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

Nicolas Navet 1 Shu-Heng Chen 2
1 TRIO - Real time and interoperability
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : 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. It is shown that the EOD price time series of the NYSE U.S. 100 stocks have significantly differing entropies and, based on surrogate testing, that there are some weak temporal dependencies in the time series. The BDS statistics and the autocorrelation suggest that these temporal dependencies are both linear and nonlinear. Then we study the extent to which the return of Genetic Programming-induced financial trading rules is correlated with the entropy rates of the price time series. Experiments are conducted on EOD data of the stocks making up the NYSE US 100 index during the period 2000-2006.
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https://hal.inria.fr/inria-00273874
Contributor : Nicolas Navet <>
Submitted on : Wednesday, April 16, 2008 - 3:24:32 PM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM

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  • HAL Id : inria-00273874, version 1

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Nicolas Navet, Shu-Heng Chen. On Predictability and Profitability: Would AI Induced Trading Rules be Sensitive to the Entropy of Time Series?. 2nd Workshop: Bridging Mathematics, Natural Sciences, Social Sciences, and Finance, Apr 2008, Monaco, Monaco. ⟨inria-00273874⟩

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