Empirical properties of inter-cancellation durations in the Chinese stock market

Frontiers in Physics

Gu, Gao-Feng¹  Xiong, Xiong²  Zhang, Yong-Jie³  Zhang, Wei⁴  Zhou, Wei-Xing⁴ 

[1] East China University of Science and Technology, Departmentof Finance / School of Business, 130 Meilong Road, Shanghai, 200237, China;East China University of Science and Technology, Research Center for Econophysics, 130 meilong road, shanghai, 200237, China;Tianjin University, China Center for Social Computing and Analytics, Tianjin, China;Tianjin University, College of Management and Economics, Tianjin, China

关键词: econophysics;    Order flow;    inter-cancellation duration;    Probability distribution;    Memory effect;    Multifractal nature;   

DOI  :  10.3389/fphy.2014.00016

学科分类：物理（综合）

来源: Frontiers

【 摘 要 】

Order cancellation process plays a crucial role in the dynamics of price formation in order-driven stock markets and is important in the construction and validation of computational finance models. Based on the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate the empirical statistical properties of inter-cancellation durations in units of events defined as the waiting times between two consecutive cancellations. The inter-cancellation durations for both buy and sell orders of all the stocks favor a $q$-exponential distribution when the maximum likelihood estimation method is adopted; In contrast, both cancelled buy orders of 9 stocks and cancelled sell orders of 4 stocks prefer Weibull distribution when the nonlinear least-square estimation is used. Applying detrended fluctuation analysis (DFA), centered detrending moving average (CDMA) and multifractal detrended fluctuation analysis (MF-DFA) methods, we unveil that the inter-cancellation duration time series process long memory and multifractal nature for both buy and sell cancellations of all the stocks. Our findings show that order cancellation processes exhibit long-range correlated bursty behaviors and are thus not Poissonian.

【 授权许可】

CC BY