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Market mill dependence pattern in the stock market: Multiscale conditional dynamics

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  • Zaitsev, Sergey
  • Zaitsev, Alexander
  • Leonidov, Andrei
  • Trainin, Vladimir

Abstract

Market Mill is a complex dependence pattern leading to nonlinear correlations and predictability in intraday dynamics of stock prices. The present paper puts together previous efforts to build a dynamical model reflecting the market mill asymmetries. We show that certain properties of the conditional dynamics at a single time scale such as a characteristic shape of an asymmetry-generating component of the conditional probability distribution result in the “elementary” market mill pattern. This asymmetry-generating component matches the empirical distribution obtained from the market data. Multiple time scale considerations make the resulting “composite” mill similar to the empirical market mill patterns. Multiscale model also reflects a multi-agent nature of the market. Interpretation of variations of asymmetry patterns of individual stocks in terms of specific deformations of the fundamental market mill asymmetry patterns is described.

Suggested Citation

  • Zaitsev, Sergey & Zaitsev, Alexander & Leonidov, Andrei & Trainin, Vladimir, 2009. "Market mill dependence pattern in the stock market: Multiscale conditional dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(21), pages 4624-4634.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:21:p:4624-4634
    DOI: 10.1016/j.physa.2009.07.014
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    References listed on IDEAS

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    1. Jean-Philippe Bouchaud & Yuval Gefen & Marc Potters & Matthieu Wyart, 2004. "Fluctuations and response in financial markets: the subtle nature of 'random' price changes," Quantitative Finance, Taylor & Francis Journals, vol. 4(2), pages 176-190.
    2. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    3. Jean-Philippe Bouchaud & Julien Kockelkoren & Marc Potters, 2006. "Random walks, liquidity molasses and critical response in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 6(2), pages 115-123.
    4. Lillo Fabrizio & Farmer J. Doyne, 2004. "The Long Memory of the Efficient Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-35, September.
    5. Leonidov, Andrei & Trainin, Vladimir & Zaitsev, Alexander & Zaitsev, Sergey, 2007. "Market mill dependence pattern in the stock market: Modeling of predictability and asymmetry via multi-component conditional distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 240-252.
    6. Paul Lynch & Gilles Zumbach, 2003. "Market heterogeneities and the causal structure of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 320-331.
    7. Blake LeBaron, 2001. "Volatility," Computing in Economics and Finance 2001 108, Society for Computational Economics.
    8. Andrei Leonidov & Vladimir Trainin & Alexander Zatsev & Sergey Zaitsev, 2007. "Market Mill Dependence Pattern in the Stock Market: Modeling of Predictability and Asymmetry via Multi-Component Conditional Distribution," Papers physics/0701158, arXiv.org, revised Mar 2007.
    9. LeBaron, Blake & Yamamoto, Ryuichi, 2007. "Long-memory in an order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 85-89.
    10. B. LeBaron, 2001. "Stochastic volatility as a simple generator of apparent financial power laws and long memory," Quantitative Finance, Taylor & Francis Journals, vol. 1(6), pages 621-631.
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    Cited by:

    1. Vladimir Markov & Slava Mazur & David Saltz, 2014. "Design and Implementation of Schedule-Based Trading Strategies Based on Uncertainty Bands," Papers 1409.1441, arXiv.org.

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    Keywords

    Econophysics; Market mill;

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