Semantic Expert Following in Prediction Markets

We develop an expert-following strategy for binary prediction markets using public wallet histories. The central idea is that expertise is local to the question being traded. A fixed text encoder represents market questions, and a thresholded similarity kernel gives greater weight to a wallet's performance in closely related, settled markets. The strategy first waits for a market to meet observable volume, price, and scheduled-horizon conditions. It then aggregates qualified wallet flow, requires support from several distinct addresses, and controls both signal concentration and capital exposure. No predictive model is trained on trading outcomes. We evaluate four allocation configurations on a historical Polymarket archive with 41,891 candidate markets and 183.74 million valid public trade records. In a delayed, quantity-unconstrained price replay, the primary configuration returns 257.25% with a 13.64% daily maximum drawdown. A higher-exposure configuration returns 318.32% with a 19.02% maximum drawdown. The comparison shows how local expert selection and capital allocation interact within a common consensus-based strategy.