S.d.: Aprendizagem participativa em agrupamento nebuloso de dados. mestrado. Universidade Estadual de Campinas (2003). http://libdigi.unicamp.br/document/?code=vtls000296353. Accessed 27 Mar 2017
Vieira, R.G., Gomide, F., Ballini, R.: Kernel evolving participatory fuzzy modeling for time series forecasting (Manuscript submitted for publication at the IEEE World Congress on Computational Intelligence)
Yager, R.R.: A model of participatory learning. Trans. Syst. Man, Cybern. 20(5), 1229–1234 (1990)
Acknowledgments
The authors thank the Brazilian Ministry of Education (CAPES), and the Brazilian National Council for Scientific and Technological Development (CNPq) for a fellowship, and grant 305906/2014-3.
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Vieira, R., Maciel, L., Ballini, R., Gomide, F. (2018). Stock Market Price Forecasting Using a Kernel Participatory Learning Fuzzy Model. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_31
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DOI: https://doi.org/10.1007/978-3-319-95312-0_31
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