Computational Experiments Successfully Predict the Emergence of Autocorrelations in Ultra-High-Frequency Stock Returns

Jian Zhou, Gao-Feng Gu, Zhi-Qiang Jiang, Xiong Xiong (), Wei Chen, Wei Zhang and Wei-Xing Zhou
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Jian Zhou: East China University of Science and Technology
Gao-Feng Gu: East China University of Science and Technology
Zhi-Qiang Jiang: East China University of Science and Technology
Xiong Xiong: Tianjin University
Wei Chen: Shenzhen Stock Exchange
Wei Zhang: Tianjin University

Computational Economics, 2017, vol. 50, issue 4, No 3, 579-594

Abstract: 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 $$H_r$$ H r . Three possible determinants embedded in the MMF model are investigated, including the Hurst index $$H_s$$ H s of order directions, the Hurst index $$H_x$$ H x and the power-law tail index $$\alpha _x$$ α x of the relative prices of placed orders. The computational experiments predict that $$H_r$$ H r is negatively correlated with $$\alpha _x$$ α x and $$H_x$$ H x and positively correlated with $$H_s$$ H s . In addition, the values of $$\alpha _x$$ α x and $$H_x$$ H x have negligible impacts on $$H_r$$ H r , whereas $$H_s$$ H s exhibits a dominating impact on $$H_r$$ H r . The predictions of the MMF model on the dependence of $$H_r$$ H r upon $$H_s$$ H s and $$H_x$$ H x are verified by the empirical results obtained from the order flow data of 43 Chinese stocks.

Keywords: Computational experiment; Order-driven model; Market efficiency; Order direction; Long memory (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10614-016-9612-1

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