On the distribution of high-frequency stock market traded volume: a dynamical scenario

TL;DR

This paper proposes a stochastic dynamical model for high-frequency stock traded volume, linking it to nonextensive statistical mechanics and agent herding behavior, validated with NASDAQ data.

Contribution

It introduces a novel dynamical scenario connecting high-frequency volume distributions with nonextensive statistics and agent herding, supported by empirical NASDAQ data modeling.

Findings

Model accurately fits NASDAQ volume distributions

Links volume fluctuations to agent herding behavior

Supports nonextensive statistical mechanics framework

Abstract

This manuscript reports a stochastic dynamical scenario whose associated stationary probability density function is exactly a previously proposed one to adjust high-frequency traded volume distributions. This dynamical conjecture, physically connected to superstatiscs, which is intimately related with the current nonextensive statistical mechanics framework, is based on the idea of local fluctuations in the mean traded volume associated to financial markets agents herding behaviour. The corroboration of this mesoscopic model is done by modelising NASDAQ 1 and 2 minute stock market traded volume.