Executive Summary 

• Prediction markets transform opinions into prices, creating continuously updated probability estimates for future events and, in many cases, outperforming polls, surveys and expert forecasts. 

• The sector has moved from academic experiments and niche political forecasting into a growing ecosystem that includes regulated exchanges, crypto-native platforms, institutional traders and media organizations. 

• While prediction markets can be powerful information-discovery tools, they remain vulnerable to regulatory uncertainty, manipulation, poor liquidity, and the tendency of observers to mistake probabilities for certainties. 

Introduction 

Prediction markets occupy an unusual position in modern finance and public discourse. They are simultaneously one of the simplest ideas in economics and one of the most contentious. To their advocates, they represent perhaps the closest thing we have to a real-time machine for aggregating dispersed information and converting it into useful forecasts. To their critics, they are little more than betting markets with an intellectual veneer. In all likelihood, the reality lies somewhere between the two. 

At their heart, prediction markets allow participants to buy and sell contracts linked to future events. The value of those contracts fluctuates according to the market’s collective judgement regarding the likelihood of a given outcome. A contract paying $1 if a particular candidate wins an election might trade at $0.65, implying that the market currently assigns a 65-percent probability to that result. Nothing particularly revolutionary is happening at the level of mechanics; what is interesting is the proposition that a market price, generated by hundreds or thousands of individuals risking their own capital, may tell us something useful about the future. 

Most people know something, but no one knows everything. Information is fragmented, scattered across institutions, industries, geographies, and personal networks. One participant may understand electoral demographics, another may follow monetary policy closely, while a third may possess specialized knowledge of energy markets or regional politics. Prediction markets attempt to gather these fragments together and express them through a single price. Rather than asking people what they think, they ask them what they are prepared to pay. 

How They Work 

Most prediction markets rely on relatively straightforward binary contracts. A participant purchases a futures contract linked to a specific outcome and receives a fixed payment if that outcome occurs. The closer the market moves toward believing the event will happen, the higher the contract price rises; the less likely it appears, the lower the price falls. 

The elegance of the model lies not in its complexity but in its incentives. Traditional forecasting exercises often reward confidence, visibility, or rhetorical skill, and can lead to herding behavior. Prediction markets reward accuracy. Participants are free to hold whatever views they wish, but if those views prove persistently incorrect, they eventually encounter a rather unforgiving corrective mechanism in the form of financial loss. Markets, unlike television panels, are under no obligation to preserve anyone’s reputation. 

This does not mean prediction markets are always right. They can become overconfident, irrational, or distorted, particularly when liquidity is limited. Yet they possess a quality that many alternative forecasting mechanisms lack: They force participants to reveal the intensity of their convictions rather than merely express them. There is a meaningful difference between believing in something and being willing to stake money on it. 

A Brief History 

The intellectual foundations of prediction markets are closely tied to broader debates about information and markets. Economists have long argued that prices contain information that no individual participant fully possesses. Financial markets themselves are often described as information-processing systems, continuously absorbing and reflecting new developments through changes in asset prices. 

Modern prediction markets emerged from this tradition. One of the earliest and most influential examples was the Iowa Electronic Markets, launched by researchers at the University of Iowa in 1988. The project allowed participants to trade contracts linked to election outcomes and, over time, developed a reputation for producing forecasts that frequently compared favorably with conventional polling. 

For many years, the field remained largely academic and somewhat obscure. That began to change as internet-based platforms reduced participation costs and broadened access. Political forecasting became the most visible application, but markets gradually expanded to cover economic indicators, sporting events, public health developments, corporate milestones, and geopolitical outcomes. 

Several political shocks further increased interest. Repeated forecasting failures by pollsters, commentators, and institutional experts created an audience more willing to consider alternative approaches. Prediction markets did not emerge from these episodes with a perfect record either, but enough individual incidences of success drew attention. 

The emergence of blockchain technology added a further dimension. Decentralized platforms offered the prospect of global participation and reduced reliance on traditional intermediaries, although they also introduced questions regarding governance, compliance, and market integrity that remain unresolved. 

The Key Players 

The contemporary prediction market landscape is no longer a niche academic curiosity. It consists of a mixture of regulated exchanges, decentralized platforms, professional traders, and increasingly sophisticated retail participants. 

Kalshi is the first federally regulated financial exchange offering these types of futures contracts. Operating within the United States regulatory framework, it has sought to establish prediction markets as a legitimate category of regulated event contracts rather than a peripheral form of wagering. The distinction may sound semantic, but it sits at the center of many regulatory debates surrounding the sector. 

Polymarket is a blockchain-based prediction market platform that allows users to trade contracts linked to a wide range of future events, including politics, economics, technology, and current affairs. Its prominence has increased alongside broader interest in prediction markets and the use of digital assets to facilitate trading and settlement. 

By comparison, unlike regulated U.S. exchanges such as Kalshi, Polymarket has historically operated through a different legal and regulatory framework, reflecting the broader distinction between decentralized crypto-native platforms and traditionally regulated financial exchanges. 

The Iowa Electronic Markets continue to occupy an important place from a research perspective, while a growing collection of analysts, hedge funds, academics, journalists, and private traders contribute liquidity and interpret market signals. Increasingly, prediction market prices have become part of the standard toolkit for anyone attempting to understand elections, policy developments, or macroeconomic expectations. 

The Regulatory Landscape 

To understand prediction markets in the United States, it helps to begin with a simple observation: The debate has never really been about forecasting. It has been about whether these contracts are financial products or a form of gambling. 

Oversight falls primarily to the Commodity Futures Trading Commission (CFTC), the agency responsible for regulating U.S. derivatives markets. Although prediction contracts may appear far removed from traditional futures on oil, wheat, or interest rates, the legal structure is broadly similar: Participants trade contracts whose value depends on a future outcome. 

For years, the CFTC approached prediction markets cautiously. Academic initiatives such as the Iowa Electronic Markets were largely tolerated as research projects, but commercial operators faced a more uncertain environment. The key battleground became political markets, with regulators questioning whether election contracts served a legitimate economic purpose or simply amounted to betting on politics. 

That debate came to a head with Kalshi, which argued that election contracts should be treated as regulated event contracts rather than gambling products. A series of legal and regulatory decisions has gradually expanded the scope for such markets, helping to establish prediction markets as a more credible part of the financial landscape. 

What Can and Can’t Be Traded on Kalshi? 

One of the more persistent misconceptions surrounding prediction markets is that they allow participants to wager on virtually anything. In reality, particularly in regulated markets such as Kalshi, the range of permissible contracts is considerably narrower than many assume. 

Under the oversight of the CFTC Kalshi structures its products as event contracts rather than traditional gambling wagers. In practical terms, this means contracts must be tied to clearly defined, objectively verifiable outcomes. The settlement mechanism must be transparent and based on publicly available data rather than subjective judgement. 

As a result, Kalshi offers contracts linked to economic indicators, interest-rate decisions, inflation releases, employment figures, weather events, commodity prices, company milestones, and a growing range of political outcomes. Participants can take positions on questions such as whether the Federal Reserve will cut rates by a specified date, whether monthly inflation will exceed a particular threshold, whether a hurricane will make landfall in a designated region, or whether a particular candidate will win an election. 

What Kalshi does not generally permit are contracts whose outcomes cannot be independently verified, or that raise significant concerns regarding public policy, criminal conduct, or market integrity. Markets based on individual acts of violence, criminal activity, terrorism, assassinations, or other harmful events are prohibited. Likewise, contracts that could create direct incentives for participants to influence the outcome themselves are unlikely to receive regulatory approval. 

The distinction reflects a broader principle underpinning regulated prediction markets. The objective is to create instruments that aggregate information about uncertain events, not markets that encourage harmful behavior or create opportunities for manipulation. Regulators have historically paid particular attention to whether a contract serves a legitimate economic or informational purpose and whether its existence could undermine the public interest. 

Political contracts have occupied an especially contentious position. For many years, election markets existed in a regulatory grey area, with critics arguing that they resembled gambling and supporters countering that they provided valuable forecasting information. More recently, legal and regulatory developments have expanded the scope for political event contracts, although debates regarding their appropriate role remain ongoing. 

The result is a category that sits somewhere between financial derivatives and traditional betting. Participants may be speculating on future outcomes, but the regulatory framework is designed to ensure that those outcomes are objectively measurable, resistant to manipulation, and connected to broader informational or economic value. Whether one views that distinction as meaningful or merely semantic depends largely on one’s perspective, but it remains central to understanding why some prediction markets are permitted while others are not. 

The Case For 

Supporters argue that prediction markets perform a valuable social function because they aggregate information more effectively than many competing mechanisms. The argument is not that markets are infallible, but that they provide a disciplined framework for converting dispersed knowledge into probabilistic forecasts. 

There is a substantial body of evidence suggesting that prediction markets frequently compare favorably with polls, expert panels and conventional forecasting exercises. This should perhaps not be surprising. Polls measure what respondents say they believe. Markets measure what participants are willing to risk on those beliefs. The latter often proves the more demanding test. 

There is also a transparency advantage. Market prices are public, continuously updated, and impossible to revise retrospectively. Forecasts remain visible for inspection and evaluation. Success and failure are recorded in real time rather than reconstructed after the fact. 

Perhaps most important, prediction markets create accountability. Participants who consistently identify valuable information gain influence through capital accumulation. Those who do not eventually find themselves subsidizing the more accurate forecasters. It is an imperfect meritocracy, but a meritocracy nonetheless. 

The Case Against 

The enthusiasm surrounding prediction markets occasionally exceeds their capabilities. 

A market price is not an objective fact about the future. It is merely the current consensus among those participating in that market. Under favorable conditions, that consensus may prove highly informative. Under unfavorable conditions, it may prove spectacularly wrong. 

Liquidity remains an important limitation. High-profile events attract attention and trading volume, but many contracts trade in relatively thin markets where a small number of participants can exert disproportionate influence on prices. The resulting forecasts may appear more precise than they actually are. 

Manipulation is another recurring concern because of this limited liquidity. In a deep and actively traded market, an individual participant must commit substantial capital to move prices meaningfully, and any distortion is likely to attract traders seeking to profit by pushing prices back towards their underlying value. By contrast, in thinly traded markets, relatively modest transactions can produce outsized price movements, making temporary distortions both easier to create and more difficult to correct. 

There is also the question of legitimacy. Some critics argue that markets linked to elections, conflicts, public health emergencies or other socially significant events risk creating perverse incentives or undermining public confidence. Whether these concerns are overstated remains open to debate, but they are unlikely to disappear. 

At the same time, it is difficult to ignore how closely prediction markets resemble conventional gambling once the institutional language is stripped away. Participants stake money on uncertain future outcomes, prices fluctuate in response to shifting sentiment and information, and profits are determined by correctly anticipating events that are inherently probabilistic. The fact that these contracts are framed as informational instruments rather than wagers does not change the underlying mechanics. For critics, this similarity is not incidental but foundational, and it is precisely why prediction markets continue to sit uneasily between financial regulation and gambling law. 

Finally, prediction markets can encourage a subtle misunderstanding among observers. Probabilities are often interpreted as predictions. A market assigning a 70-percent probability to an outcome is not saying that outcome will occur; it is saying that it should occur seven times out of 10. Human beings, unfortunately, have never been particularly comfortable with probabilistic thinking. 

Conclusion 

Prediction markets are best understood as neither gambling platforms nor oracles, but as information markets whose usefulness derives from their ability to force competing beliefs into direct confrontation. Their great strength is not that they eliminate uncertainty, but that they quantify it, providing a constantly evolving estimate of what informed participants collectively believe is most likely to happen. That estimate can be extraordinarily valuable, and occasionally remarkably accurate, but it remains an estimate, nonetheless. As the industry matures and regulatory frameworks develop, prediction markets are likely to become a more familiar part of the information landscape. The sensible observer will treat them as an important signal, sometimes the most important signal available, while resisting the temptation to mistake a market price for the future itself.