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Evolutionary Computation in Economics and Finance pp 359–381Cite as

GP Forecasts of Stock Prices for Profitable Trading

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 100))

Abstract

This chapter documents how GP forecasting of stock prices used to execute a single-day-trading-strategy (or SDTS) improves trading returns. The strategy mandates holding no positions overnight to minimize risk and daily trading decisions are based on forecasts of daily high and low stock prices. For comparison, two methods produce the price forecasts. Genetically evolved models produce one. The other is a naive forecast where today’s actual price is used as tomorrow’s forecast. Trading decisions tested on a small sample of four stocks over a period of twenty days produced higher returns for decisions based on the GP price forecasts.

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Author information

Authors and Affiliations

  1. Management Science & Information Systems, Penn State Lehigh Valley, USA

    Mahmoud Kaboudan

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  1. Mahmoud Kaboudan
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Editor information

Editors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan, R. O. China

    Shu-Heng Chen

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© 2002 Springer-Verlag Berlin Heidelberg

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Kaboudan, M. (2002). GP Forecasts of Stock Prices for Profitable Trading. In: Chen, SH. (eds) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol 100. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1784-3_19

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  • DOI: https://doi.org/10.1007/978-3-7908-1784-3_19

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2512-1

  • Online ISBN: 978-3-7908-1784-3

  • eBook Packages: Springer Book Archive

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