Federal Reserve Study: Kalshi Is a Better Macro Forecast Accuracy Tool
Key Highlights: The Federal Reserve study finds Kalshi data offers faster, real-time signals on macroeconomic expectations. Kalshi forecasts showed accuracy on par with, and sometimes better than, traditional surveys and market indicators. Researchers say prediction markets could complement existing policy tools by revealing sentiment, distribution risks, and shifts in expectations. A research paper from the Federal Reserve reveals that prediction market data from Kalshi could improve how policymakers gauge macroeconomic expectations in real time. The study notes that these markets provide faster and more detailed hints than traditional tools such as surveys or derivatives pricing. The paper, titled “Kalshi and the Rise of Macro Markets,” is authored by Federal Reserve Board chief economist Anthony Diercks, research assistant Jared Dean Katz, and Jonathan Wright of Johns Hopkins University. The authors compared Kalshi’s market-implied forecasts with standard benchmarks including professional surveys and market-derived indicators. Is Kalshi a Superior Macroeconomic Forecast Tool Their findings show that Kalshi’s probability distributions give quick responses to latest information. These updates happen throughout the trading day. Hence, policymakers and researchers can observe changes in predictions almost immediately after economic data releases or official statements. The paper shared that managing expectations is important for modern monetary policy, and that existing tools often lack speed, frequency, or distributional detail. Kalshi continuously updates predictions for events such as interest rate decisions, inflation readings, and labor market outcomes. The researchers observed that these predictions adjust quickly to macroeconomic developments. For example, after speeches by Federal Reserve Governors Christopher Waller and Michelle Bowman, Kalshi markets increased the implied probability of a July rate cut to around 25 percent. That probability later declined when the June employment report exceeded forecasts. The research also assessed the accuracy of Kalshi forecasts. For federal funds rate decisions, the platform’s mean, median, and mode estimates performed much in line with established forecasts such as the Federal Reserve Bank of New York’s Survey of Market Expectations. In several cases, Kalshi delivered slightly lower prediction errors as key policy meetings approached. Notably, the modal forecast from Kalshi matched the actual policy outcome on the day of each Federal Open Market Committee meeting since 2022. The paper extended its analysis to inflation, unemployment, and GDP expectations. For consumer price inflation, Kalshi’s mean absolute error on the day of release was close to seven basis points, compared with roughly eight basis points for the Bloomberg consensus. For unemployment predictions, Kalshi provided a real-time distribution of outcomes. This is something that is not available in traditional options markets. The authors of the paper highlighted that these distributions offer insights into tail risks and asymmetry that point forecasts cannot capture. Statistical testing showed that in some cases Kalshi forecasts hugely outperformed the Bloomberg consensus, particularly for headline inflation. In other cases, there was no meaningful difference in accuracy. The study found no instance where Kalshi was significantly worse than established benchmarks. This consistency suggests that prediction markets can be complementary to existing tools. Prediction markets capture the beliefs of a diverse participant base, including retail traders and institutional actors. This mix can produce a wider view of sentiment than traditional surveys that depend on a limited group of professional forecasters. At the same time, the market structure allows for continuous price discovery, which hints at changing expectations about monetary policy, growth, and inflation. Notably, the Federal Reserve clarified that the paper is preliminary research intended to encourage discussion. It does not signal any immediate change in policy frameworks or decision-making processes. The central bank continues to rely on a range of indicators when assessing economic conditions and setting interest rates. Even so, the study points to another striking aspect. As liquidity improves and contract coverage expands, platforms like Kalshi may offer policymakers a more detailed and timely picture of how markets interpret incoming data. The research concludes that such tools could improve the study of monetary policy transmission, investor sentiment, and macroeconomic uncertainty over time. Also Read: Solflare Wallet Integrates Prediction Market, Powered by Kalshi