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Natural Computing in Computational Finance pp 197–210Cite as

On Predictability and Profitability: Would GP Induced Trading Rules be Sensitive to the Observed Entropy of Time Series?

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

Summary

The entropy rate of a dynamic process measures the uncertainty that remains in the next information produced by the process given complete knowledge of the past. It is thus a natural measure of the difficulty faced in predicting the evolution of the process. The first question investigated here is whether stock price time series exhibit temporal dependencies that can be measured through entropy estimates. Then we study the extent to which the return of GP-induced financial trading rules is correlated with the entropy rates of the price time series. Experiments are conducted on end of day (EOD) data of the stocks making up the NYSE US 100 index during the period 2000–2006, with genetic programming being used to induce the trading rules.

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Authors and Affiliations

  1. INRIA Lorraine, Campus-Scientifique, BP239, F-54506, Vandoeuvre, France

    Nicolas Navet & Shu-Heng Chen

  2. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan, 11623

    Nicolas Navet

Authors
  1. Nicolas Navet
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  2. Shu-Heng Chen
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Editor information

Editors and Affiliations

  1. School of Business Quinn School, University College Dublin, Belfield, Dublin 4, Ireland

    Anthony Brabazon (Head of Research) (Head of Research)

  2. Natural Computing Research and Applications School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael O’Neill (Director) (Director)

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© 2008 Springer-Verlag Berlin Heidelberg

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Navet, N., Chen, SH. (2008). On Predictability and Profitability: Would GP Induced Trading Rules be Sensitive to the Observed Entropy of Time Series?. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_11

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  • DOI: https://doi.org/10.1007/978-3-540-77477-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

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