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Computational experiments successfully predict the emergence of autocorrelations in ultra-high-frequency stock returns

Author

Listed:
  • Jian Zhou

    (ECUST)

  • Gao-Feng Gu

    (ECUST)

  • Zhi-Qiang Jiang

    (ECUST)

  • Xiong Xiong

    (TJU)

  • Wei Chen

    (SZSE)

  • Wei Zhang

    (TJU)

  • Wei-Xing Zhou

    (ECUST)

Abstract

Social and economic systems are complex adaptive systems, in which heterogenous agents interact and evolve in a self-organized manner, and macroscopic laws emerge from microscopic properties. To understand the behaviors of complex systems, computational experiments based on physical and mathematical models provide a useful tools. Here, we perform computational experiments using a phenomenological order-driven model called the modified Mike-Farmer (MMF) to predict the impacts of order flows on the autocorrelations in ultra-high-frequency returns, quantified by Hurst index $H_r$. Three possible determinants embedded in the MMF model are investigated, including the Hurst index $H_s$ of order directions, the Hurst index $H_x$ and the power-law tail index $\alpha_x$ of the relative prices of placed orders. The computational experiments predict that $H_r$ is negatively correlated with $\alpha_x$ and $H_x$ and positively correlated with $H_s$. In addition, the values of $\alpha_x$ and $H_x$ have negligible impacts on $H_r$, whereas $H_s$ exhibits a dominating impact on $H_r$. The predictions of the MMF model on the dependence of $H_r$ upon $H_s$ and $H_x$ are verified by the empirical results obtained from the order flow data of 43 Chinese stocks.

Suggested Citation

  • Jian Zhou & Gao-Feng Gu & Zhi-Qiang Jiang & Xiong Xiong & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2014. "Computational experiments successfully predict the emergence of autocorrelations in ultra-high-frequency stock returns," Papers 1404.1051, arXiv.org, revised Feb 2018.
  • Handle: RePEc:arx:papers:1404.1051
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    References listed on IDEAS

    as
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    Cited by:

    1. Kei Katahira & Yu Chen & Gaku Hashimoto & Hiroshi Okuda, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Papers 1902.02040, arXiv.org.
    2. repec:eee:phsmap:v:524:y:2019:i:c:p:503-518 is not listed on IDEAS
    3. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yong-Jie Zhang & Wei Chen & Wei-Xing Zhou, 2017. "An empirical behavioural order-driven model with price limit rules," Papers 1704.04354, arXiv.org.
    4. repec:eee:ememar:v:38:y:2019:i:c:p:458-467 is not listed on IDEAS
    5. repec:eee:phsmap:v:509:y:2018:i:c:p:1152-1161 is not listed on IDEAS
    6. repec:eee:finlet:v:28:y:2019:i:c:p:348-354 is not listed on IDEAS

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