Financial Time Series Modeling and Prediction Using Postfix-GP

Vipul Dabhi () and Sanjay Chaudhary ()

Computational Economics, 2016, vol. 47, issue 2, 219-253

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. Copyright Springer Science+Business Media New York 2016

Keywords: Financial time series prediction; Postfix genetic programming; One-step prediction; Multi-step prediction (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10614-015-9482-y

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