Written by: Aryan Desarapu
Prediction markets have taken off as tools to estimate the probability of a variety of uncertain events, from elections to Super Bowl outcomes to the peak temperature in New York today. Rather than relying on mathematical models to come to these conclusions, prediction markets allow individuals with diverse, fragmented information and beliefs to anonymously express their beliefs with their money. Specifically, users can buy a “Yes” or “No” contract to answer a certain question, like “Will Democrats win the 2028 U.S. presidential election?” Each contract is offered for some fraction of a dollar, meaning winners receive a full dollar and losers earn nothing. Thus, the price of the “Yes” contract on a given event can be interpreted as the aggregated expectation of that event occurring.
Today, the two biggest prediction markets in the U.S. are Kalshi and Polymarket (Osipovich & Ostroff, 2026). Trading volume on world events has already grown enormously since prediction markets have entered the public eye; for example, Polymarket markets on the 2024 presidential election peaked at a volume of over 3 billion dollars(Liu & Bierwirth, 2025). A forecast from the firm Eilers & Krejcik claims that annual trading volume in prediction markets could hit a staggering 1 trillion dollars by the end of the decade (Brewer, 2025). As of February 2026, however, about 50% of the trading volume on Polymarket and 90% of volume on Kalshi went to wagers on sports events, despite lawsuits from multiple states alleging that such bets violate state gambling laws (Sweet, 2026). Although sports markets will likely continue to drive revenue growth for Kalshi and Polymarket, their role as information-gathering marketplaces arguably lies elsewhere, like in markets where different individuals with distinct sets of information and beliefs can collectively produce worthwhile signals about financial, political, and social outcomes. As prediction markets keep growing, investors and regulators alike will have to contend with the unique role of prediction markets in the wider financial system and media economy.
Crucially, trades on prediction markets like Polymarket and Kalshi allow anonymous traders to use possibly confidential information to inform their bets (Park, 2026). These markets have therefore come under fire for allegedly failing to prevent insider trading; for example, prior to the January 2026 capture of Nicolas Maduro, an anonymous trader won over 400,000 dollars on Maduro’s capture from trades placed just hours before the raid (Rogelberg, 2026). Critics of prediction markets pointed to the transaction as evidence that the anonymity shields insiders from facing consequences for insider trading (Park, 2026). On the other hand, this incident can also be seen as evidence of the epistemic value of prediction markets. Those who saw the price of the “Yes” contract increase after the 400,000 dollar bet essentially received an early, albeit weak, signal that Maduro’s capture was imminent. Thus, even if they fail as traditional financial markets to prevent insider trading, prediction markets can still serve as powerful aggregators of information that may not be entirely publicly available. This carries an important qualification—the participation of insiders who can manipulate the outcome, rather than simply having some private information, blurs the line between predicting an event and manufacturing it.
The anonymity and incentive structure provided by prediction markets can create moral hazard when participants exercise some control over the outcome of a market. In economics, the concept of moral hazard refers to a situation where an individual or entity has an incentive to behave more riskily than they otherwise would because they are at least partially protected from the consequences of their actions (Ahrens, 2008). For example, a member of the Federal Open Market Committee (FOMC), which sets the target range for the federal funds rate, could anonymously bet on how much the Fed will cut interest rates and therefore sway policy decisions for their own gain. Here, the incentives posed by the prediction market do more than create an early signal of future interest rates while helping the insider profit from their knowledge; rather, they can distort interest rates decisions themselves, thanks to the participation of the policymaker. Even if such cases are rare in practice, the fact that they could occur poses challenges for prediction markets like Kalshi and Polymarket that position themselves to the wider public as credible, trustworthy tools to consolidate information.
Beyond fears of insider trading and moral hazards, the gravity of political events whose likelihoods are bought and sold could provoke “moral discomfort” among those outside the market, posing yet another obstacle to widespread trust of information gathered through prediction markets. For example, consider the Policy Analysis Market (PAM), a prediction market proposed in 2001 by the Defense Advanced Research Projects Agency (DARPA), part of the then-Department of Defense (Hanson, 2007). Traders would be able to buy and sell futures contracts on possible geopolitical events in Middle Eastern countries; the prices of these contracts, reflecting collective belief in these outcomes, would therefore help the government accurately project and assess these risks over time (Wolfers & Zitzewitz, 2003). However, in July 2003, the initiative faced sharp congressional criticism, with lawmakers condemning PAM as a “terrorism futures market” (Wolfers & Zitzewitz, 2003). The project was swiftly cancelled after members of the media argued that, among other drawbacks, the market could incentivize terrorists to profit by betting on political violence before carrying it out (Hanson, 2007). In response, some of PAM’s supporters contended that similar financial incentives already existed, since the prices of stocks and commodities like oil are known to respond strongly to such attacks (Wolfers & Zitzewitz, 2003).
Although there is a distinction to be drawn between markets organized by the federal government itself as opposed to private entities like Kalshi and Polymarket, the same underlying anxiety that led to PAM’s cancellation will likely cause hesitancy and pushback as prediction markets aim to grow their role in the media ecosystem, which often deals primarily with the kinds of serious geopolitical events that PAM attempted to assess. Both companies have already begun expanding into the mainstream media space; either via Kalshi or Polymarket, companies like CNBC, CNN, and Dow Jones have agreed to incorporate prediction market data into their live or online media offerings (Park, 2026). Ultimately, in a time of AI-driven misinformation, fragmentation of the traditional media landscape, and growing distrust in mass media establishments, the ability of prediction markets to inform the public hinges on if their ostensibly dual nature as both credible information institutions and venues for speculative investment can be reconciled in the collective consciousness.
References
Ahrens, F. (2008, March 19). “Moral hazard”: Why risk is good. The Washington Post. https://www.washingtonpost.com/wp-dyn/content/article/2008/03/18/AR2008031802873.html
Brewer, C. (2025, December 17). Prediction markets could hit a trillion dollars in trading volume: E&K report. CNBC. https://www.cnbc.com/2025/12/17/prediction-markets-trillion-dollar-trading-volume-ek-report.html
Hanson, R. (2007). Shall we vote on values, but bet on beliefs? Innovations: Technology, Governance, Globalization, 2(3), 73–77. https://doi.org/10.1162/itgg.2007.2.3.73
Liu, V., & Bierwirth, D. (2025, January 23). A primer on prediction markets. Wharton Initiative on Financial Policy and Regulation. https://wifpr.wharton.upenn.edu/blog/a-primer-on-prediction-markets/
Osipovich, A., & Ostroff, C. (2026, February 2). The wild markets behind Polymarket’s ‘truth machine’. The Wall Street Journal. https://www.wsj.com/finance/regulation/polymarket-prediction-markets-kalshi-dd4702d6
Park, A. (2026, January 9). Why prediction markets need insider trading, according to their godfather. Forbes.https://www.forbes.com/sites/aliciapark/2026/01/09/why-prediction-markets-need-insider-trading-according-to-their-godfather/
Rogelberg, S. (2026, January 5). Polymarket user wins $400K betting on Maduro capture, raising insider-trading concerns. Fortune. https://fortune.com/2026/01/05/polymarket-user-400k-maduro-capture-bets-insider-trading-suspicion/
Wolfers, J., & Zitzewitz, E. (2003, July 31). The furor over “terrorism futures”. The Washington Post, A19. https://users.nber.org/~jwolfers/Press/TerrorismFutures.pdf