Investment Decision Making Using FGP: A Case Study

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{li:1999:IDMUFACS,

• author = "Jin Li and Edward P. K. Tsang",
• title = "Investment Decision Making Using FGP: A Case Study",
• booktitle = "Proceedings of the Congress on Evolutionary Computation",
• year = "1999",
• editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
• volume = "2",
• pages = "1253--1259",
• 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, forecasting, Dow Jones Industrial Average, FGP, Financial Genetic Programming, case study, daily closing price, financial investment decision making, future prices, genetic generated decision trees, genetic programming based forecasting system, inflation rate, moving average, price-earning ratio, self-adjusted thresholds, share prices, shorter term investment decisions, technical analysis rules, technical rules, trading range breakout, decision support systems, decision trees, forecasting theory, investment, stock markets",
• ISBN = "0-7803-5536-9 (softbound)",
• ISBN = "0-7803-5537-7 (Microfiche)",
• URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/CEC99.pdf",
• URL = "ftp://ftp.essex.ac.uk/pub/csp/LiTsa-C45-Cec99.ps",
• URL = "http://citeseer.ist.psu.edu/237547.html",
• DOI = "doi:10.1109/CEC.1999.782584",
• abstract = "Financial investment decision making is extremely difficult due to the complexity of the domain. Many factors could influence the change of share prices. FGP (Financial Genetic Programming) is a genetic programming based forecasting system, which is designed to help users evaluate the impact of factors and explore their interactions in relation to future prices. Users channel into FGP factors which they believe are relevant to the prediction. Examples of such factors may include fundamental factors such as 'price-earning ratio', 'inflation rate' and/or technical factors such as '5-days moving average', '63-days trading range breakout', etc. FGP uses the power of genetic generated decision trees through technical rules with self-adjusted thresholds. In earlier papers, we have reported how FGP used well-known technical analysis rules to make investment decisions (E.P.K. Tsang et al., 1998; J. Li and E.P.K. Tsang, 1999). The paper tests the versatility of FGP by testing it on shorter term investment decisions. To evaluate FGP more thoroughly, we also compare it with C4.5, a well known machine learning classifier system. We used six and a half years' daily closing price of the Dow Jones Industrial Average (DJIA) index for training and over three and half years' data for testing, and obtained favourable results for FGP",
• 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 Jin Li Edward P K Tsang

Citations