Introduction
Election forecasting is a vital tool in modern democracies, shaping public opinion and influencing how citizens and analysts understand political outcomes. Forecasting methods have evolved from traditional polls to advanced models on prediction markets, such as Kalshi (CFTC-regulated), Polymarket, Robinhood, and PredictIt, that react in real-time to new information. This article explains not just that prediction market election odds move quickly. But why, how, and what those movements signal about uncertainty and information flow.
While polls measure voter intent at a specific moment in time, prediction markets aggregate incentives, information, and uncertainty through political trading, often producing more adaptive signals as conditions change. Understanding these markets is essential for stakeholders in politics, finance, and academia because they provide a near 24/7 window into the evolving probabilities of political power, which in turn affects everyone.
U.S. election markets explained
U.S. prediction markets are innovative financial platforms where participants trade event contracts tied to the outcome of future events. Most notably, they allow users to trade on the outcome of presidential elections, but they also offer contracts on many topics besides politics, including weather, economics, crypto, and sports.
While people often refer to this sort of trading as "political betting" or "gambling," this is a misnomer. Prediction markets have much more in common with futures trading or trading in the stock market than they do with traditional sportsbooks. This is why platforms like Kalshi are regulated by the Commodity Futures Trading Commission (CFTC).
Unlike sports betting platforms like FanDuel and DraftKings, where users are gambling against the house, prediction markets have no vested interest in the outcome. They simply act as a middleman to facilitate trades, and make their money from transaction fees. Also, prediction markets do not set betting odds on the outcomes of current events. On platforms like Kalshi, odds are based on market prices set by the traders themselves.
Prediction markets operate on the "wisdom of crowds" principle, suggesting that a large group of people, each with unique pieces of information and a financial incentive to be right, can produce a more accurate forecast than any single expert. Platforms such as Kalshi, Polymarket, Robinhood, and PredictIt are the four most popular prediction markets, and Kalshi is the largest one that is regulated by the Commodity Futures Trading Commission (CFTC).
How markets function
In these prediction markets, an outcome like "Candidate A wins the presidency" is represented by a contract. If the event occurs, the contract pays out $1. If it does not, the contract expires worthless ($0.00).
The "Stock Market" for Politics: Just as investors buy shares of a company they expect to succeed, traders buy "shares" of a candidate they expect to win.
Interpreting Results: The trading price of the contract serves as a direct proxy for probability. A contract trading at $0.65 implies a 65% market-implied probability of that candidate winning.
On CFTC-regulated exchanges like Kalshi, these markets provide a transparent venue where probabilities are set directly by market participants from political trading rather than biased editorial judgment, or survey snapshots. Not only did Kalshi’s 2024 Presidential Election market drive headlines, but it also provided traders and non-traders alike with beneficial information on the state of the race by giving quantifiable probabilities on the percent chance of each candidate to win.
Polls vs. markets: Why signals diverge
The most frequent source of confusion in election season is why a poll might show a "dead heat" while the political market odds on a prediction market like Kalshi or Polymarket show a clear favorite. These discrepancies are not necessarily errors; rather, they reflect the different nature of the data being collected. Let me explain:
Contrasting the signals
Feature | Polls | Prediction Markets (e.g. Kalshi, Polymarket, Robinhood, PredictIt) |
Primary input | Self-reported voter intent | Capital-at-risk trades |
What it measures | Voter preference today | Expected outcome on Election Day |
Update frequency | Weekly or monthly (high lag) | Real-time (sub-second) |
Bias risk | Social desirability, sampling, and non-response bias (medium-high bias risk) | Favorite-longshot and trader-specific bias (low bias risk) |
Treatment of uncertainty | Margin of error (statistical) | Price volatility (market-driven) |
Incentives | None for the respondent | Profit / loss for the trader |
Sensitivity to news | Low (takes days to field) | Instantaneous |
Factors that drive discrepancies
Several factors contribute to the gap between polling signals and market predictions:
Behavioral factors: Polls are susceptible to "social desirability bias," where respondents may be hesitant to admit support for a controversial presidential candidate. Traders, conversely, are financially penalized for lying to themselves; they bet on who they think will win, not who they want to win. They are not susceptible to any social desirability bias when they are anonymously engaging in political trading behind a computer or phone screen.
Structural Factors: Polls often struggle with "low-propensity" voters, people who don't answer the phone but do show up to vote. Markets often price in a "premium" or a "discount" based on historical polling misses and polling quality.
Timing and Information: Markets incorporate "non-polling" data, such as campaign fundraising, candidate health, economic shifts, and presidential debate performances, long before these factors manifest in voter preference surveys.
Reliability and Credibility in Forecasting
Neither method is infallible. Polling data is the only way to measure the raw sentiment of the electorate, but its credibility has been strained by "undercounts" for Trump in 2016, 2020, and 2024. Prediction markets are credited with extreme speed, calling the presidential winners hours before networks, but can occasionally fall into "echo chambers" or trading narratives or be influenced by "whales" (large traders) if the market has low volume.
Historical case studies in U.S. Presidential Elections
To understand the value of market signals, we must look at how they performed when the stakes were highest.
Case Study 1: 2000 Presidential Election (Bush vs. Gore)
The 2000 election was a statistical tie in the polls for weeks. Prediction markets, however, showed significant volatility as the Florida recount loomed.
Polls vs. Markets: Polls essentially stopped being useful once the voting ended. Prediction markets, however, continued to trade, providing a "real-time" assessment of legal ruling outcomes.
Key Insight: In the early morning after Election Day, the political trading odds for Gore dropped to near zero before rebounding as the Florida dispute intensified. The market served as a barometer for legal and procedural risk that polls simply could not capture.
Case Study 2: 2008 Presidential Election (Obama vs. McCain)
Leading up to 2008, the prediction markets and polls were largely in sync, both favoring Obama.
Polls vs. Markets: While poll aggregators like RealClearPolitics showed an Obama lead of about 7.6%, political market odds were even more aggressive in their certainty of an Obama victory.
Key Insight: Prediction markets were much faster to price in the impact of the 2008 Financial Crisis into their political market odds. As Lehman Brothers collapsed in September, McCain's odds plummeted on markets like Intrade instantly (this was before Kalshi, Polymarket, Robinhood, and PredictIt existed), whereas polls took nearly a week to reflect the shift in public confidence.
Case Study 3: 2016 Presidential Election (Trump vs. Clinton)
The 2016 election is the most cited example of polling failure. Most polls and forecasts based on the polls gave Clinton a 70–99% chance of winning. Prediction markets like PredictIt favored Clinton but gave her lower odds
Polls vs. Markets: On election night, as the first results from Florida and the "Blue Wall" states (PA, MI, WI) trickled in, prediction markets flipped from Clinton to Trump within an hour.
Key Insight: Markets processed the rural turnout surge in real-time. Traders who saw the "over-performance" in small precincts moved the odds toward Trump hours before news anchors felt comfortable calling the race.
Case Study 4: 2020 Presidential Election (Biden vs. Trump)
In 2020, polls showed Biden with a substantial and stable lead, but markets were more cautious. Forecasts based purely on the polls had Biden’s chances at victory anywhere between 80-99%, even more confident than they were of a Clinton victory in 2016.
Polls vs. Markets: Traders priced in a "Trump Premium" into their odds, remembering the 2016 miss. Even as polls showed Biden up 8–10 points, the markets often sat closer to a 65 / 35 split.
Key Insight: Markets correctly signaled high uncertainty. While Biden ultimately won, the race was much closer in the Electoral College than national polls suggested (and much closer than Trump won by in 2016 and 2024 if you look at the number of votes in the tipping point states), vindicating the market’s more conservative "odds."
Key Lessons for Interpreting Election Signals
For those looking to use market data effectively, it is best to view prediction markets as a complement to polling, not a replacement. Also, it’s important to remember that polling is a key input into election markets.
Probabilities over predictions
Never look at a market price of $0.55 as a "prediction" that Candidate A will win. Look at it as a 55% chance. This means that out of 100 elections held in these exact conditions, the candidate would lose 45 of them. Prediction markets quantify the range of possibilities, not a singular truth.
Disagreement Signals Uncertainty
When polls show a 5-point lead for one candidate but the prediction markets are at 50 / 50, it is a signal of high informational complexity. This usually means the market expects a "polling miss," a "turnout surprise," or an "October Surprise" that the surveys haven't caught yet. With polling increasingly coming under scrutiny for big polling misses, it’s entirely possible that traders are underweighting them relative to other factors like historical results and qualitative factors.
Combine Polls and Markets
The most robust models today, such as those used in high-frequency finance, combine polling averages with market prices. Use polls to understand the "base case" of voter sentiment and use markets to see how that sentiment is reacting to breaking news.
Volatility Reflects Confidence
Sharp price swings on platforms like Kalshi, Polymarket, Robinhood, and PredictIt are not necessarily "instability." Rather, they reflect the speed of information absorption. A sudden drop in a candidate's price during a debate is the market successfully doing its job: re-calculating the odds based on new, significant evidence.
Conclusion
Modern election forecasting is an arms race between different types of data. Traditional polls provide the "what", the raw preference of the people, while CFTC-regulated election markets provide the "when" and the "how likely." Polls are not trying to do what prediction markets do and vice versa, but it is undeniable that prediction markets incorporate more pieces of data than traditional polling does, and this is by design.
The importance of correctly interpreting these signals cannot be overstated. By understanding that market prices are a real-time aggregation of incentives and “secret information”, analysts can better navigate the noise of a campaign cycle. Whether you are an investor hedging against policy shifts or a citizen trying to cut through the punditry, integrating the signals from both polls and markets is the only way to gain a full picture of the political horizon.
Important Disclaimer: Prediction markets are tools for interpreting signals and collective probabilities, not endorsements or guaranteed forecasts of future outcomes. Market prices reflect the aggregate beliefs of participants and should be considered as one input among many—such as polling data and economic fundamentals—when analyzing political events.