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 Hr. Three possible determinants embedded in the MMF model are investigated,
including the Hurst index Hs of order directions, the Hurst index Hx and the
power-law tail index αx of the relative prices of placed orders. The computational
experiments predict that Hr is negatively correlated with αx and Hx and positively
correlated with Hs. In addition, the values of αx and Hx have negligible impacts on
Hr, whereas Hs exhibits a dominating impact on Hr. The predictions of the MMF

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