Genetic Programming of Polynomial Models for Financial Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Nikolaev:2002:gagpcf,

• author = "Nikolay Y. Nikolaev and Hitoshi Iba",
• title = "Genetic Programming of Polynomial Models for Financial Forecasting",
• booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance",
• publisher = "Kluwer Academic Press",
• year = "2002",
• editor = "Shu-Heng Chen",
• chapter = "5",
• pages = "103--123",
• keywords = "genetic algorithms, genetic programming, STROGANOFF, Polynomial Models, Overfitting Avoidance",
• 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_5",
• abstract = "addresses the problem of finding trends in financial data series using genetic programming (GP). A GP system STROGANOFF that searches for polynomial autoregressive models is presented. The system is specialized for time series processing with elaborations in two aspects: 1) preprocessing the given series using data transformations and embedding; and, 2) design of a fitness function for efficient search control that favours accurate, parsimonious, and predictive models. STROGANOFF is related to a traditional GP system which manipulates functional expressions. Both GP systems are examined on a Nikkei225 series from the Tokyo Stock Exchange. Using statistical and economical measures we show that STROGANOFF outperforms traditional GP, and it can evolve profitable polynomials.",
• notes = "part of \cite{chen:2002:gagpcf}",

}

Genetic Programming entries for Nikolay Nikolaev Hitoshi Iba

Citations