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Maximum Entropy Production Principle for Stock Returns

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  • Pawe{l} Fiedor

Abstract

In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Such structural complexity of the time series describing logarithmic stock returns can be used as a measure of the inherent (model-free) predictability of the underlying price formation processes, testing the Efficient-Market Hypothesis in practice. We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant degree. To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two mentioned markets, inquiring into whether it is possible to enhance prediction of stock returns based on the structural complexity of these and the mentioned principle.

Suggested Citation

  • Pawe{l} Fiedor, 2014. "Maximum Entropy Production Principle for Stock Returns," Papers 1408.3728, arXiv.org.
    • Handle: RePEc:arx:papers:1408.3728
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    References listed on IDEAS

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    1. L. Ingber, 1984. "Statistical mechanics of nonlinear nonequilibrium financial markets," Lester Ingber Papers 84nn, Lester Ingber.
    2. Cătălin Stărică & Clive Granger, 2005. "Nonstationarities in Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 503-522, August.
    3. Pawe{l} Fiedor, 2013. "Frequency Effects on Predictability of Stock Returns," Papers 1310.5540, arXiv.org, revised Nov 2013.
    4. R. Steuer & L. Molgedey & W. Ebeling & M.A. Jiménez-Montaño, 2001. "Entropy and optimal partition for data analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 19(2), pages 265-269, January.
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    Cited by:

    1. Paweł Fiedor, 2015. "Multiscale Analysis of the Predictability of Stock Returns," Risks, MDPI, vol. 3(2), pages 1-15, June.
    2. Paweł Fiedor & Artur Hołda, 2015. "The Effects of Bankruptcy on the Structural Complexity of the Price Changes on WSE," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 41.
    3. Fiedor Paweł & Hołda Artur, 2016. "The Effects of Bankruptcy on the Predictability of Price Formation Processes on Warsaw’s Stock Market," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 12(1), pages 32-42.
    4. Pawe³ Fiedor & Artur Ho³da, 2016. "The Effects Of Bankruptcy On The Predictability Of Price Formation Processes On Warsaw’S Stock Market," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 12(1), pages 32-42, June.

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