- author = "Hitoshi Iba and Nikolay Nikolaev",
- title = "Genetic Programming Polynomial Models of Financial Data Series",
- booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00",
- year = "2000",
- pages = "1459--1466",
- volume = "2",
- address = "La Jolla Marriott Hotel, La Jolla, California, USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, time series, stroganoff, GP system, Tokyo Stock Exchange data, data transformations, economical measures, financial data series, fitness function, functional models, polynomial models, predictive models, profit increase, profitable polynomials, series preprocessing, stock market analysis, traditional GP, data handling, financial data processing, polynomials, series (mathematics), stock markets",
- ISBN = "0-7803-6375-2",
-
DOI = "
doi:10.1109/CEC.2000.870826",
- abstract = "The problem of identifying the trend in financial data series in order to forecast them for profit increase is addressed using genetic programming (GP). We enhance a GP system that searches for polynomial models of financial data series and relate it to a traditional GP manipulating functional models. Two of the key issues in the development are: 1) preprocessing of the series which includes data transformations and embedding; and 2) design of a proper fitness function that navigates the search by favouring parsimonious and predictive models. The two GP systems are applied for stock market analysis, and examined with real Tokyo Stock Exchange data. Using statistical and economical measures to estimate the results, we show that the GP could evolve profitable polynomials",
-
notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 00TH8512,
Library of Congress Number = 00-018644",
