Genetic programming polynomial models of financial data series
Iba, H.
Nikolaev, N.
Dept. of Inf. & Commun. Eng., Tokyo Univ.;
This paper appears in: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Publication Date: 2000
Volume: 2,
On page(s): 1459-1466 vol.2
Meeting Date: 07/16/2000 - 07/19/2000
Location: La Jolla, CA, USA
ISBN: 0-7803-6375-2
References Cited: 15
INSPEC Accession Number: 6735751
Digital Object Identifier: 10.1109/CEC.2000.870826
Current Version Published: 2002-08-06
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
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