Use with Caution: The Hidden Risks of Prediction Markets
Humankind has long been interested in predicting the future, from early celestial observations to the study of mathematics and sciences used in advanced meteorology today. But never has the ability to predict the future been commodified as expressly as it is today.
With the rise in popularity of sports betting over the last decade and increased retail investor activity, the emergence of a new kind of market, already processing billions of dollars in transaction volume, is hardly unpredictable. But the idea of what a prediction market really is can be elusive. What does it mean to “trade” on predictions? And what are the consumer implications of this new system of exchange?
In this article, I introduce the prediction market mechanism, discuss Hayek’s theory of price as an information aggregator, examine the risks and implications of this new market form, and suggest that these platforms be approached with caution for multiple reasons: the nuanced nature of trading future events, the difficulty of regulating insider trading across jurisdictions, and the risks of conflating prediction markets with cable media sources that people turn to in search of reliable information about the world. To help combat these issues, regulators can implement more comprehensive measures, such as disclaimers, to provide more information about how prediction markets work and what the probabilities actually represent.
What is a Prediction Market?
While often compared to traditional financial markets and sports betting, prediction markets escape easy categorization. To make sense of where a prediction market stands among other prediction-based for-profit ventures, it may be helpful to place it in the context of two prominent ways that the average person, also called a retail investor, may also hope to make a profit: the stock market and sports betting platforms like FanDuel.
In a traditional stock market, you can purchase shares in a real company’s stock. This makes you the material owner of a small piece of said company. You can also place an options contract on stocks and other assets that pay out when the asset price moves in the direction you predict.
Sports betting platforms like FanDuel appear conceptually close to the idea of a “prediction market,” since both deal with “betting” on the outcomes of future events. However, the platform mechanisms diverge quite significantly. Sports betting platforms set the odds for a given outcome before a game and adjust them throughout the game to mitigate losses. However, unlike sports betting, Kalshi uses a central limit order book, a system that matches the lowest available selling price with the market’s highest asking price, to facilitate peer-to-peer trading, much like traditional markets do. Prediction market critics will likely call trading on these platforms a form of gambling, given the apparent difficulty of predicting the future in general.
Thus, it becomes clear that Kalshi operates more like a typical market than a gambling site, with the primary differentiators being the nature and pricing of the underlying asset. On prediction market platforms, the price of any given event contract is set by incoming and outgoing put or call orders, ranging from $0 to $1 to represent the “implied probability” of the event occurring. For example, if the contract is priced at $0.33, the platforms will show that the event has a 33% “chance” of occurring based on all the competing orders it has priced in.
Relevant Economic Theory
Looking toward economic theory can help us better understand what prices actually represent and how they can be meaningful. It is well established that price reflects more than just the nominal value of a good in any given currency: the late Austrian economist Friedrich August von Hayek (1899-1992) published his thoughts on the role of price as an information aggregator in Volume XXXV No. 4 of the American Economic Review. In his 1945 article, “The Use of Knowledge in Society,” which aims to understand what makes a market system efficient, he discusses a “very important but unorganized” kind of knowledge: the knowledge of “the particular circumstances of time and place.” He argues that “prices can act to coordinate the separate actions of different people.” To illustrate this concept, he uses commodity prices as an example in which the demand for tin elsewhere in the world becomes “priced in” by those who have access to the knowledge that it has recently become more valuable. In other words, people you have never met, in contributing capital into the price of a commodity or asset, indirectly communicate to you something meaningful about that asset.
In this way, price becomes much more than just an abstract concept of “value.” Rather, it represents the consolidation of a vast network of unique and disconnected information into a single, unified value. Furthermore, Hayek argues that while price cannot possibly communicate the totality of the “particular circumstances of time and place” for such a large number of market participants, it acts as a proxy for this information through its direction/movement, conveys the most essential information about the item, and thereby influences agents to make certain decisions.
The introduction of prediction markets adds some nuance to the modern interpretation of Hayek’s argument. The most striking difference between Hayek’s idea of price and the price of an event contract you can purchase on Kalshi is that the price is systemically limited and represents an implied probability rather than its material present value. In other words, the price does not convey the value of or any material gain associated with the event transpiring, but rather, consumer sentiment about whether it will transpire at all. In this way, prediction markets still aggregate information about these events in line with Hayek’s theory; it’s just a different kind of information.
Dangers and Concerns of Prediction Markets
Since markets serve to aggregate information into a single price, whoever has the most accurate and up-to-date information often stands to win over less-informed market participants, sometimes legally, sometimes not. Thus, insider trading is still a significant concern in this new market form. As of 2026, both Kalshi and Polymarket are regulated by the Commodity Futures Trading Commission (CFTC), which enforces a ban on insider trading in commodity and futures markets per the U.S. Securities and Exchange Commission’s (SEC) guidelines. For Polymarket, however, a similar platform with a primarily overseas customer base, the question becomes how effectively the CFTC or SEC could enforce their policies abroad.
For example, foreign government officials with access to sensitive U.S. military intelligence can use this non-public information to buy Polymarket’s contracts and see their gains skyrocket at the expense of retail investors. Is the U.S. government going to risk an international incident with a powerful foreign entity over something as trivial as a seemingly isolated case of insider trading? Probably not. In this way, a small number of domestic or foreign elites can profit immensely from the platform that many might dismiss as a new mode of gambling by the uninformed masses. Though not everyone is uninformed, it seems: the New York Times recently observed that, on Polymarket, 0.04% of addresses made roughly 70% of the profits. Furthermore, Kalshi has admitted to two documented cases of insider trading and published its response to the situation in a press release. These examples call into question whether prediction markets and their regulators are doing enough to curb insider trading or whether it is even possible to do so.
Another concern is the marketing and news media partnerships of prediction market platforms. Consumers may easily mistake the probabilities calculated using these peer-to-peer trading platforms as reputable. Recently, CNN partnered with Kalshi to provide the implied probabilities of contracts users are trading alongside its news coverage of related events. This creates a dangerous and irresponsible precedent of mixing the goal of factual, objective journalism coverage with a faux “probability” derived from a prediction market’s aggregate of what is ultimately subjective consumer sentiment.
Conclusion
While prediction markets like Kalshi and Polymarket are an innovative approach to event-based trading, consumers should approach these platforms with caution. They present the totality of future events as a new asset class and create new peer-to-peer trading opportunities for investors interested in placing their bets on future outcomes. However, the distinction must be made clearer between the likelihood of the event occurring in the real world and the platform probabilities themselves: the probabilities displayed on platforms like Kalshi and Polymarket are not necessarily rooted in fact, can be manipulated by malicious actors and insider activity, and still hold notable risk as investment instruments marketed with trendy headlines. In order to prevent these platforms from exerting unfounded influence on our perception of current events, regulators should require a disclaimer explaining how the probabilities are calculated and that they do not necessarily have any factual basis. Overall, it seems that prediction markets are here to stay; regulatory bodies like the SEC and CFTC should attempt to regulate them more comprehensively to make the platform mechanisms and their consequences clearer to the public.