Genetic Programming Prediction of Stock Prices View Full Text


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Article Info

DATE

2000-12

AUTHORS

M. A. Kaboudan

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. More... »

PAGES

207-236

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1008768404046

DOI

http://dx.doi.org/10.1023/a:1008768404046

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004555001


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