{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T07:30:24Z","timestamp":1793431824563,"version":"4.5.0"},"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:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>This paper develops a likelihood ratio framework for ranking wallets whose trades may contain information not yet reflected in a binary prediction market price. The score combines a market level prior, an order flow changepoint term, and wallet level behavioral likelihood ratios. The framework replaces the original probability of informed trading measure with an adjusted PIN specification that permits symmetric order flow shocks, calibrates prior probabilities to a stated wallet level prevalence, and separates ranking scores from multiple testing decisions. Several analytical results establish when the component scores are valid likelihood ratio updates and where their assumptions matter. We correct the outcome evidence bound, distinguish exact sign randomization p-values from mid-p values, and clarify that the latter do not automatically inherit standard Benjamini-Hochberg guarantees. Reported simulations show strong discrimination in a balanced synthetic setting, while the live analysis contains only four labelled pairs from two wallets and is too sparse to validate detection performance. The framework is therefore best viewed as a calibrated screening method, not as proof of insider trading.<\/jats:p>","DOI":"10.2139\/ssrn.7670998","type":"posted-content","created":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T06:36:10Z","timestamp":1793428570000},"source":"Crossref","is-referenced-by-count":0,"title":["Scoring Informed Trading in Prediction Markets: A Likelihood Ratio Framework"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4702-1354","authenticated-orcid":true,"given":"Ahura","family":"Azadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T06:36:10Z","timestamp":1793428570000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7670998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7670998","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}