FRACSION: A Novel Hybrid Algorithm for Forecasting the Industry Index Trend in Tehran Stock Exchange

Authors
payam noor university
Abstract
Due to the dynamic structure and nonlinear fluctuations of the stock market, it is difficult to accurately predict the trend of this market using the old methods. In this study, in order to improve the accuracy of predicting the index trend in different industries, we propose a new algorithm that combines algorithms fractal interpolation and support vector machine regression, abbreviated as fracsion algorithm. . For this purpose, after recognizing the fractal structure of industries using the Hurst exponent of each industry, we consider the value of the index in each fractal industry as the primary data to predict the trend of the index. Then, by modifying the fractal interpolation algorithm, we will generate new data, and finally, by calling the support vector regression algorithm on the obtained data, we will predict the index trend. The results of the implementation of the Hybrid fracsion algorithm and its comparison with two conventional methods, namely artificial neural network and support vector machine regression, indicate the superiority of the predictive accuracy of the proposed algorithm.
Keywords

[3] Barnsley, M. Fractal everywhere.1ed New York, Academic Press,(1988).



[4] Chen, S.M. & Chung, N.Y. Forecasting enrollments of students by using fuzzy time series and genetic algorithms, International Journal of Information and Management Sciences, vol.17, (2006), pp.1-17.



[5] Chen, S. M. & Chung, N. Y. Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles. International Journal of Information and Management Sciences, vol.21, (2006), pp.485-501.



[6] Chin, C. & Isa, Z. A short range dependence adjusted hurst exponent evaluation for Malaysian and Indonesian financial markets. African Journal of Business Management, Vol.5,(2011) ,2644-2653.



[7] Lu, C.J., Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting, Neural Applied Soft computing, vol.40, (2013), pp.164-178.



[8] Cortes C. & Vapnik V., Support vector networks, Machine Learning, vol.20, (1995),273–297.



[9] Gocken, M., Ozcahci. M., Boru, A. & Dosdogru, A.T. Integrating Metaheuristics and Artificial Neural Networks for improved Stock Price Prediction. Expert Systems with Applications, Vol.44, (2016), pp.320-332.



[10] Huang, W., Nakamori, Y. & Wang, S.Y. Forecasting stock market movement direction with support vector machine, Computers and Operations Research, vol.32, (2005), pp.2513-2522.



[11] Hurst, H.E. Long term storage capacities of reservoirs. Transactions of the American Society of Civil Engineers, vol.116, (1951), pp.770-799.



[12] Kara, Y., Boyacioglu, M.A. & Baykan, O.K. Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange, Expert Systems with Applications, vol.38, (2011), pp.5311-5319.



[13] khaloozadeh, H.,khaki sedigh A. & Lucas, C. Are Stock Prices Predictable in the Tehran Stock Exchange?, Financial Research Journal ,vol.11, (1996), pp.37-46.(in persian)



[14] Kim, K. Financial time series forecasting using support vector machines, Neurocomputing 55, (2003) , pp.307-319.



[15] Kumagai, Y. Fractal structure of financial high frequency data, Fractals, vol.10(1), (2002), pp.13-18.



[16] Kuo, H., Horng, H., Kao, S.J., Lin, T.W., Lee, T.L. & Pan, C.L. An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Systems with Applications, vol.36, (2009), pp.6108-6117.



[17] Kuo, H., Horng, H., Kao, S.J., Lin, T.W., Lee, T.L. & Pan, C.L. Forecasting TAIFEX based on fuzzy time series and particle swarm optimization. Expert Systems with Applications, vol.37, (2010), pp.1494-1502.



[18] Kwapien, J. & Drozdz, S. Physical approach to complex systems, Physics Reports, vol.515, (2012), pp.115-226.



[19] Lee, L.W., WANG, L.H. & Chen, S.M. Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Systems with Applications, vol.33, (2007), pp.539-550.



[20] Lee, L.W., Wang, H.F. & Chen, S.M.Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques. Expert Systems with Applications,vol.34, (2008), pp.328-336.



[21] Li G.Z. , Huang J.B. & Huang H.Y. Calculating method of contraction operators in fractal interpolation based on the B-spline, Journal of Ordnance Engineering College ,vol.18(2), (2006) , pp.76-78.



[22] Mandelbrot, B. B., & Wallis, J.R. Robustness of the rescaled range R/S in the measurement of non-cyclic long run statistical dependence. Water Resources Research, vol. 5, (1969),967-988.



[23] Mangasarian O.L., Nonlinear Programming, McGraw-Hill., New York, (1969).



[24] Mantegna, R.N. & Stanley, H.E. Turbulence and Financial Markets. Nature, vol.383, (1996), pp.587-588.



[25] Matsushita, M. & Fukiwara, H. Fractal growth in financial markets formation, physical sciences and biology, vol.35, (1993), pp.637-548.

[26] Morovat, H. Test of fractal market hypothesis in Tehran Stock Exchange, Stock Exchange, vol.19, (1993), pp.5-25. (in persian).



[27] Park, J. I., Lee, D. J., Song, C. K. & Chun, M.G. TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization. Expert Systems with Applications, vol.37, (2010), pp.959-967.



[28] Peters, E.E. Fractal Market Analysis:Applying Chaos Theory to Investment and Economics ,New York:John Wiley and Son Inc, vol.24,(1994).



[29] Serletin. A. & Shintani M. No evidence of chaos but some evidence of dependence in US stock market, Chaos, solitonis and fractals, Vol.17, (2003), pp.449-459.



[30] Thomas A.T. An Empirical Analysis of the Fractal Dimension of Chinese Equity Returns, doctoral dissertation.(2007).



[31] Vandebei R.J., LOQO users manual-version 3.10, Optimization Methods and Software, vol.11, (1997), pp.485-514.



[32] Vapnik V. The Nature of Statistical Learning Theory.2th ed. Springer-Verlag, (1995).



[33] Wang, H.Y. & Wang, T.T. Multifractal analysis of the Chinese stock, bond and fund markets, Physica A: Statistical Mechanics and its Applications, vol.512, (2018), pp.280-292.



[34] Wang H.Y. , Li H. & Shen J.Y. A Novel Hybrid Fractal Interpolation-SVM Model for Forecasting Stock Price Indexes, worldscientific, vol.27 , (2018), NO.04.



[35] Zhai M.Y. A new method for short-term load forecasting based on fractal interpolation and wavelet analysis, Electrical Power and Energy Systems, vol.69, (2015), pp.241-245.