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.
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Kaboudan, M.A. Genetic Programming Prediction of Stock Prices. Computational Economics 16, 207–236 (2000). https://doi.org/10.1023/A:1008768404046
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DOI: https://doi.org/10.1023/A:1008768404046