Forecasting non-stationary financial time series through genetic algorithm

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We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm's predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.
4 Pages and 5 figures

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Chaotic Dynamics, Data Analysis, Statistics and Probability, Statistical Finance

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http://salesiana.dossiersoluciones.com/handle/123456789/208972

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/nlin/0507037
 http://arxiv.org/abs/nlin/0507037

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arXiv