Using genetic programming to predict financial data
Iba, H.
Sasaki, T.
Dept. of Inf. & Commun. Eng., Tokyo Univ.;
This paper appears in: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Publication Date: 1999
Volume: 1,
On page(s): -251 Vol. 1
Meeting Date: 07/06/1999 - 07/09/1999
Location: Washington, DC, USA
ISBN: 0-7803-5536-9
References Cited: 14
INSPEC Accession Number: 6338849
Digital Object Identifier: 10.1109/CEC.1999.781932
Current Version Published: 2002-08-06
Abstract
This paper presents the application of genetic programming (GP) to
the prediction of price data in the Japanese stock market. The goal of
this task is to choose the best stocks when making an investment and to
decide when and how many stocks to sell or buy. There have been several
applications of genetic algorithms (GAs) to financial problems, such as
portfolio optimization, bankruptcy prediction, financial forecasting,
fraud detection and scheduling. GP has also been applied to many
problems in time-series prediction. However, relatively few studies have
been made for the purpose of predicting stock market data by means of
GP. This paper describes how successfully GP is applied to predicting
stock data so as to gain a high profit. Comparative experiments are
conducted with neural networks to show the effectiveness of the GP-based
approach
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