GP and the Predictive Power of Internet Message Traffic

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

@InCollection{Thomas:2002:gagpcf,

• author = "James D. Thomas and Katia Sycara",
• title = "{GP} and the Predictive Power of Internet Message Traffic",
• booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance",
• publisher = "Kluwer Academic Press",
• year = "2002",
• editor = "Shu-Heng Chen",
• chapter = "4",
• pages = "81--102",
• keywords = "genetic algorithms, genetic programming, Computational Finance, Internet Message Boards",
• ISBN = "0-7923-7601-3",
• URL = "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9",
• DOI = "doi:10.1007/978-1-4615-0835-9_4",
• abstract = "This paper investigates the predictive power of the volume of messages produced on internet stock-related measure boards. We introduce a specialized GP learner and demonstrate that it produces trading rules that outperform appropriate buy and hold strategy benchmarks in measures of risk adjusted returns. We compare the results to those attained by using other relevant variables, lags of price and volume, and find that the the message board volume produces clearly superior results. We experiment with alternative representations for the GP trading rule learner. Finally, we find a potential regime shift in the market reaction to the message volume data, and speculate about future trends.",
• notes = "part of \cite{chen:2002:gagpcf}",

}

Genetic Programming entries for James D Thomas Katia Sycara

Citations