Stock markets are efficient in the weak form in the sense that no significant autocorrelations can be identified in the returns. However, the microscopic mechanisms are unclear. We aim at understanding the impacts of order flows on the weak-form efficiency through computational experiments based on an empirical order-driven model  . Three possible determinants embedded in the  model are investigated, including the tail heaviness of relative prices of the placed orders characterized by the tail index \alpha_x , the degree of long memory in relative prices quantified by its Hurst index H_x, and the strength of long memory in order direction depicted by H_x. It is found that the degree of autocorrelations in returns (quantified by its Hurst index 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. Our results suggest that stock markets are complex adaptive systems and URLanize to a critical state in which the returns are not correlated .
Social and economic systems are complex adaptive systems, in which heterogenous agents interact and evolve in a URLanized 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 .
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Stock markets are efficient in the weak form in the sense that no significant autocorrelations can be identified in the returns. However, the microscopic mechanisms are unclear. We aim at understanding the impacts of order flows on the weak-form efficiency through
Social and economic systems are complex adaptive systems, in which heterogenous agents interact and evolve in a URLanized manner, and macroscopic laws emerge from microscopic properties. To understand the behaviors of complex systems,
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an empirical
physical and mathematical models provide a useful tools. Here, we perform computational experiments using a phenomenological
A
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
A
MMF
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tail heaviness of relative prices of the placed orders characterized by the
Hurst index H_s of order directions, the Hurst index H_x and the power-law
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, the degree of long memory in relative prices quantified by its Hurst index H_x, and the strength of long memory in order direction depicted by H_x. It is found that the degree of autocorrelations in returns (quantified by its Hurst index H_r )
of the relative prices of placed orders. The computational experiments predict that H_r
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Our results suggest that stock markets are complex adaptive systems and URLanize to a critical state in which the returns are not correlated
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