文献类型：期刊文献

英文题名：Computational Experiments Successfully Predict the Emergence of Autocorrelations in Ultra-High-Frequency Stock Returns

作者：Zhou, Jian[1];Gu, Gao-Feng[2,3];Jiang, Zhi-Qiang[2,3];Xiong, Xiong[4,5];Chen, Wei[6];Zhang, Wei[4,5];Zhou, Wei-Xing[7,8]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[4]Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China;[5]Tianjin Univ, China Ctr Social Comp & Analyt, Tianjin 300072, Peoples R China;[6]Shenzhen Stock Exchange, 5045 Shennan East Rd, Shenzhen 518010, Peoples R China;[7]East China Univ Sci & Technol, Dept Math, Sch Business, Shanghai 200237, Peoples R China;[8]East China Univ Sci & Technol, Res Ctr Econophys, Sch Business, Shanghai 200237, Peoples R China

年份：2017

卷号：50

期号：4

起止页码：579

外文期刊名：COMPUTATIONAL ECONOMICS

收录：;WOS:【SSCI(收录号:WOS:000413954900003)，SCI-EXPANDED(收录号:WOS:000413954900003)】；

基金：Zhi-Qiang Jiang, Gao-Feng Gu and Wei-Xing Zhou received support from the National Natural Science Foundation of China (71501072) and the Fundamental Research Funds for the Central Universities. Xiong Xiong and Wei Zhang received support from the National Natural Science Foundation of China (71532009,71131007) and the Program for Changjiang Scholars and Innovative Research Team in University (IRT1028). Wei Chen received support from the National Natural Science Foundation of China (71571121).

语种：英文

外文关键词：Computational experiment; Order-driven model; Market efficiency; Order direction; Long memory

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.