Tiger Research Predicts Market as Macro Data Infrastructure

Tiger Research, a research organization, has released a report discussing the practical utility of prediction markets. The report points out that participants bear the financial consequences of their judgments, with prices updated in real-time, forming probability distributions comparable to traditional polls, expert surveys, and economic forecasts. Prediction markets have expanded from sports and election forecasting to macroeconomic and geopolitical data infrastructure. The report mentions that Google Finance, CNBC, CNN, and The Wall Street Journal have showcased prediction market probabilities, while the Intercontinental Exchange, the parent company of the New York Stock Exchange, and the electronic bond trading platform Tradeweb distribute related data to institutional clients. A working paper from the Federal Reserve Board describes the prediction market Kalshi as a high-frequency, continuously updated, and information-rich benchmark, noting that Kalshi's median and mode predictions for the federal funds rate were perfectly recorded the day before the FOMC meeting, significantly outperforming federal funds futures. Compared to Bloomberg consensus, it is more accurate for overall CPI and statistically similar for core CPI and unemployment rate. The report cites research from the Iowa Electronic Markets, showing that in five U.S. presidential elections from 1988 to 2004, it was more accurate 74% of the time, with an average voting share error of 1.5 percentage points one week before the election, lower than Gallup's final poll error of 2.1 percentage points. The report suggests that platforms need to deepen liquidity to guard against manipulation and communicate with regulators to maintain open transparency and independent research-supported data.

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