Tiger Research Report Highlights Predictive Markets as Macro Data Infrastructure

Huo Xing Cai Jing reports that 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 reflect 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 cites examples such as Google Finance, CNBC, CNN, and The Wall Street Journal, which 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 notes that Kalshi’s median and mode forecasts for the federal funds rate recorded perfect accuracy 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 comparable for core CPI and unemployment rates. The report also references research on the Iowa Electronic Markets: in 74% of cases across five U.S. presidential elections from 1988 to 2004 and 964 polls, its forecasts 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 suggests that platforms must deepen liquidity to resist manipulation, communicate with regulators, position themselves as monitoring tools, and maintain open, transparent data supported by independent research.