Genetic Programming Prediction of Stock Prices

Authors:
M. A. Kaboudan
Affiliations:
Management Science and Information Systems, Smeal College of Business, Penn State Lehigh Valley, Fogelsville, PA 18051, U.S.A. mak7@psu.edu
Venue:
Computational Economics
Year:
2000

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Abstract

Based on predictions of stock-pricesusing genetic programming (or GP), a possiblyprofitable trading strategy is proposed. A metricquantifying the probability that a specific timeseries is GP-predictable is presented first. It isused to show that stock prices are predictable. GPthen evolves regression models that produce reasonableone-day-ahead forecasts only. This limited ability ledto the development of a single day-trading strategy(SDTS) in which trading decisions are based onGP-forecasts of daily highest and lowest stock prices.SDTS executed for fifty consecutive trading days ofsix stocks yielded relatively high returns oninvestment.