Genetic Programming Polynomial Models of Financial Data Series

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

@InProceedings{iba:2000:gppmfds,
  author =       "Hitoshi Iba and Nikolay Nikolaev",
  title =        "Genetic Programming Polynomial Models of Financial
                 Data Series",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1459--1466",
  volume =       "2",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series,
                 stroganoff, GP system, Tokyo Stock Exchange data, data
                 transformations, economical measures, financial data
                 series, fitness function, functional models, polynomial
                 models, predictive models, profit increase, profitable
                 polynomials, series preprocessing, stock market
                 analysis, traditional GP, data handling, financial data
                 processing, polynomials, series (mathematics), stock
                 markets",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870826",
  abstract =     "The problem of identifying the trend in financial data
                 series in order to forecast them for profit increase is
                 addressed using genetic programming (GP). We enhance a
                 GP system that searches for polynomial models of
                 financial data series and relate it to a traditional GP
                 manipulating functional models. Two of the key issues
                 in the development are: 1) preprocessing of the series
                 which includes data transformations and embedding; and
                 2) design of a proper fitness function that navigates
                 the search by favouring parsimonious and predictive
                 models. The two GP systems are applied for stock market
                 analysis, and examined with real Tokyo Stock Exchange
                 data. Using statistical and economical measures to
                 estimate the results, we show that the GP could evolve
                 profitable polynomials",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

Genetic Programming entries for Hitoshi Iba Nikolay Nikolaev

Citations