From Academic Curiosity to $44 Billion in Volume
In 2025, participants moved $44 billion through prediction markets. Three years earlier, that figure would have seemed like a typo. These platforms went from an interesting research experiment — something economists pointed to when making arguments about crowd wisdom — to a genuine force shaping how financial professionals think about uncertainty, probability, and price discovery.
Understanding prediction markets has become increasingly relevant — not necessarily because everyone needs to use them, but because they’re becoming an input that affects how equities, currency pairs, and rate futures behave. They’re a signal source that a growing number of institutional participants monitor.
The Mechanics: Simpler Than You Think
The underlying structure is elegant. Participants buy contracts on whether a specific event will occur. A “Yes” contract on a Federal Reserve rate cut priced at $0.65 means the market collectively believes there’s a 65% probability of that cut happening. If the cut occurs, the contract pays $1. If it doesn’t, it expires at zero.
The logic parallels traditional market pricing: the gap between the market’s implied probability and the actual probability of an event represents the potential for mispricing. Whether that gap is exploitable — and by whom — is a more complicated question.
The major platforms have each carved out different approaches to this same basic structure.
Polymarket runs on the Polygon blockchain using USDC stablecoins. It’s decentralised, charges no trading fees, and generated $21.5 billion in volume in 2025. After years of CFTC scrutiny — including a $1.4 million fine in 2022 — enforcement actions were dropped in 2025, opening the door for its return to US users.
Kalshi is the regulated anchor of the space, having received CFTC approval as a Designated Contract Market back in 2020 — the first prediction platform to achieve that status. It brought in $17.1 billion in volume in 2025, partnered with Susquehanna International Group for market-making, and now has integrations with Robinhood and Webull. Kalshi has expanded well beyond politics, covering Fed decisions, economic data releases, and sports outcomes.
PredictIt had a rougher path — the CFTC rescinded its operating letter in 2022, triggering years of litigation. It finally settled in mid-2025 and received full regulatory approval, relaunching without the participant caps that had previously limited its scale.
These platforms no longer just cover election outcomes. They cover GDP prints, CPI releases, central bank decisions, sports results, and geopolitical events. The diversification of available markets is directly responsible for that volume surge.
Why Prediction Markets Are Becoming Part of the Information Landscape
Here’s the part that matters even for people who have never opened a Kalshi account.
Prediction markets are now functioning as a real-time probability feed that flows back into traditional markets. When a Fed decision contract starts pricing in a 70% chance of a rate cut, equity market participants notice. Treasury futures positions shift. Capital moves before the official data lands. The prediction market isn’t causing these moves exactly — it’s aggregating information faster and making implied probabilities visible in a way that’s accessible, in real time, for anyone watching.
The Federal Reserve’s own research has concluded that prediction markets can be as accurate as — and in some cases more accurate than — traditional economic surveys for macroeconomic forecasting. That’s a noteworthy finding from one of the world’s most influential financial institutions.
Corporate executives are taking note too. There are documented cases of C-suite teams monitoring prediction markets to gauge how participants are pricing specific company events — upcoming earnings, regulatory decisions, product launches. The information flows in multiple directions.
More significant for the long run: asset managers are now filing for ETFs with direct exposure to prediction market contracts. When institutional money enters at scale, the feedback loop between these platforms and traditional markets will tighten considerably.
The Accuracy Question: What the Research Says
One of the most examined aspects of prediction markets is whether they actually produce accurate forecasts. The evidence is mixed, which is itself an important finding.
Where they’ve performed well: The Federal Reserve study found prediction markets compared favourably to surveys of professional economists for macroeconomic forecasting. For events where a large number of informed participants are actively trading — such as US Federal Reserve decisions — the prices have tended to converge toward outcomes that align with the eventual result more often than not.
Where they’ve failed: Brexit and the 2016 US election are the most cited examples. In both cases, prediction markets assigned high probabilities to the outcome that didn’t happen, sometimes right up until results came in. These weren’t close-call mispricings — they were confident consensus views that turned out to be wrong.
The academic interpretation of these failures matters. Prediction markets don’t aggregate “truth” — they aggregate the views of participants who have chosen to put money on the line. If that participant pool carries systematic biases — geographic, ideological, demographic — the prices will reflect those biases. A 73% probability contract doesn’t mean the event will happen. It means the current pool of participants, carrying all their collective biases and information gaps, thinks there’s a 73% chance.
This distinction between consensus and accuracy is fundamental to understanding what prediction market prices actually represent. They’re a data point, not an oracle.
The Risks That Aren’t Immediately Obvious
Prediction markets look clean. Simple binary outcomes, transparent pricing, clear settlement rules. But there are hazards that aren’t apparent from the surface.
Liquidity can be brutal. In thinly traded markets, a few large orders can blow out the bid-ask spread and completely misprice a contract. Positions that appear correctly valued on fundamentals may have no exit at anything resembling fair value. Liquidity is not evenly distributed — some events attract deep participation, many don’t.
Manipulation is a real and documented problem. The specific mechanics look like this: coordinated participants inflate the prices of unlikely outcomes, wash trading through multiple accounts creates the appearance of volume, and layered orders manufacture artificial sentiment. Traditional markets have manipulation too, but prediction markets — with thinner liquidity and historically lighter oversight — are more vulnerable to it.
Insider information sits in a regulatory grey zone. If someone has advance knowledge of a Federal Reserve decision and trades a Fed prediction market on that information, existing insider trading laws may not cover it. The Public Integrity in Financial Prediction Markets Act of 2026 is attempting to close this gap specifically for government officials, but enforcement mechanisms are still being built.
Regulatory uncertainty hasn’t fully resolved. Despite Kalshi’s landmark legal win in 2024 — which established that political event contracts aren’t gambling under federal law — states are still fighting. Multiple attorneys general and gaming commissions have sent cease-and-desist letters, arguing these platforms operate as unlicensed sportsbooks. The federal framework is clearer, but the state-level fight will run through 2026 and beyond.
The gambling-investing line is genuinely ambiguous. This isn’t just philosophical hand-wringing. Age verification on these platforms is typically 18, rather than the 21 required for licensed sportsbooks. Responsible gaming requirements are fewer. The platform design is built for engagement in ways that can reinforce impulsive decision-making. Whether prediction markets are a financial tool, entertainment, or gambling is a live regulatory and ethical question.
The Gambling Debate: Finance or Entertainment?
The tension between prediction markets and gambling goes deeper than regulatory classification. It touches on fundamental questions about what these platforms actually are and who they serve.
Proponents argue prediction markets serve a genuine economic function: they produce actionable probability signals for events that affect businesses, governments, and financial markets. A company trying to plan around the outcome of a regulatory decision has a legitimate interest in the probability that decision will go one way or the other. The information function, they argue, justifies treating these markets as financial infrastructure rather than gambling.
Critics counter that the vast majority of prediction market volume comes from participants who aren’t hedging business risk — they’re speculating on elections, sports, and other events for entertainment or profit. The platforms’ user interfaces, marketing, and engagement mechanics look more like sportsbooks than Bloomberg terminals. And the real-world consequences of normalising betting on political outcomes raise concerns that go beyond financial regulation.
The honest assessment is that both sides have a point. Prediction markets can serve an informational function. They also function as a gambling venue for most of their participants. How regulators ultimately resolve this tension will shape the future of the industry.
The Regulatory Landscape in 2026
The regulatory picture for prediction markets is fragmented and evolving rapidly.
Federal level: Kalshi’s 2024 victory against the CFTC established that political event contracts are legal under federal commodity law, not gambling. This was a landmark ruling. The CFTC has since moved toward creating a more comprehensive framework for event contracts, though the details are still being developed.
State level: Multiple states continue to challenge prediction market operations. Attorneys general in several states have argued these platforms need state gaming licences. Gaming commissions have sent cease-and-desist orders. The legal battles will play out over years.
International: The regulatory picture outside the US varies enormously. Some jurisdictions treat prediction markets as financial instruments, others as gambling, and many haven’t classified them at all. Blockchain-based platforms like Polymarket add another layer of complexity, since they operate across jurisdictions without a clear physical presence in any one of them.
The insider trading gap: One of the most significant unresolved issues is whether trading on non-public information in prediction markets constitutes insider trading. Traditional securities law doesn’t clearly cover it. The Public Integrity in Financial Prediction Markets Act of 2026 addresses government officials specifically, but the broader question — whether private sector insider knowledge traded in prediction markets is illegal — remains open.
This regulatory uncertainty is itself a risk factor for prediction markets. Platforms that operate legally today could face new restrictions tomorrow. The rules are genuinely unclear, and that ambiguity creates real exposure for participants.
What the Next Few Years Look Like
Prediction markets will almost certainly grow. Global economic uncertainty, central bank policy calendars, and major geopolitical events all provide fertile ground for new markets. The more interesting question is whether institutional money makes these platforms more accurate price discovery tools, or whether it simply makes systematic biases operate at larger scale and faster speed.
The convergence between prediction markets and traditional financial markets is likely to accelerate. ETF filings, broker integrations (like Kalshi’s partnerships with Robinhood and Webull), and growing institutional monitoring all point toward prediction markets becoming a more embedded part of the broader financial information ecosystem.
Whether that’s a net positive — producing better information for better decisions — or a net negative — introducing gambling mechanics and manipulation risks into financial infrastructure — is one of the more consequential questions in market structure right now.
Understanding how these platforms work, what their limitations are, and what the research says about their accuracy is increasingly valuable context for anyone interested in how modern financial markets process information and uncertainty.
For anyone who participates in financial markets and wants to ensure they’re doing so through properly regulated infrastructure, platforms like Fortrade — which operates under robust regulatory oversight across multiple jurisdictions — provide the kind of transparent, supervised environment that matters when navigating complex and fast-moving markets.
This article is for informational and educational purposes only and does not constitute financial advice. Prediction markets carry substantial risk of loss. Regulatory frameworks are evolving and may change. Always consult a qualified financial advisor before making investment decisions.
Frequently Asked Questions
What is a prediction market?
A prediction market is a platform where participants buy and sell contracts on the outcome of future events. Each contract is priced between 0 and $1, with the price reflecting the market's collective probability estimate for that event occurring. If the event happens, the contract pays $1. If it doesn't, it expires worthless.
Are prediction markets legal?
In the US, Kalshi is fully regulated by the CFTC as a Designated Contract Market. Polymarket operates on blockchain infrastructure and received regulatory clarity in 2025 after years of scrutiny. State-level legal battles continue in several jurisdictions, with some gaming commissions contesting whether these markets constitute unlicensed sports wagering. Outside the US, the regulatory picture varies considerably by country.
How accurate are prediction markets at forecasting events?
Research from the Federal Reserve found prediction markets can be as accurate as — and sometimes more accurate than — traditional economic surveys for macroeconomic forecasting. However, they've also gotten major events badly wrong, including Brexit and the 2016 US election, often right up until results came in. They reflect current consensus, not truth — and consensus can be systematically wrong.
Can prediction markets be manipulated?
Yes, and it's a documented problem. In thin markets with limited liquidity, coordinated participants can inflate unlikely outcomes, create artificial sentiment through wash trading, or use layered orders to move contract prices. Traditional financial markets face manipulation too, but prediction markets historically have had less liquidity and lighter oversight, which amplifies the risk.
What is the difference between a prediction market and gambling?
This is one of the central regulatory debates. Functionally, buying a prediction market contract on an election outcome shares structural similarities with placing a bet. Legally, the distinction hinges on whether the contracts serve a price discovery or hedging function — which is how Kalshi successfully argued its case before the CFTC. Critics argue the distinction is artificial, pointing out that many prediction market participants are motivated by entertainment or speculation rather than economic hedging. The debate is far from settled, and it has real implications for how these platforms are regulated.
