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

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