- author = "Hitoshi Iba and Takashi Sasaki",
- title = "Using Genetic Programming to Predict Financial Data",
- booktitle = "Proceedings of the Congress on Evolutionary Computation",
- year = "1999",
- editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
- volume = "1",
- pages = "244--251",
- address = "Mayflower Hotel, Washington D.C., USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, time series, Japanese stock market, bankruptcy prediction, best stock choosing, financial data prediction, financial forecasting, fraud detection, high profit, investment, neural networks, portfolio optimization, price data prediction, scheduling, time-series prediction, evolutionary computation, financial data processing, investment, neural nets, stock markets",
- ISBN = "0-7803-5536-9 (softbound)",
- ISBN = "0-7803-5537-7 (Microfiche)",
-
DOI = "
doi:10.1109/CEC.1999.781932",
- 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 optimisation, 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",
-
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
