Order flow in the financial markets from the perspective of the Fractional L\'evy stable motion

TL;DR

This paper investigates order flow in financial markets using fractional Lévy stable motion, revealing that order size distributions influence persistence and anti-correlation in order disbalance time series.

Contribution

It introduces a fractional Lévy stable motion framework to analyze order flow, highlighting the impact of power-law distributed order sizes on market dynamics.

Findings

Persistence in order flow linked to power-law order size distribution

Stable anti-correlation estimates across multiple stocks

Burst duration analysis supports the significance of power-law distributions

Abstract

It is a challenging task to identify the best possible models based on given empirical data of observed time series. Though the financial markets provide us with a vast amount of empirical data, the best model selection is still a big challenge for researchers. The widely used long-range memory and self-similarity estimators give varying values of the parameters as these estimators themselves are developed for the specific models of time series. Here we investigate from the general fractional L\'evy stable motion perspective the order disbalance time series constructed from the limit order book data of the financial markets. Our results suggest that previous findings of persistence in order flow could be related to the power-law distribution of order sizes and other deviations from the normal distribution. Still, orders have stable estimates of anti-correlation for the 18 randomly selected stocks when Absolute value and Higuchi's estimators are implemented. Though the burst duration analysis based on the first passage problem of time series and implemented in this research gives slightly higher estimates of the Hurst and memory parameters, it qualitatively supports the importance of the power-law distribution of order sizes.