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

• Published in: Computational economics 2017-12, Vol.50 (4), p.579-594
• Main Authors: Zhou, Jian, Gu, Gao-Feng, Jiang, Zhi-Qiang, Xiong, Xiong, Chen, Wei, Zhang, Wei, Zhou, Wei-Xing
• Format: Article
• Language: English
• Subjects: Adaptive systems; Behavioral/Experimental Economics; Complex systems; Computation; Computer Appl. in Social and Behavioral Sciences; Economic systems; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Experiments; Math Applications in Computer Science; Mathematical models; Operations Research/Decision Theory; Power; Prices; Software
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-016-9612-1

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 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 α x of the relative prices of placed orders. The computational experiments predict that H r is negatively correlated with α x and H x and positively correlated with H s . In addition, the values of α 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.