Using Genetic Programming to Predict Financial Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Hitoshi Iba and Takashi Sasaki",
  title =        "Using Genetic Programming to Predict Financial Data",
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "244--251",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series,
                 Japanese stock market, bankruptcy prediction, best
                 stock choosing, financial data prediction, financial
                 forecasting, fraud detection, high profit, investment,
                 neural networks, portfolio optimization, price data
                 prediction, scheduling, time-series prediction,
                 evolutionary computation, financial data processing,
                 investment, neural nets, stock markets",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  DOI =          "doi:10.1109/CEC.1999.781932",
  abstract =     "This paper presents the application of genetic
                 programming (GP) to the prediction of price data in the
                 Japanese stock market. The goal of this task is to
                 choose the best stocks when making an investment and to
                 decide when and how many stocks to sell or buy. There
                 have been several applications of genetic algorithms
                 (GAs) to financial problems, such as portfolio
                 optimisation, bankruptcy prediction, financial
                 forecasting, fraud detection and scheduling. GP has
                 also been applied to many problems in time-series
                 prediction. However, relatively few studies have been
                 made for the purpose of predicting stock market data by
                 means of GP. This paper describes how successfully GP
                 is applied to predicting stock data so as to gain a
                 high profit. Comparative experiments are conducted with
                 neural networks to show the effectiveness of the
                 GP-based approach",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",

Genetic Programming entries for Hitoshi Iba Takashi Sasaki