IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1408.3728.html
   My bibliography  Save this paper

Maximum Entropy Production Principle for Stock Returns

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1408.3728
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paweł Fiedor, 2015. "Multiscale Analysis of the Predictability of Stock Returns," Risks, MDPI, vol. 3(2), pages 1-15, June.
    2. Choi, Gahyun & Park, Kwangyeol & Yi, Eojin & Ahn, Kwangwon, 2023. "Price fairness: Clean energy stocks and the overall market," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    4. Barunik, Jozef & Krehlik, Tomas, 2016. "Measuring the frequency dynamics of financial and macroeconomic connectedness," FinMaP-Working Papers 54, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    5. Ewing, Bradley T. & Malik, Farooq, 2016. "Volatility spillovers between oil prices and the stock market under structural breaks," Global Finance Journal, Elsevier, vol. 29(C), pages 12-23.
    6. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00611932, HAL.
    7. Fryzlewicz, Piotr & Oh, H. S., 2011. "Thick pen transformation for time series," LSE Research Online Documents on Economics 37663, London School of Economics and Political Science, LSE Library.
    8. Misheck Mutize & Sean J. Gossel, 2019. "Sovereign Credit Rating Announcement Effects on Foreign Currency Denominated Bond and Equity Markets in Africa," Journal of African Business, Taylor & Francis Journals, vol. 20(1), pages 135-152, January.
    9. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    10. Renzo Pardo Figueroa & Gabriel Rodríguez, 2014. "Distinguishing between True and Spurious Long Memory in the Volatility of Stock Market Returns in Latin America," Documentos de Trabajo / Working Papers 2014-395, Departamento de Economía - Pontificia Universidad Católica del Perú.
    11. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    12. L. Ingber, 1984. "Statistical mechanics of neocortical interactions. Derivation of short-term-memory capacity," Lester Ingber Papers 84ni, Lester Ingber.
    13. Luis Alberiko & OlaOluwa S. Yaya & Olarenwaju I. Shittu, 2015. "Fractional integration and asymmetric volatility in european, asian and american bull and bear markets. Applications to high frequency stock data," NCID Working Papers 07/2015, Navarra Center for International Development, University of Navarra.
    14. L. Ingber, 1986. "Riemannian contributions to short-ranged velocity-dependent nucleon-nucleon interactions," Lester Ingber Papers 86rc, Lester Ingber.
    15. Jozef Barunik & Mattia Bevilacqua & Radu Tunaru, 2022. "Asymmetric Network Connectedness of Fears," The Review of Economics and Statistics, MIT Press, vol. 104(6), pages 1304-1316, November.
    16. Nazlioglu, Saban & Gupta, Rangan & Gormus, Alper & Soytas, Ugur, 2020. "Price and volatility linkages between international REITs and oil markets," Energy Economics, Elsevier, vol. 88(C).
    17. J. Polzehl & V. Spokoiny & C. Starica, 2004. "When did the 2001 recession really start?," Econometrics 0411017, University Library of Munich, Germany.
    18. Feiyu Jiang & Dong Li & Ke Zhu, 2019. "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model," Papers 1907.04147, arXiv.org, revised Oct 2020.
    19. Julien Idier., 2006. "Stock exchanges industry consolidation and shock transmission," Working papers 159, Banque de France.
    20. L. Ingber & J.K. Wilson, 1999. "Volatility of volatility of financial markets," Lester Ingber Papers 99vv, Lester Ingber.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1408.3728. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.