Scaling in the distribution of intertrade durations of Chinese stocks

TL;DR

This study reveals a universal scaling pattern in intertrade durations of Chinese stocks, modeled effectively by Weibull and Tsallis distributions, with Weibull providing a better fit for most data.

Contribution

It demonstrates a scaling pattern in intertrade durations across multiple stocks and compares Weibull and Tsallis models, highlighting Weibull's superior fit for most durations.

Findings

Scaling pattern observed in intertrade durations

Weibull distribution outperforms q-exponential for most data

Intertrade durations follow a Weibull distribution with a power-law tail

Abstract

The distribution of intertrade durations, defined as the waiting times between two consecutive transactions, is investigated based upon the limit order book data of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. A scaling pattern is observed in the distributions of intertrade durations, where the empirical density functions of the normalized intertrade durations of all 23 stocks collapse onto a single curve. The scaling pattern is also observed in the intertrade duration distributions for filled and partially filled trades and in the conditional distributions. The ensemble distributions for all stocks are modeled by the Weibull and the Tsallis $q$-exponential distributions. Maximum likelihood estimation shows that the Weibull distribution outperforms the $q$-exponential for not-too-large intertrade durations which account for more than 98.5% of the data. Alternatively, nonlinear least-squares estimation selects the $q$-exponential as a better model, in which the optimization is conducted on the distance between empirical and theoretical values of the logarithmic probability densities. The distribution of intertrade durations is Weibull followed by a power-law tail with an asymptotic tail exponent close to 3.