Metaorder modelling and identification from public data

TL;DR

This paper validates the Lillo-Mike-Farmer order-splitting theory of long-range correlations in financial market order flow using publicly available data from the Johannesburg Stock Exchange, demonstrating the theory's applicability across markets.

Contribution

It shows that the LMF theory can be empirically validated with public data by reconstructing synthetic metaorders, enabling broader reproducibility and cross-market analysis.

Findings

Validation of LMF theory using JSE data

Reconstruction of synthetic metaorders from public data

Effective trader numbers N=50 and N=150 tested

Abstract

Market-order flow in financial markets exhibits long-range correlations. This is a widely known stylised fact of financial markets. A popular hypothesis for this stylised fact comes from the Lillo-Mike-Farmer (LMF) order-splitting theory. However, quantitative tests of this theory have historically relied on proprietary datasets with trader identifiers, limiting reproducibility and cross-market validation. We show that the LMF theory can be validated using publicly available Johannesburg Stock Exchange (JSE) data by leveraging recently developed methods for reconstructing synthetic metaorders. We demonstrate the validation using 3 years of Transaction and Quote Data (TAQ) for the largest 100 stocks on the JSE when assuming that there are either N=50 or N=150 effective traders managing metaorders in the market.