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Multiscale Analysis of the Predictability of Stock Returns

Listed author(s):
  • Paweł Fiedor

    ()

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

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.

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Article provided by MDPI, Open Access Journal in its journal Risks.

Volume (Year): 3 (2015)
Issue (Month): 2 (June)
Pages: 1-15

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Handle: RePEc:gam:jrisks:v:3:y:2015:i:2:p:219-233:d:50807
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  1. Zapart, Christopher A., 2015. "Econophysics: A challenge to econometricians," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 318-327.
  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. 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.
  8. 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.
  9. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters,in: THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78 World Scientific Publishing Co. Pte. Ltd..
  10. 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.
  11. 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.
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