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Social and economic systems are complex adaptive systems, in whichheterogenous agents interact and evolve in a self-organized manner, andmacroscopic laws emerge from microscopic properties. To understand thebehaviors of complex systems, computational experiments based on physical andmathematical models provide a useful tools. Here, we perform computationalexperiments using a phenomenological order-driven model called the modifiedMike-Farmer (MMF) to predict the impacts of order flows on the autocorrelationsin ultra-high-frequency returns, quantified by Hurst index $H_r$. Threepossible determinants embedded in the MMF model are investigated, including theHurst index $H_s$ of order directions, the Hurst index $H_x$ and the power-lawtail index $\alpha_x$ of the relative prices of placed orders. Thecomputational experiments predict that $H_r$ is negatively correlated with$\alpha_x$ and $H_x$ and positively correlated with $H_s$. In addition, thevalues 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 thedependence of $H_r$ upon $H_s$ and $H_x$ are verified by the empirical resultsobtained from the order flow data of 43 Chinese stocks.

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