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

摘要

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