Genetic Programming with Wavelet-Based Indicators for Financial Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Jin_GP_Wavelet,
  author =       "Jin Li and Zhu Shi and Xiaoli Li",
  title =        "Genetic Programming with Wavelet-Based Indicators for
                 Financial Forecasting",
  journal =      "Transactions of the Institute of Measurement and
                 Control",
  year =         "2006",
  volume =       "28",
  number =       "3",
  pages =        "285--297",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, wavelet
                 analysis, financial forecasting",
  URL =          "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/Jin_GP_Wavelet.pdf",
  URL =          "http://tim.sagepub.com/content/vol28/issue3/",
  DOI =          "doi:10.1191/0142331206tim177oa",
  size =         "13 pages",
  abstract =     "Wavelet analysis, as a promising technique, has been
                 used to approach numerous problems in science and
                 engineering. Recent years have witnessed its novel
                 application in economic and finance. This paper is to
                 investigate whether features (or indicators) extracted
                 using the wavelet analysis technique could improve
                 financial forecasting by means of Financial Genetic
                 Programming (FGP), a genetic programming based
                 forecasting tool (i.e., Li, 2001). More specifically,
                 to predict whether Down Jones Industrial Average (DJIA)
                 Index will rise by 2.2per cent or more within the next
                 21 trading days, we first extract some indicators based
                 on wavelet coefficients of the DJIA time series using a
                 discrete wavelet transform; we then feed FGP with those
                 wavelet-based indicators to generate decision trees and
                 make predictions. By comparison with the prediction
                 performance of our previous study (i.e., Li and Tsang,
                 2000), it is suggested that wavelet analysis be capable
                 of bringing in promising indicators, and improving the
                 forecasting performance of FGP.",
}

Genetic Programming entries for Jin Li Zhu Shi Xiaoli Li

Citations