Prediction market smart money shows varied success and trading styles
Beyond Profit Rankings: Identifying, Understanding, and Evaluating Smart Money in Prediction Markets
Computational Engineering, Finance, and Science
Summary
Prediction markets let people bet on future events, and some traders, called smart money, seem to do well over time. The authors studied a popular decentralized market called Polymarket by looking at real trading data to understand these traders' behavior and if copying them is profitable. They found that these successful traders use many different strategies and that copying them doesn't always guarantee making money. The study also created a system to explain why smart money makes certain trades and tested a method to copy their trades live.
What this means in practice
- •For trading platform operators: Build tools that identify distinct trader types to provide tailored features based on smart money behaviors in decentralized markets.
- •For investment strategy developers: Design and test trading strategies informed by multifaceted profiles of historically profitable wallets rather than relying solely on profit rankings.
Authors
Huaiyu Jia, Shuo Sun
Abstract
Prediction markets have emerged as an important mechanism for aggregating information and trading beliefs about future events. The transparency of on-chain transactions has enabled investors to identify historically profitable wallets, commonly referred to as smart money, and replicate their trading activities through copy trading. However, despite the growing popularity of smart-money tracking, systematic research on these wallets' trading behavior, profitability persistence, and the economic feasibility of following their trades remains limited. To address this gap, we conduct a comprehensive empirical study of Polymarket, the most popular decentralized prediction market, by reconstructing wallet-level trading activities from on-chain transactions, position changes, market histories, and settlement outcomes. We first examine the distribution and persistence of historical profitability using statistical analysis and logistic regression, and further characterize heterogeneous trading strategies through multidimensional behavioral profiling and unsupervised clustering. Building on these findings, we develop an evidence-grounded multi-agent research framework that integrates wallet behavior, market conditions, and real-world information to generate interpretable and verifiable explanations of individual trades. Finally, we implement a real-time copy-trading system and evaluate its practical feasibility through order-book-aware paper trading and live on-chain execution. Our findings reveal substantial behavioral heterogeneity among historically profitable wallets and highlight the challenges of translating observed trading performance into replicable economic returns.