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Financial Time Series Modeling and Prediction Using Postfix-GP


  • Vipul K. Dabhi

    () (Dharmsinh Desai University)

  • Sanjay Chaudhary

    () (Ahmedabad University)


Abstract Financial time series prediction is considered as a challenging task. The task becomes difficult due to inherent nonlinear and non-stationary characteristics of financial time series. This article proposes a combination of wavelet and Postfix-GP, a postfix notation based genetic programming system, for financial time series prediction. The discrete wavelet transform approach is used to smoothen the time series by separating the fluctuations from the trend of the series. Postx-GP is then employed to evolve models for the smoothen series. The out-of-sample prediction capability of evolved solutions is tested on two stocks price and two stock indexes series. The results are compared with those obtained using ECJ, a Java based evolutionary framework. The nonparametric statistical tests are applied to evaluate the significance of the obtained results.

Suggested Citation

  • Vipul K. Dabhi & Sanjay Chaudhary, 2016. "Financial Time Series Modeling and Prediction Using Postfix-GP," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 219-253, February.
  • Handle: RePEc:kap:compec:v:47:y:2016:i:2:d:10.1007_s10614-015-9482-y
    DOI: 10.1007/s10614-015-9482-y

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    References listed on IDEAS

    1. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    2. M. B. Porecha & P. K. Panigrahi & J. C. Parikh & C. M. Kishtawal & Sujit Basu, 2005. "Forecasting non-stationary financial time series through genetic algorithm," Papers nlin/0507037,
    3. M. A. Kaboudan, 2000. "Genetic Programming Prediction of Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 16(3), pages 207-236, December.
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