What Are Prediction Markets For?

Alex Johnson is the founder of Fintech Takes, a newsletter and podcast that brings banking, fintech, and public policy analysis to more than 30,000 industry professionals. Alex has more than 20 years of experience in financial services, including stops at FICO, Cornerstone Advisors, and Zoot Enterprises.

Set — the Egyptian god of storms and chaos — was depicted like other gods: a human body with an animal’s head. But unlike the others, we don’t know what animal Set was meant to be. His head doesn’t correspond to any known animal.

The ambiguity is fitting. Set represented disorder — his form resisted classification.

Prediction markets occupy a similar position today. 

Depending on who you ask, they are financial instruments for hedging risk, casinos with better incentives, or powerful tools for aggregating truth. Each description captures something real, and yet none fully defines the category. This ambiguity is not just conceptual. It is the central obstacle to regulating them coherently. 

Until we decide what prediction markets are for, we cannot decide how they should be governed.

What Are Prediction Markets?

Before we discuss what prediction markets are for, we should clarify what they are.

At their simplest, prediction markets are platforms for betting on real-world events. Participants buy and sell contracts tied to specific outcomes: an election result, a policy decision, or a sporting event. A typical contract pays out a fixed amount, often $1, if a specified outcome occurs, and $0 if it does not. The price of that contract can be read as the market’s implied probability of the event occurring.

Mechanically, they resemble financial markets: prices fluctuate as participants trade, incorporating new information into a continuously updated forecast.

Historically, prediction markets were understood in narrow terms; as tools for forecasting and, in some cases, hedging real-world risk. Early academic and policy interest focused on their ability to produce accurate forecasts on elections, economic indicators, and other measurable outcomes.

That description is no longer sufficient. Modern platforms list contracts on a wide range of events, many with little connection to real economic risk. In practice, they function less as tools for hedging and more as venues for open-ended speculation.

The terminology, however, has not kept pace. “Prediction markets” still evoke a narrow, academically grounded concept associated with disciplined forecasting and socially useful risk transfer. The reality is broader, more ambiguous, and harder to categorize.

This ambiguity has real regulatory consequences. We cannot apply a single framework to systems that are, in effect, trying to be different things simultaneously.

A market for hedging risk needs to be fair and confined to events that carry meaningful economic consequences. A gambling platform also needs to be fair, but the scope of contracts offered can be much broader. A truth-seeking information system should be broader still and should not concern itself with fairness at all. Only accuracy.

These use cases for prediction markets overlap, but they do not align perfectly. In order to regulate them coherently, we first need to decide what purpose we want them to serve. 

We have three choices.

Choice #1: Prediction Markets are For Hedging Significant Economic Consequences

Under this view, the purpose of prediction markets is to transfer risk. They allow individuals and institutions to hedge exposure to uncertain events with real economic consequences.

Under the Commodity Exchange Act, derivatives markets exist to enable participants to manage risk. Futures and options contracts are permitted because they support hedging and price discovery tied to real economic activity.

Prediction market operators often lean into this framing when it is advantageous. Kalshi, for example, has loudly partnered with an insurance provider that insures professional sports teams against contractual risks, like players hitting performance-based bonuses.

But this framing is applied selectively. The same platforms offer contracts on events with no clear connection to economic exposure, down to moment-level outcomes within sporting events, such as whether an announcer will say a particular phrase (“what a catch!”) during a game. These contracts cannot be reconciled with a hedging-based justification. They look less like risk management and more like speculation.

Applied rigorously, a risk hedging framework would limit contracts to events where participants have a legitimate economic stake in the outcomes. Interest rate decisions, inflation reports, and certain policy outcomes fit comfortably within this model. They affect asset prices and business operations.

However, many of the most popular contracts on these platforms would not fit. Markets on celebrity outcomes (“Kylie Jenner and Timothée Chalamet engaged in 2026?”) and religious beliefs (“Will Jesus Christ return before 2027?”) may be engaging, but they are difficult to justify as hedging instruments.¹

This distinction matters because the regulatory priorities under this model are demanding. If prediction markets are financial instruments, then market integrity is paramount. That requires robust anti-manipulation rules, restrictions on insider trading, surveillance for abusive practices, and limits on who can participate. It also implies discipline around what contracts are allowed in the first place. Not every uncertain event is appropriate for a regulated financial product.

Taken seriously, this framework would significantly narrow the scope of permissible markets. It would favor institutional use cases and restrict contracts to those with clear economic relevance. Many of the most popular markets today would fall outside its bounds.

Choice #2: Prediction Markets Are For Slightly Fairer Gambling

Under this view, the purpose of prediction markets is entertainment. They allow consumers to speculate on a wide range of different real-world outcomes in a fair, data-driven marketplace.

What distinguishes them from traditional casinos is their structure: they are peer-to-peer, often more transparent, and in some cases more efficient. Rather than betting against the house, participants trade against each other, and prices adjust continuously.

This framing — peer-to-peer gambling rather than betting against a house — is one prediction market operators frequently rely on. They point out, accurately, that sportsbooks set lines to drive activity rather than reflect true odds, and that they will identify and constrain the wagers of above-average bettors and aggressively encourage more wagers from below-average bettors.

However, this argument is less compelling than it sounds. Much of the liquidity in prediction markets comes from large, sophisticated institutional traders like Susquehanna and Jane Street, which are financially rewarded by the prediction markets for their participation, and that make their living by feasting on unsophisticated retail traders.

More importantly, traditional sportsbooks are regulated like casinos. They are legalized state-by-state and required to offer self-exclusion tools, risk disclosures, and to fund addiction treatment through taxation.

Prediction markets are not. They are, in effect, unregulated national casinos offering bets on an ever-expanding range of events.

However, if we were to apply the gambling framework to prediction markets in a rigorous way, the implications would be straightforward.

First, the range of permissible contracts could remain broad, subject primarily to social and ethical boundaries rather than economic relevance. It would be reasonable to allow markets on sports, entertainment, and many public events, while drawing clearer lines around areas widely considered harmful, such as assassinations.

Second, regulation would focus more on consumer protection: self-exclusion tools, limits on excessive participation, transparency around risks, and funding for addiction treatment.

Third, market integrity and clarity of resolution would be essential. Participants must trust that the game is fair, that outcomes are determined transparently, and that rules are applied consistently. Ambiguous or manipulable contracts would need to be eliminated, and insider trading would need to be strictly monitored.

Taken seriously, this framework would legitimize much of what prediction markets already are in practice (thus preserving their primary source of growth and revenue), while clarifying their obligations regarding consumer protection and market integrity.

Choice #3: Prediction Markets Are For Discovering Truth

Under this view, the purpose of prediction markets is information discovery. They exist to aggregate dispersed knowledge and produce the most accurate possible forecasts about uncertain future events.

This is the most ambitious framing and, in some respects, the most compelling. Research suggests markets can effectively incorporate diverse information into prices. Unlike polls or expert forecasts, prediction markets force participants to put money behind their views, rewarding those with better information.

Advocates (particularly VC investors) lean heavily on this argument, describing prediction markets as “civilization-scale truth-telling machines.” They point to their accuracy in forecasting elections and argue they could serve as early warning systems for political, economic, and geopolitical events.

Taken seriously, however, this framework carries difficult implications.

First, insider trading isn’t a bug in this model. It’s a feature. If the goal is to produce the most accurate possible forecast, then participants with privileged or non-public information improve the market. Restricting them would degrade the signal. The same logic applies to participants with operational or firsthand knowledge of an event. The more informed the trader, the more valuable their participation.

Second, the scope of permissible markets would expand dramatically. Events that are currently considered sensitive or off-limits — wars, political instability, even acts of violence — are often precisely the events where better information would be most valuable. Indeed, Polymarket CEO Shayne Coplan made this exact argument at a recent event, saying, “When I get hit up by people in the Middle East who are saying, ‘Hey, we’re looking at Polymarket to decide whether we sleep near the bomb shelter; we look at it every day’ and I’m like, ‘Oh, it’s really that popular over there?’ That’s very powerful. That’s an undeniable value proposition that did not exist before.”

Third, traditional notions of fairness and consumer protection become irrelevant. The goal isn’t fairness. It’s to ensure that prices reflect reality as accurately as possible. If more sophisticated or better-informed participants consistently profit at the expense of others, that is evidence that the system is working.

These implications are the logical consequence of defining prediction markets as tools for maximizing informational accuracy.

Taken seriously, this framework would loosen restrictions on participation and contract design, and shift regulation toward preserving accurate price formation rather than protecting participants.

That is a coherent vision. But it is one that policymakers and the public may find difficult to accept.

Distribution Should Follow Function

Clarifying what prediction markets are for can help us determine where they belong.

If they are tools for hedging risk, they belong in financial infrastructure: brokerage apps, wealth management platforms, and institutional trading systems.

If they are gambling, they should exist as standalone applications with clear boundaries and built-in protections.

If they are mechanisms for discovering truth, they should be integrated into information systems: news platforms, research tools, and real-world decision-making environments.

Today, prediction markets pursue all of these models simultaneously. They are embedded in brokerage apps like Robinhood, marketed as standalone products to gamblers, and embedded into media platforms. This further blurs an already unclear category.

We Need to Decide What Prediction Markets Are For

Each of these three frameworks is coherent, and each comes with trade-offs.

A hedging-based system is disciplined and economically grounded, but narrow. A gambling-oriented system is flexible and scalable, but requires robust consumer protections. A truth-seeking system is intellectually compelling, but forces uncomfortable compromises around fairness, ethics, and the kinds of events society is willing to monetize.

The current instability around prediction markets is not the result of any one of these models. It is the result of trying to be all of them at once.

Prediction market operators like to shift between these frameworks depending on the context. When raising capital or promoting their long-term vision, they describe themselves as civilization-scale truth-telling machines. When defending their expansion into sports and entertainment, they emphasize that they are simply a better form of gambling. When engaging with financial regulators, they frame themselves as commodity markets designed for hedging risk.

Each claim is defensible in isolation. Together, they are completely incoherent.

That incoherence is now colliding with the regulatory system. Recent legislative and regulatory efforts are, in effect, attempts to force a choice. The Prediction Markets Are Gambling Act proposes to restrict sports-related contracts and push prediction markets away from the gambling model. The BETS OFF Act proposes to ban contracts on war, government actions, and other events “ripe for manipulation,” pushing back directly against the idea that prediction markets should serve as open-ended truth-discovery mechanisms. And the CFTC’s recent rulemaking activity — particularly its focus on consumer protection and fairness — suggests a preference for treating them as a form of regulated gambling rather than as traditional financial markets.

This process will continue. The open question is whether it will be guided by a clear understanding of what prediction markets are for, or by ad hoc reactions to their most controversial uses.

We should make the choice explicitly.

Because the alternative is to let the chaos decide for us.

The opinions shared in this article are the author’s own and do not reflect the views of any organization they are affiliated with.

^[1]  Technically this is an anti-hedge, if anything, as it would be a bad bet to try to make yourself richer in the event that the man who said “it is easier for a camel to go through the eye of a needle than for someone who is rich to enter the kingdom of God” comes back to operationalize that rule.

Open Banker curates and shares policy perspectives in the evolving landscape of financial services for free.

If an idea matters, you’ll find it here. If you find an idea here, it matters. 

Interested in contributing to Open Banker? Send us an email at [email protected].

OPEN BANKER SALON - JUNE 5, 2026

We started Open Banker to raise the quality of financial policy debate. Now, we are bringing the newsletter to life with our first ever event: Open Banker Salon. This isn't a typical conference. No 40-minute talking-head panels. No bland keynotes. The Salon is designed for real intellectual engagement with in-depth debate-style discussions.

Location: The Aspen Institute, Washington, DC

Standard pricing ends on May 21. Pay just $795. Register here today!