{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:56:49Z","timestamp":1791223009319,"version":"4.1.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Order-level data from exchanges is often restricted or costly, which makes it hard for independent researchers to build a synthetic limit order book for the venue they study. This paper shows how to build a venue-specific generator from public sources alone: published research, exchange statistics, and regulator reports. The method has four steps. We write a profile of the venue's trading day, turn its published statistics into numeric targets, add mechanisms one at a time, and check each mechanism directly. Using the Nikkei 225 mini futures on the Osaka Exchange as a worked example, the resulting generator meets 13 required targets on the five synthetic days used for calibration and, under the same pass rules, on 20 further days. The build also shows that a target can pass or fail for reasons unrelated to the mechanism it is meant to test. Trades did not move the price until that was built in; a standard impact measure passed by measuring trades that follow price moves; the price impact of trades did not fade without a slow mechanism; and the branching ratio estimated from trades understated its true value, partly through the order book and partly through the estimation procedure. The generator has not been compared with real order-level data, and all of its output is synthetic. An appendix gives a checklist for researchers building generators for other venues.<\/jats:p>","DOI":"10.2139\/ssrn.7558718","type":"posted-content","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:31:03Z","timestamp":1791221463000},"source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Limit-Order Book Generation from Public Data: A Case Study of Nikkei 225 Mini Futures"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8491-6130","authenticated-orcid":true,"given":"Kambiz","family":"Homayounfar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:31:03Z","timestamp":1791221463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7558718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7558718","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}