Trading activity as driven Poisson process: comparison with empirical data

TL;DR

This paper introduces a Poissonian-like point process model with a slowly varying mean rate to describe trading activity across multiple stocks, providing a universal framework that fits empirical NYSE data.

Contribution

The study develops a scaled stochastic differential equation model that captures trading activity with universal parameters applicable to various stocks.

Findings

Model fits empirical trading data well

Parameters are consistent across different stocks

Provides a universal description of trading activity

Abstract

We propose the point process model as the Poissonian-like stochastic sequence with slowly diffusing mean rate and adjust the parameters of the model to the empirical data of trading activity for 26 stocks traded on NYSE. The proposed scaled stochastic differential equation provides the universal description of the trading activities with the same parameters applicable for all stocks.