Abstract: This study explores the convergence of prediction markets and gambling, examining how platforms that allow trading on real-world events are reshaping both industries. It provides a broad overview of regulatory challenges, key players like Kalshi and Polymarket, and the growing debate over whether these markets are financial tools or forms of betting. The report also highlights emerging technologies such as blockchain and AI, and considers how prediction markets could influence finance, entertainment, and policymaking in the years ahead.

Length: ~ 26,961 words

Read Time: ~ 75 minutes

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Executive Summary

Prediction markets – exchanges where participants trade on the outcomes of future events – are emerging at the intersection of finance and gambling. This primer provides an in-depth exploration of how these markets have evolved from niche forecasting tools into potentially mainstream financial instruments, and the complex regulatory landscape they face. Key findings include:

• Rise of Regulated Platforms: Platforms like Kalshi, the first CFTC-regulated event-contract exchange (designated in 2020), have pioneered legal trading on events ranging from elections to sports. Kalshi’s case exemplifies the opportunities and legal battles in bringing prediction markets into the U.S. financial fold. The company fought a high-profile lawsuit against the Commodity Futures Trading Commission (CFTC) over political event contracts – a battle that it ultimately won in court, effectively securing the right to offer election markets. At the same time, Kalshi’s expansion into sports event markets in 2025 ignited conflict with state gambling regulators, who argue these trades are indistinguishable from sports bets.
• Blurring Line with Gambling: A central tension is whether prediction markets constitute regulated financial products or simply another form of gambling. Critics (including state gaming commissions and some CFTC officials) contend that betting on events like elections or games poses risks of manipulation, disinformation, and moral hazard. Proponents argue that, when properly regulated, prediction markets harness the “wisdom of crowds” for accurate forecasting and hedging, aligning more with financial derivatives than casino wagers.
• Regulatory Tug-of-War: The primer details a patchwork of U.S. regulations governing these markets. Federally, the CFTC oversees event contracts under the Commodity Exchange Act, imposing strict rules (e.g., prohibiting contracts on war or gaming outcomes) but grappling with how to treat political and sports events. At the state level, gambling regulators in jurisdictions like New Jersey, Nevada, Montana, and others have issued cease-and-desist orders against prediction platforms, asserting state gambling laws and consumer protections. A major theme is federal preemption vs. state sovereignty: Kalshi and its peers assert that a CFTC-regulated exchange can operate nationwide, while states (and Native American tribes) vehemently object to the sidestepping of local gambling controls.
• Market Landscape and Innovation: Beyond Kalshi, we examine platforms such as PredictIt, Polymarket, Manifold Markets, and Insight Prediction. These range from centralized, U.S.-based markets (often operating under academic or experimental allowances) to decentralized, blockchain-based markets operating globally. We compare centralized vs. decentralized models and the role of blockchain in enabling peer-to-peer “betting” without intermediaries. Notably, blockchain-based prediction markets (e.g. Polymarket, Augur) have demonstrated both the potential (billions in trading volume during the 2024 election cycle) and pitfalls (e.g. enforcement actions like Polymarket’s $1.4M CFTC fine in 2022) of a DeFi approach.
• Future Outlook: The primer discusses possible pathways to legalization and mainstream adoption of prediction markets. These include clearer regulatory frameworks (the CFTC in 2025 is actively soliciting input on sports event contracts), potential integration with traditional finance (e.g. retail brokerages offering event contracts, institutional use for hedging), and the influence of emerging technologies. The role of AI in forecasting and in monitoring prediction markets is considered, as are ethical safeguards to ensure these markets enhance rather than undermine decision-making and public trust.

Implications: For policymakers, the growth of prediction markets raises questions about how to balance innovation with protection of investors, consumers, and democratic institutions. For industry stakeholders – from startups and VCs to casinos and sports leagues – prediction markets represent both a disruptive threat and an opportunity to engage users in new ways. Finally, for society, these markets could become valuable predictive tools (improving information aggregation on everything from elections to epidemics) if their integrity is maintained. This primer concludes with recommendations and considerations for regulators, industry leaders, and researchers as they navigate the convergence of betting, trading, and information markets.

2. Introduction to Prediction Markets

Definition and Concept: Prediction markets (also known as information markets or event futures) are marketplaces where contracts tied to the outcome of specific events are traded. Each contract pays out based on whether a particular event occurs, essentially allowing participants to “bet” on outcomes. As Morgan Stanley’s analysts describe, “prediction markets are a type of marketplace where event contracts trade… trading an outcome at an event, such as the November election, economic indicators, or even corporate events”. Unlike traditional futures, these event contracts often have a binary payoff (e.g. $1 if event happens vs. $0 if not), giving them a defined risk/reward profile. The price of a contract fluctuates between 0 and 100 cents, effectively reflecting the market’s consensus probability of the event. For example, if a contract on “Candidate X wins the election” trades at $0.75, the market is estimating a 75% chance of that outcome. This mechanism leverages the “wisdom of crowds”: by putting real money at stake, traders are incentivized to incorporate all available information, yielding prices that often serve as predictive odds.

Historical Context: Prediction markets have a surprisingly long history, evolving from academic experiments into modern financial instruments. One of the earliest examples was the Iowa Electronic Markets (IEM), launched in 1988 at University of Iowa, which allowed small-stakes trading on political elections. The IEM demonstrated that market prices can outperform polls in forecasting election results, attracting economists’ interest in the 1990s. Another notable (though ill-fated) initiative was DARPA’s proposed Policy Analysis Market in the early 2000s, which aimed to use futures contracts to predict geopolitical events. Although that program was shut down amid controversy (due to ethical concerns over “betting on terrorism”), it helped popularize the idea of markets as predictive tools. In the mid-2000s, InTrade and Betfair (in the UK) gained traction by allowing real-money trading on elections, sports, and other events, though U.S. participants were eventually barred by regulations. Internally, companies like Hewlett-Packard and Google experimented with prediction markets to forecast project completion times and sales figures, further proving the concept’s value in aggregating dispersed information.

Evolution into Financial Tools: Initially seen as a curiosity or a form of gambling, prediction markets have increasingly been framed as financial instruments akin to derivatives. Contracts can be structured in various ways – winner-takes-all binaries, index contracts with scaled payouts, or spread contracts tied to an outcome threshold. These designs mirror financial products: for instance, a binary event contract is essentially an option that pays $1 if a certain condition is met. Over time, the use cases for prediction markets have broadened beyond politics into domains such as:

• Elections: The most well-known application – markets predicting election results or policy outcomes. Election markets have repeatedly demonstrated accuracy and quick information assimilation (e.g., reacting to debate performances or scandals in real time). During the 2024 U.S. presidential race, prediction markets provided a running forecast of the Trump vs. Harris contest, and even outpaced traditional polling in responsiveness.
• Sports: Traditionally the realm of sportsbooks, sports outcomes are now traded on prediction exchanges. This includes contracts on game winners, championship outcomes, or even player statistics. For example, one could buy a contract paying $1 if a certain team wins the NCAA tournament. Such contracts effectively turn sports betting into a commodities-style market where odds move with news like injuries or weather. (Sports prediction markets are discussed in depth in Section 5 and Section 7 due to their legal sensitivity).
• Finance and Economics: Prediction markets have converged with finance by offering contracts on economic indicators and business events. Kalshi and others have listed contracts on metrics like inflation rates, unemployment figures, Federal Reserve interest rate decisions, and even the debut dates of specific IPOs. These allow investors to hedge or speculate on economic outcomes in a simplified way. In fact, event contracts on weather and other commodities have existed on Chicago exchanges for decades, and modern platforms build on that legacy. The information value is also significant – a contract on, say, the Federal Funds rate target can serve as a live forecast of Fed policy changes.

Major Use Cases and Appeal: Why have prediction markets gained appeal? A key reason is their track record of accuracy in certain domains. Academic research has shown that in many cases, prediction markets outperform individual experts and polls. They incorporate diverse viewpoints and update continuously as traders react to new information (much like stock prices adjust to news). Additionally, these markets provide tangible financial hedges for real-world risk. For instance, farmers might hedge against bad weather by trading weather event futures, or media companies might hedge the risk of low viewership of an event by trading on outcomes related to ratings. Businesses and investors see potential in using event-driven contracts as an “insurance-like” tool. Finally, on the demand side, prediction markets tap into the same human interest that drives both investing and gambling: the desire to put money behind one’s predictions or beliefs about uncertain future events. This crossover appeal – engaging both those looking for profit opportunities and those seeking entertainment – underpins the surge in public interest in recent years.

In summary, prediction markets have evolved from obscure academic projects to platforms attracting millions in bets and trades. Their unique blend of speculation and information aggregation positions them at the frontier of finance. However, this evolution also raises questions about how society should classify and regulate these markets – a theme that resonates throughout this primer.

Figure: Screenshot of a Kalshi market on the 2024 U.S. election, illustrating a prediction market interface where users can buy “Yes/No” shares and see real-time odds. Kalshi’s regulated platform contrasts with decentralized crypto markets in usability and compliance.

Table: Comparison of notable prediction market platforms and their characteristics.