IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v3y2015i2p219-233d50807.html
   My bibliography  Save this article

Multiscale Analysis of the Predictability of Stock Returns

Author

Listed:
  • Paweł Fiedor

    (Cracow University of Economics, Rakowicka 27, 31-510 Kraków, Poland)

Abstract

Due to the strong complexity of financial markets, economics does not have a unified theory of price formation in financial markets. The most common assumption is the Efficient-Market Hypothesis, which has been attacked by a number of researchers, using different tools. There were varying degrees to which these tools complied with the formal definitions of efficiency and predictability. In our earlier work, we analysed the predictability of stock returns at two time scales using the entropy rate, which can be directly linked to the mathematical definition of predictability. Nonetheless, none of the above-mentioned studies allow any general understanding of how the financial markets work, beyond disproving the Efficient-Market Hypothesis. In our previous study, we proposed the Maximum Entropy Production Principle, which uses the entropy rate to create a general principle underlying the price formation processes. Both of these studies show that the predictability of price changes is higher at the transaction level intraday scale than the scale of daily returns, but ignore all scales in between. In this study we extend these ideas using the multiscale entropy analysis framework to enhance our understanding of the predictability of price formation processes at various time scales.

Suggested Citation

  • Paweł Fiedor, 2015. "Multiscale Analysis of the Predictability of Stock Returns," Risks, MDPI, vol. 3(2), pages 1-15, June.
  • Handle: RePEc:gam:jrisks:v:3:y:2015:i:2:p:219-233:d:50807
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/3/2/219/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/3/2/219/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gili Yen & Cheng-few Lee, 2008. "Efficient Market Hypothesis (EMH): Past, Present and Future," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 305-329.
    2. Aghamohammadi, Cina & Ebrahimian, Mehran & Tahmooresi, Hamed, 2014. "Permutation approach, high frequency trading and variety of micro patterns in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 25-30.
    3. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "US stock market efficiency over weekly, monthly, quarterly and yearly time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 554-564.
    4. Pawe{l} Fiedor, 2013. "Frequency Effects on Predictability of Stock Returns," Papers 1310.5540, arXiv.org, revised Nov 2013.
    5. Paweł Fiedor, 2014. "Information-theoretic approach to lead-lag effect on financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(8), pages 1-9, August.
    6. Pawe{l} Fiedor, 2014. "Maximum Entropy Production Principle for Stock Returns," Papers 1408.3728, arXiv.org.
    7. Lee, Chien-Chiang & Lee, Jun-De, 2009. "Energy prices, multiple structural breaks, and efficient market hypothesis," Applied Energy, Elsevier, vol. 86(4), pages 466-479, April.
    8. Zapart, Christopher A., 2015. "Econophysics: A challenge to econometricians," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 318-327.
    9. J. Barkley Rosser, 2008. "Econophysics And Economic Complexity," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 745-760.
    10. Rothenstein, Roland & Pawelzik, Klaus, 2005. "Limited profit in predictable stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 419-427.
    11. Cina Aghamohammadi & Mehran Ebrahimian & Hamed Tahmooresi, 2014. "Permutation approach, high frequency trading and variety of micro patterns in financial time series," Papers 1407.5254, arXiv.org.
    12. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    13. 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)

    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. Fiedor, Paweł, 2014. "Sector strength and efficiency on developed and emerging financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 180-188.
    2. Pawe{l} Fiedor, 2013. "Frequency Effects on Predictability of Stock Returns," Papers 1310.5540, arXiv.org, revised Nov 2013.
    3. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    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.
    5. Wang, Guochao & Zheng, Shenzhou & Wang, Jun, 2019. "Complex and composite entropy fluctuation behaviors of statistical physics interacting financial model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 97-113.
    6. Wang, Guochao & Zheng, Shenzhou & Wang, Jun, 2020. "Fluctuation and volatility dynamics of stochastic interacting energy futures price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    7. Roland Rothenstein, 2018. "Quantification of market efficiency based on informational-entropy," Papers 1812.02371, arXiv.org.
    8. Xiong, Xiong & Meng, Yongqiang & Li, Xiao & Shen, Dehua, 2019. "An empirical analysis of the Adaptive Market Hypothesis with calendar effects:Evidence from China," Finance Research Letters, Elsevier, vol. 31(C).
    9. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    10. Pawe{l} Fiedor, 2014. "Maximum Entropy Production Principle for Stock Returns," Papers 1408.3728, arXiv.org.
    11. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2016. "Emerging interdependence between stock values during financial crashes," Papers 1611.02549, arXiv.org.
    12. Syeda Tayyaba Ijaz & Rabia Komal, 2015. "Role Of Hurst Exponent In Prediction Of Market Efficiency In Kse-100 Index," IBT Journal of Business Studies (JBS), Ilma University, Faculty of Management Science, vol. 11(2), pages 11-14.
    13. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    14. Pawe{l} Fiedor, 2013. "Structural Changes on Warsaw's Stock Exchange: the end of Financial Crisis," Papers 1311.4230, arXiv.org.
    15. Mr. M. Awais Mehmood & Dr. Faisal Aftab & Dr. Hafiz Mushtaq, 2016. "Role Of Social Media Marketing (Smm) In Hei’S Admission," IBT Journal of Business Studies (JBS), Ilma University, Faculty of Management Science, vol. 12(1), pages 12-10.
    16. Natália Costa & César Silva & Paulo Ferreira, 2019. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies," IJFS, MDPI, vol. 7(3), pages 1-12, September.
    17. 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.
    18. Alagidede, Paul, 2011. "Return behaviour in Africa's emerging equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 133-140, May.
    19. Paulo Ferreira & Luís Carlos Loures, 2020. "An Econophysics Study of the S&P Global Clean Energy Index," Sustainability, MDPI, vol. 12(2), pages 1-9, January.
    20. Wahbeeah Mohti & Andreia Dionísio & Paulo Ferreira & Isabel Vieira, 2019. "Frontier markets’ efficiency: mutual information and detrended fluctuation analyses," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 551-572, September.

    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:gam:jrisks:v:3:y:2015:i:2:p:219-233:d:50807. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.