Genetic programming prediction of stock prices

被引：98

作者：

Kaboudan M.A. ^{[1
]}

机构：

[1] Management Science and Information Systems, Smeal College of Business, Penn State Lehigh Valley, Fogelsville

来源：

Computational Economics | 2000年 / 16卷 / 3期

关键词：

Evolved regression models; Financial market analysis; Nonlinear systems; Stock returns;

D O I：

10.1023/A:1008768404046

中图分类号：

学科分类号：

摘要：

Based on predictions of stock-prices using genetic programming (or GP), a possibly profitable trading strategy is proposed. A metric quantifying the probability that a specific time series is GP-predictable is presented first. It is used to show that stock prices are predictable. GP then evolves regression models that produce reasonable one-day-ahead forecasts only. This limited ability led to the development of a single day-trading strategy (SDTS) in which trading decisions are based on GP-forecasts of daily highest and lowest stock prices. SDTS executed for fifty consecutive trading days of six stocks yielded relatively high returns on investment. © 2001 Kluwer Academic Publishers.

引用

页码：207 / 236

页数：29

共 53 条

[1] Andrew M., Prager R., Genetic programming for the acquisition of double auction market strategies, Advances in Genetic Programming, pp. 355-368, (1994)
[2] Bhattacharyya S., Pietet O., Zumbach G., Representational semantics for genetic programming based learning in high-frequency financial data, Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 11-16, (1998)
[3] Brock W., Lakonishok J., LeBaron B., Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47, pp. 1731-1764, (1992)
[4] Chen S.-H., Genetic programming, predictability, and stock market efficiency, Modelling and Control of National and Regional Economies, pp. 283-288, (1996)
[5] Chen S.-H., Hedging derivative securities with genetic programming, Application of Machine Learning and Data Mining in Finance: Workshop at ECML-98, (1998)
[6] Chen S.-H., Ni C.-C., Evolutionary artificial neural networks and genetic programming: A comparative study based on financial data, Artificial Neural Networks and Genetic Algorithms, (1997)
[7] Chen S.-H., Yeh C.-H., Predicting stock returns with genetic programming: Do the short-term nonlinear regularities exist?, Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pp. 95-101, (1995)
[8] Chen S.-H., Yeh C.-H., Genetic programming and the efficient market hypothesis, Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 45-53, (1996)
[9] Chen S.-H., Yeh C.-H., Using genetic programming to model volatility in financial time series, Genetic Programming 1997: Proceedings of the Second Annual Conference, pp. 58-63, (1997)
[10] Chen S.-H., Yeh C.-H., Toward a computable approach to the efficient market hypothesis: An application of genetic programming, Journal of Economic Dynamics and Control, 21, pp. 1043-1063, (1997)

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