Tiger Research Report: Prediction Markets Evolving into Macro Data Infrastructure

ChainCatcher report: Ryan Yoon, a researcher at Tiger Research, has released the final report in a series on prediction markets, examining whether prediction markets have practical utility. The report states that participants stake capital to judge outcomes, with prices updating in real time to new information, forming probability distributions comparable to traditional polls, expert surveys, and economic forecasts. Prediction markets have expanded beyond sports and election forecasting to include macroeconomic and geopolitical data infrastructure. The report notes that Google Finance, CNBC, CNN, and The Wall Street Journal have displayed prediction market probabilities; the Intercontinental Exchange, parent company of the New York Stock Exchange, and the bond electronic trading platform Tradeweb distribute related data to institutional clients. A Federal Reserve Board working paper describes Kalshi as a high-frequency, continuously updated, and information-rich benchmark. The paper finds that Kalshi’s median and mode forecasts for the federal funds rate perfectly matched actual outcomes the day before FOMC meetings and significantly outperformed federal funds futures over the same horizon; it was more accurate than Bloomberg Consensus for overall CPI and statistically similar for core CPI and unemployment rates. The report also cites research on the Iowa Electronic Markets: in 74% of cases across five U.S. presidential elections from 1988 to 2004 and 964 polls, its predictions were more accurate; the average vote share error one week before elections was 1.5 percentage points, lower than Gallup’s final poll error of 2.1 percentage points. The report concludes that platforms must deepen liquidity to resist manipulation, engage with regulators, position themselves as monitoring tools, and maintain open, transparent data supported by independent research.