🤖 AI Summary
This study addresses a central challenge in financial market microstructure: validating the order-splitting theory’s explanation of long-range correlations in market order flow without access to trader identity information. Leveraging three years of public TAQ data from the Johannesburg Stock Exchange, the authors propose a novel synthetic meta-order reconstruction method that relies solely on publicly available data. Under plausible assumptions of either 50 or 150 effective traders, the approach successfully replicates the autocorrelation structure of order flow predicted by the Lillo-Mike-Farmer (LMF) model. This work provides the first empirical validation of the LMF theory in the absence of trader-identified data, thereby overcoming the field’s traditional reliance on proprietary datasets and significantly enhancing the reproducibility and cross-market applicability of empirical findings.
📝 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.