Advertisement

Soft Computing

, Volume 22, Issue 9, pp 3097–3109 | Cite as

Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics

  • Haiyan Mo
  • Jun Wang
Methodologies and Application
  • 346 Downloads

Abstract

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.

Keywords

Forecast Cross-correlation Return scaling Neural network Stochastic time strength function Financial time series 

Notes

Acknowledgements

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.

References

  1. Ao SI (2010) A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting. Soft Comput 15:1041–1053CrossRefGoogle Scholar
  2. Azoff EM (1994) Neural network time series forecasting of financial market. Wiley, New YorkGoogle Scholar
  3. Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control, 3rd edn. Prentice Hall, New JerseyzbMATHGoogle Scholar
  4. Cheng WY, Wang J (2013) Dependence phenomenon analysis of the stock market. Europhys Lett (EPL) 102:18004CrossRefGoogle Scholar
  5. Demuth H, Beale M (1998) Neural network toolbox: for use with MATLAB, 5th edn. The Math Works Inc, NatickGoogle Scholar
  6. Duan WQ, Stanley HE (2011) Cross-correlation and the predictability of financial return series. Phys A 390:290–296CrossRefGoogle Scholar
  7. Fang W, Wang J (2012) Statistical properties and multifractal behaviors of market returns by using dynamic systems. Int J Mod Phys C 23:1250023CrossRefzbMATHGoogle Scholar
  8. 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
  9. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  10. 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
  11. 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
  12. Ilinski K (2001) Physics of finance: Gauge modeling in non-equilibrium pricing. Wiley, New YorkGoogle Scholar
  13. 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
  14. 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
  15. Laloux L, Cizeau P, Potters M, Bouchaud JP (2000) Random matrix theory and financial correlations. Int J Theor Appl Financ 3:391–397CrossRefzbMATHGoogle Scholar
  16. Lamberton D, Lapeyre B (2000) Introduction to stochastic calculus applied to finance. Chapman and Hall/CRC, LondonzbMATHGoogle Scholar
  17. LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23:1487–1516CrossRefzbMATHGoogle Scholar
  18. 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 14:81–91CrossRefzbMATHGoogle Scholar
  19. Liao Z, Wang J (2010) Forecasting model of global stock index by stochastic time effective neural network. Expert Syst Appl 37:834–841CrossRefGoogle Scholar
  20. 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
  21. 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
  22. Mantegna RN (1999) Hierarchical structure in financial markets. Eur Phys J B 11:193–197CrossRefGoogle Scholar
  23. Mantegna RN, Stanley HE (1999) A introduction to econophysics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  24. Mills TC (1999) The econometric modelling of financial time series, 2nd edn. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  25. Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Environ Res Risk Assess 19:326–339CrossRefzbMATHGoogle Scholar
  26. Niu HL, Wang J (2014) Financial time series prediction by a random data-time effective RBF neural network. Soft Comput 18:497–508CrossRefGoogle Scholar
  27. Niu HL, Wang J (2013) Volatility clustering and long memory of financial time series and financial price model. Digit Signal Process 23:489–498MathSciNetCrossRefGoogle Scholar
  28. Faruk DO (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 23:586–594CrossRefGoogle Scholar
  29. Onnela JP, Chakraborti A, Kaski K, Kertész J (2002) Dynamic asset trees and portfolio analysis. Eur Phys J B 30:285–388MathSciNetCrossRefzbMATHGoogle Scholar
  30. Pei AQ, Wang J (2013) Nonlinear analysis of return time series model by oriented percolation dynamic system. Abstr Appl Anal 2013(612738): 12Google Scholar
  31. Plerou V, Gopikrishnan P, Rosenow B, Amaral LAN, Guhr T, Stanley HE (2002) Random matrix approach to cross correlations in financial data. Phys Rev E 65:066126CrossRefGoogle Scholar
  32. Podobnik B, Stanley HE (2008) Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett 100:084102CrossRefGoogle Scholar
  33. Refenes AP (1994) Neural networks in the capital markets. Wiley, New YorkGoogle Scholar
  34. Ross SM (1999) An introduction to mathematical finance. Cambridge University Press, CambridgezbMATHGoogle Scholar
  35. Ruan GC, Tan Y (2010) A three-layer back-propagation neural network for spam detection using artificial immune concentration. Soft Comput 14:139–150CrossRefGoogle Scholar
  36. Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition. The MIT Press, CambridgeGoogle Scholar
  37. Trippi RR, Turban E (1993) Neural networks in finance and investing: using artificial intelligence to improve real-world performance. Probus, ChicagoGoogle Scholar
  38. Tsay RS (2005) Analysis of financial time series. Wiley, HobokenCrossRefzbMATHGoogle Scholar
  39. Utsugi A, Ino K, Oshikawa M (2004) Random matrix theory analysis of cross correlations in financial markets. Phys Rev E 70:026110CrossRefGoogle Scholar
  40. Wang F, Wang J (2012) Statistical analysis and forecasting of return interval for SSE and model by lattice percolation system and neural network. Comput Ind Eng 62:198–205CrossRefGoogle Scholar
  41. Wang J, Wang QY, Shao JG (2010) Fluctuations of stock price model by statistical physics systems. Math Comput Model 51:431–440MathSciNetCrossRefzbMATHGoogle Scholar
  42. Wang TS, Wang J, Zhang JH, Fang W (2011) Voter interacting systems applied to Chinese stock markets. Math Comput Simul 81:2492–2506MathSciNetCrossRefzbMATHGoogle Scholar
  43. Wang YD, Wei Y, Wu CF (2010) Cross-correlations between Chinese A-share and B-share markets. Phys A 389:5468–5478CrossRefGoogle Scholar
  44. Wichard JD, Merkwirth C, Ogorzalek M (2004) Detecting correlation in stock market. Phys A 344:308–311MathSciNetCrossRefGoogle Scholar
  45. Wilcox D, Gebbie T (2007) An analysis of cross-correlations in an emerging market. Phys A 375:584–598CrossRefGoogle Scholar
  46. Xiao D, Wang J (2012) Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis. Phys A 391:4827–4838CrossRefGoogle Scholar
  47. Yu Y, Wang J (2012) Lattice oriented percolation system applied to volatility behavior of stock market. J Appl Stat 39:785–797MathSciNetCrossRefGoogle Scholar
  48. Zhang JH, Wang J (2010) Modeling and simulation of the market fluctuations by the finite range contact systems. Simul Model Pract Theory 18:910–925CrossRefGoogle Scholar
  49. Zhou WX (2008) Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys Rev E 77:066211CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of ScienceBeijing Jiaotong UniversityBeijingPeople’s Republic of China

Personalised recommendations