An Application of Genetic Programming to Forecasting Foreign Exchange Rates

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

@InCollection{Buckley:2009:niiiakd,
  title =        "An Application of Genetic Programming to Forecasting
                 Foreign Exchange Rates",
  author =       "Muneer Buckley and Zbigniew Michalewicz and 
                 Ralf Zurbruegg",
  publisher =    "IGI Global",
  year =         "2009",
  booktitle =    "Nature-Inspired Informatics for Intelligent
                 Applications and Knowledge Discovery: Implications in
                 Business, Science, and Engineering",
  editor =       "Raymond Chiong",
  chapter =      "2",
  pages =        "26--48",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "1605667056",
  URL =          "http://www.igi-global.com/bookstore/chapter.aspx?titleid=36310",
  URL =          "http://hdl.handle.net/2440/54525",
  oai =          "oai:digital.library.adelaide.edu.au:2440/54525",
  abstract =     "There is a great need for accurate predictions of
                 foreign exchange rates. Many industries participate in
                 foreign exchange scenarios with little idea where the
                 exchange rate is moving, and what the optimum decision
                 to make at any given time is. Although current economic
                 models do exist for this purpose, improvements could be
                 made in both their flexibility and adaptability. This
                 provides much room for models that do not suffer from
                 such constraints. This chapter proposes the use of a
                 genetic program (GP) to predict future foreign exchange
                 rates. The GP is an extension of the DyFor GP tailored
                 for forecasting in dynamic environments. The GP is
                 tested on the Australian / US (AUD/USD) exchange rate
                 and compared against a basic economic model. The
                 results show that the system has potential in
                 forecasting long term values, and may do so better than
                 established models. Further improvements are also
                 suggested.",
}

Genetic Programming entries for Muneer Buckley Zbigniew Michalewicz Ralf Zurbruegg

Citations