Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.19590 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910030046953472 |
|---|---|
| author | Goliath, Ezra Gebbie, Tim |
| author_facet | Goliath, Ezra Gebbie, Tim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19590 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Metaorder modelling and identification from public data Goliath, Ezra Gebbie, Tim Trading and Market Microstructure Computational Engineering, Finance, and Science Statistical Finance Computation 91-04, 91G10, 91G80, 82C41, 91G60, 91B26 G.3 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. |
| title | Metaorder modelling and identification from public data |
| topic | Trading and Market Microstructure Computational Engineering, Finance, and Science Statistical Finance Computation 91-04, 91G10, 91G80, 82C41, 91G60, 91B26 G.3 |
| url | https://arxiv.org/abs/2602.19590 |