A return scaling cross-correlation function of exponential parameter is introduced in the present work, and a stochastic time strength neural network model is developed to predict the return scaling cross-correlations between two real stock market indexes, Shanghai Composite Index and Shenzhen Component Index. In the proposed model, the stochastic time strength function gives a weight for each historical data and makes the model have the effect of random movement. The empirical research is performed in testing the model forecasting effect of long-term cross-correlation relationships by training short-term cross-correlations, and a corresponding comparison analysis is made to the backpropagation neural network model. The empirical results show that the proposed neural network is advantageous in increasing the forecasting precision.
Forecast Cross-correlation Return scaling Neural network Stochastic time strength function Financial time series
This is a preview of subscription content, log in to check access.
The authors were supported in part by National Natural Science Foundation of China Grant No. 71271026.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Ao SI (2010) A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting. Soft Comput 15:1041–1053CrossRefGoogle Scholar
Azoff EM (1994) Neural network time series forecasting of financial market. Wiley, New YorkGoogle Scholar
Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control, 3rd edn. Prentice Hall, New JerseyMATHGoogle Scholar
Cheng WY, Wang J (2013) Dependence phenomenon analysis of the stock market. Europhys Lett (EPL) 102:18004CrossRefGoogle Scholar
Demuth H, Beale M (1998) Neural network toolbox: for use with MATLAB, 5th edn. The Math Works Inc, NatickGoogle Scholar
Duan WQ, Stanley HE (2011) Cross-correlation and the predictability of financial return series. Phys A 390:290–296CrossRefGoogle Scholar
Fang W, Wang J (2012) Statistical properties and multifractal behaviors of market returns by using dynamic systems. Int J Mod Phys C 23:1250023CrossRefMATHGoogle Scholar
Ghiassi M, Saidane H, Zimbra DK (2005) A dynamic artificial neural network model for forecasting time series events. Int J Forecast 21:341–362CrossRefGoogle Scholar
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood CliffsMATHGoogle Scholar
He LY, Chen SP (2011) A new approach to quantify power-law cross-correlation and its application to commodity markets. Phys A 390:3806–3814CrossRefGoogle Scholar
Horvatic D, Stanley HE, Podobnik B (2011) Detrended cross-correlation analysis for non-stationary time series with periodic trends. Europhys Lett (EPL) 94:18007CrossRefGoogle Scholar
Ilinski K (2001) Physics of finance: Gauge modeling in non-equilibrium pricing. Wiley, New YorkGoogle Scholar
Kim KJ, Han I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19:125–132CrossRefGoogle Scholar
Kullmann L, Kertész J, Kaski K (2002) Time-dependent cross- correlations between different stock returns: a directed network of influence. Phys Rev E 66:026125CrossRefGoogle Scholar
Laloux L, Cizeau P, Potters M, Bouchaud JP (2000) Random matrix theory and financial correlations. Int J Theor Appl Financ 3:391–397CrossRefMATHGoogle Scholar
Lamberton D, Lapeyre B (2000) Introduction to stochastic calculus applied to finance. Chapman and Hall/CRC, LondonMATHGoogle Scholar
LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23:1487–1516CrossRefMATHGoogle Scholar
Lendasse A, Bodt ED, Wertz V, Verleysen M (2000) Non-linear financial time series forecasting—application to the Bel 20 stock market index. Eur J Econ Soc Syst 14:81–91CrossRefMATHGoogle Scholar
Liao Z, Wang J (2010) Forecasting model of global stock index by stochastic time effective neural network. Expert Syst Appl 37:834–841CrossRefGoogle Scholar
Liu FJ, Wang J (2012) Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing 83:12–21CrossRefGoogle Scholar
Liu HF, Wang J (2011) Integrating independent component analysis and principal component analysis with neural network to predict Chinese stock market. Math Problems Eng 382659:15Google Scholar