Kalshi for Academic Economists: Using Event Contract Data to Study Monetary Policy Expectations and Rate Hikes

Central banks have long struggled to measure what markets and the public actually expect from future monetary policy. Traditional surveys of economists are infrequent, backward-looking, and subject to herding bias. Forward guidance from the Federal Reserve itself shapes expectations rather than simply revealing them. A regulated prediction market operating under financial regulatory oversight offers a different signal: real-time, stake-backed beliefs about specific outcomes tied to measurable economic indicators and policy decisions. Kalshi, an exchange where participants trade Event Contracts priced between $0 and $100 to reflect probability estimates, creates a data source that academic economists and central bank researchers can analyze to understand collective expectations about inflation, employment, and rate decisions.

The distinction between asking economists what they think will happen and observing what market participants are willing to pay to bet on it carries methodological weight. A prediction market embeds opportunity cost and conviction into every quote. When a contract reflecting the probability of a Federal Reserve rate hike within a specified window trades at $72, that price aggregates information from thousands of participants with heterogeneous beliefs, risk tolerances, and access to data. The result is not a forecast from a single institution but a quantifiable outcome reflecting distributed intelligence. For researchers studying monetary policy transmission, expectations formation, and the relationship between real-time sentiment and subsequent central bank action, this represents a rich and largely underexploited resource.

Why prediction market prices carry information traditional surveys cannot

The Federal Reserve Bank of Philadelphia conducts the Survey of Professional Forecasters quarterly. It asks economists to provide point estimates for inflation, unemployment, real GDP growth, and other variables. The results inform policymakers and researchers about expert consensus. Yet surveys have embedded limitations. They ask about multi-quarter or annual outcomes, not discrete policy decisions. They sample a fixed panel that may not represent all relevant participants in financial and real economies. They are conducted infrequently, creating gaps between survey rounds where views evolve but data does not. Respondents face no cost for forecast error, removing the financial discipline that makes markets selective.

A prediction market operates under different incentives. Participants who hold positions have capital at risk. If a contract reflecting the probability of a 25-basis-point rate hike trades at $58, a participant who believes the true probability is 70 percent can buy at $58 and profit if the contract resolves at $100. A participant who believes it is 45 percent can sell. The price discovery process continues continuously as new information arrives—employment data, inflation reports, Fed communications, financial conditions—and participants adjust positions accordingly. This creates an evolving, real-time estimate of collective expectations about quantifiable outcomes without requiring a researcher to survey anyone.

The information content of market prices in prediction markets has been documented across political elections, sports contests, and weather outcomes. Prices tend to outperform individual expert forecasts and survey aggregates, especially when participants have genuine financial exposure and the outcome is objectively verifiable. Because Kalshi contracts are tied to specific, well-defined economic indicators and policy decisions—not vague statements about “generally rising” expectations—the translation from price to probability is mechanically straightforward. A contract priced at $75 reflects a 75 percent market-implied probability that the stated event will occur.

For monetary policy specifically, this matters because the Federal Reserve’s own communications strategy depends partly on moving expectations without necessarily moving policy rates. If forward guidance alone can reduce long-term rates by shifting beliefs about future policy, economists need an accurate, timely measure of what beliefs are actually changing. Traditional surveys become stale within days. A prediction market contract remains current through every trading session, allowing researchers to track the day-to-day, hour-to-hour evolution of expectations in response to Fed communication, data releases, and financial stress.

Mapping Fed decision probabilities across meeting cycles

The Federal Reserve holds monetary policy meetings on a regular schedule, typically eight per calendar year. Each meeting concludes with a decision on the federal funds rate target. Kalshi offers contracts on many of these specific outcomes: will the Fed raise, hold, or cut rates at the June meeting? Within that, will a 25-basis-point hike occur, or is the probability distributed across 50 basis points or no change? By constructing a time series of contract prices leading up to and away from each decision, researchers can observe how market expectations about future policy evolved in response to economic data and Fed communication.

This methodology yields several research advantages. First, it creates a high-frequency measure of expectations that can be compared against the timing of specific economic releases. When the Bureau of Labor Statistics announces a monthly jobs report, researchers can observe whether and how quickly Kalshi contract prices for the next Fed meeting adjust. This helps identify which data series matter most to market participants and how different employment figures (payroll growth, unemployment rate, wage growth, labor force participation) are weighted relative to each other. The prediction market aggregates this weighting across thousands of participants without requiring a researcher to poll them.

Second, the method captures tail risks and uncertainty, not only the modal expectation. A contract priced at $65 reflects a 65 percent probability of the stated event, which means a 35 percent probability that it will not occur. By analyzing the full distribution of contract prices across multiple scenarios—a 25-basis-point hike, a 50-basis-point hike, no change, and a cut—researchers can measure how much uncertainty market participants perceive. Higher uncertainty manifests as more balanced pricing across outcomes. When the Fed’s next move becomes less certain, prices should spread; when consensus hardens, prices should cluster near extremes. This heterogeneity carries information about market participants’ confidence in their own expectations.

Third, cross-contract comparisons reveal what markets price in to future decisions based on current conditions. If a contract on the June Fed decision trades at $72 (implying 72 percent probability of a hike) and the July contract trades at $38 (implying 38 percent probability), the difference reflects market expectations about how economic data between June and July might influence the decision. By charting these conditional probabilities across multiple future meetings, researchers can construct an implied path of policy over quarters, then compare it against what the Fed itself has communicated in forward guidance and what survey respondents predicted. Divergences between market-implied and communicated expectations suggest either that markets are skeptical of forward guidance or that the Fed’s own communication is not reaching participants clearly.

Linking inflation expectations to labor market signals

The Federal Reserve has a dual mandate: price stability and maximum employment. In practice, the relationship between inflation and unemployment has been unstable, particularly during periods of financial stress or supply shocks. Academic economists and policymakers debate whether the Phillips Curve—the traditional inverse relationship between inflation and unemployment—remains operative. Kalshi contracts on both economic indicators and forecasting provide a new lens for this debate.

Contracts tracking the Consumer Price Index, the Producer Price Index, core inflation, wage growth, unemployment rates, and non-farm payroll employment create a data structure in which researchers can observe how market participants simultaneously expect these variables to co-move. If a participant believes inflation will remain elevated, do they also expect the Fed to raise rates more than currently priced in? Do higher unemployment expectations correlate with lower inflation expectations, as traditional theory suggests, or has the relationship fragmented? By analyzing Kalshi contract positions and prices, economists can quantify the strength and direction of these relationships as perceived by market participants in real time.

This is particularly valuable during periods when the Phillips Curve appears to break down. From 2010 to 2019, unemployment fell steadily while inflation remained subdued, puzzling many observers who expected tighter labor markets to push wages and prices higher. During 2021 to 2023, inflation surged while unemployment remained low, challenging again whether the traditional trade-off held. Prediction market data from this period—if available retroactively and if contracts had tracked these specific outcomes—could help researchers identify when and why market participants shifted their beliefs about inflation-unemployment co-movement. The collective expectations embedded in Kalshi pricing reflect whether the market thinks the Phillips Curve is alive or dormant, and how that belief itself might change if new data arrives.

The advantage over survey data is temporal resolution and cost. A researcher can observe market expectations about both inflation and unemployment continuously rather than waiting for quarterly survey releases. The cost of gathering this information is zero for academic researchers who gain access to historical data; market participants have already paid the cost through trading, revealing their true beliefs through capital commitment.

Using contract data to validate central bank communication effectiveness

The Federal Reserve communicates monetary policy intentions through multiple channels: official statements, press conferences, speeches by governors and presidents of regional Federal Reserve banks, and financial markets’ interpretation of all of the above. One persistent question in monetary policy research is whether the Fed’s forward guidance actually shifts expectations in the direction it intends. If the Fed says “rates will remain elevated,” does that language translate into market expectations of higher rates for longer, or do participants interpret it differently based on economic data?

Prediction market contracts offer a way to measure this directly. Researchers can identify specific Fed communications—a Chair’s testimony to Congress, a policy decision statement, a speech on future rate expectations—then observe Kalshi contract prices immediately before and after. If a contract pricing the probability of a rate hike at the next meeting was trading at $45 before a hawkish Fed Chair statement, and it moves to $62 afterward, that price movement quantifies the communication’s market impact. Over many such events, patterns emerge: Do certain types of communication move markets more? Are market participants more responsive to data-dependent language or explicit guidance about future decisions?

This research methodology is more rigorous than asking survey respondents whether they changed their views based on a Fed speech. Market prices reveal actual behavioral response through capital allocation. If a participant did not really believe the Fed’s hawkish message, they would not bid up the probability contract. If the message was ambiguous, prices might move less than hawkish language would predict, suggesting that markets are discounting the Fed’s credibility or interpreting the statement as less aggressive than public commentary suggests.

For central banks themselves, this feedback mechanism has strategic value. If a central bank wants to understand whether its communication is effective, rather than relying on post-meeting surveys or analyst commentary, it can examine real-time prediction market pricing. You can read more about how economic institutions are beginning to use these data sources to validate and refine their own forecasting and communication strategies. The lag between intention and market understanding becomes visible, revealing where clarification or repeated messaging might be necessary.

Constructing datasets and addressing methodological challenges

Converting Kalshi contract prices into usable research datasets requires careful specification. A researcher must identify which contracts are relevant to the research question, ensure that the event definition and resolution criteria are clear and consistent, gather price and volume data over the entire trading window, and establish a method for converting prices into probability estimates. For most prediction markets, the conversion is direct: a contract priced at $75 reflects a 75 percent market-implied probability. But this assumes efficient pricing and liquidity. Contracts with low trading volume, wide bid-ask spreads, or concentrated position-holders may not aggregate information as cleanly.

One methodological challenge is the distinction between unconditional and conditional expectations. A contract asking “Will the Fed raise rates at its June meeting?” reflects an unconditional probability: what is the market’s overall assessment? A contract asking “Will the Fed raise rates at its June meeting if inflation exceeds 3.5 percent by May?” would be conditional. Most Kalshi economic contracts are unconditional, which makes them more directly comparable to survey data but less useful for studying how expectations change based on specific data scenarios. Researchers building their own conditional expectation measures must infer them from multiple contract prices or design survey instruments to capture them separately.

A second challenge is selection bias in the participant pool. Kalshi participants may not represent the full distribution of economic beliefs in the population or even among professional forecasters. They are self-selected: people interested enough in prediction markets to open accounts and trade. They may disproportionately include financial professionals, data enthusiasts, or individuals with strong prior beliefs about inflation and monetary policy. Researchers should acknowledge this composition when interpreting results. Market-implied expectations from Kalshi may differ from survey expectations or Fed funds futures prices precisely because the participant pool differs.

A third consideration is contract definition and resolution risk. If a Kalshi contract on a specific inflation target is worded ambiguously—for example, if it refers to “Core CPI inflation” without specifying the measurement period or seasonal adjustment—resolution disputes can arise. Researchers using the data should verify that contract specifications align with official economic statistics to ensure that trading prices reflect genuine expectations about the economic indicator rather than uncertainty about which version of the data will be used to resolve the contract.

Comparing prediction market data to Fed futures and survey forecasts

The academic study of monetary policy expectations is not new, but the data sources have been limited. Fed funds futures contracts, traded on the Chicago Mercantile Exchange, have long provided a market-implied measure of future federal funds rates. Surveys including the Survey of Professional Forecasters, the Blue Chip consensus, and the Wall Street Journal’s quarterly poll provide expert opinions. Each source has strengths and weaknesses. Futures are liquid and updated frequently but are tied to interest rates rather than discrete policy decisions. Surveys are infrequent and subject to known biases, but they ask directly about outcomes and expectations over specific horizons.

Kalshi contracts occupy a middle ground. They are updated in real time like futures, they are tied to specific policy decisions and economic outcomes like surveys, and they have lower capital requirements and more granular outcome definitions than Fed funds futures. A researcher studying whether market participants understand the Fed’s reaction function—the systematic relationship between economic conditions and policy decisions—could use Kalshi data to identify which combinations of inflation, unemployment, and financial conditions market participants expect to trigger policy changes. By comparing these expectations across data sources, researchers can assess consistency and identify where different markets or participant groups diverge.

For example, if Fed funds futures price in a 60 percent probability of a rate cut by a specific date, but a Kalshi contract on that same decision prices in a 45 percent probability, what explains the gap? One possibility is that the futures market has lower trading costs and includes more institutional participants, who may have different information or risk preferences. Another is that the contract definitions differ subtly. A researcher investigating this discrepancy gains insight into both market microstructure and the fundamental question of what different participant pools believe about future policy.

Building a research agenda around prediction market monetary policy data

Academic economists and central bank researchers are beginning to recognize prediction markets as a data source, but systematic exploitation of this resource remains limited. Several research directions suggest themselves. First, a time-series analysis of Kalshi economic indicator and policy contracts could establish baseline relationships between real-time market expectations and subsequent economic releases. How much does the unemployment contract price change when the jobs report arrives? What is the autocorrelation of expectations about future inflation? Does uncertainty about economic variables predict volatility in policy decision probabilities?

Second, event studies of Fed communication could measure the causal effect of specific statements on market expectations. A Fed Chair’s speech or congressional testimony offers a natural experiment. By comparing Kalshi contract prices before and after the event, controlling for other news arriving during the same window, researchers can isolate the communication effect. Repeated analysis across many events could identify which types of communication move markets most effectively and whether the Fed’s messaging priorities align with what markets actually price in as important.

Third, cross-country comparative research could use similar prediction market data from other jurisdictions to examine whether expectations about monetary policy are globally synchronized or region-specific. If European central bank decision probabilities and inflation expectations diverge from U.S. market expectations, researchers can investigate whether this reflects genuine differences in economic fundamentals or differences in central bank credibility and communication.

Fourth, machine learning and natural language processing could extract information from trader commentary, Kalshi’s platform documentation, and financial news to build models predicting contract price movements. This would help identify which information sources drive expectation changes and whether any subset of information is reliably predictive of subsequent policy decisions.

Institutional access, data quality, and future evolution

Currently, Kalshi operates as a regulated exchange open to individual and institutional participants. Academic researchers and central bank staff can in principle access historical contract data and real-time prices through the platform’s public interface or through data partnerships. As the platform grows and becomes more widely known, the quality of data should improve through greater liquidity and more diverse participant pools. The Federal Reserve and other central banks have shown increasing interest in prediction markets as a research and monitoring tool, suggesting that formal data-sharing agreements between market platforms and research institutions may develop.

One practical consideration is how contract definitions themselves evolve and whether Kalshi maintains consistent definitions of economic indicators over time. For long-term research, consistency matters. If a contract on unemployment was based on the “civilian unemployment rate” in 2023 but on the “unemployment rate including discouraged workers” in 2024, time-series analysis becomes complicated. Researchers should document and account for such changes when building historical datasets.

Another consideration is regulatory risk. Kalshi operates under financial regulatory oversight ensuring market integrity and participant protection, but regulatory requirements for how economic prediction markets must be designed and administered are still evolving. Changes in regulation could affect contract availability, minimum trade sizes, or reporting requirements, altering the data available to researchers. Institutions relying on Kalshi data for ongoing analysis should monitor regulatory developments and maintain backup methodologies using traditional data sources.

The broader evolution likely involves greater institutional participation in prediction markets, more sophisticated contract designs capturing conditional probabilities and tail risks, and tighter integration between prediction market data and central bank forecasting models. Forward-looking central banks and academic economists who begin working with Kalshi data now will be positioned to contribute to this evolution and to influence how prediction markets are designed to serve research and policy needs. The collective expectations reflected in real-time pricing represent an underutilized asset for understanding monetary policy formation and expectations dynamics.