The Economics of Prediction Markets Explained

Quicknode

September 29, 2026 — 15 min read

The 2024 US presidential election had billions watching. Most waited for a name to see whether their candidate had won.

Others watched something else: the price of who would win and how much they could make if they were right.

Welcome to prediction markets: the "put your money where your mouth is" thesis in action.

Election markets saw billions of dollars flow in from traders expressing their beliefs with a price: a candidate trading at 60 cents implied a ~60% chance of winning.

But where did that 60% come from?

It came from people trading against one another, each with different information, incentives, capital, and risk appetite.

Today, let's explore the economic engine behind these prediction markets.

How Do Prediction Markets Turn Information Into Prices?

Prediction markets start with disagreement and end with one winner.

Participants hold different information, think about the same event differently, or simply assign different odds. The market gives those differences a price.

Beliefs Become Trades and Trades Become a Price

Now, let's play out an election prediction market that pays $1 if a candidate wins and $0 if the candidate loses.

The contract currently trades at $0.60. That means the market requires $0.60 to buy a claim that pays $1 if the candidate wins and $0 if they lose.

As more participants buy and sell, their orders compete.

• Buyers push prices higher when they see value.

• Sellers push them lower when they do not.

New information, like a poll or a debate stumble, changes those calculations, so prices move with it.

The resulting price is the price at which the market can currently match buyers and sellers. We call it the marginal price of disagreement.

When Does That Prediction Market Price Actually Represent Probability?

In a perfect world, a $0.60 price can be read as: the candidate has a 60% chance of winning.

That holds when the only thing traders care about is their belief in one outcome of an event.

But in the real world, several forces can bend traders' beliefs, and with them the price, away from the true underlying probability.

Price is observable, but probability needs to be inferred. How good that inference becomes depends partly on the people putting capital behind it.

So who is playing the game?

Who Participates in Pricing a Prediction Market?

Every price is the culmination of different players' beliefs, reasons, and goals. More importantly, their incentives differ too.

These participants don't need the same beliefs, or even the same reason for trading, for the market to work.

If it's so random, what makes information worth putting money behind in the first place?

Incentives: Why Do People Trade Predictions?

Every participant in the market expects some return. Is it all about dollars? There are several reasons information becomes a tradable position.

Monetary Incentives to Reveal Information Through Prediction Markets

The most direct incentive is profit. It's simple math: belief minus price minus fees.

But trading profit is only one part of the incentive system. As we saw, there are different participants, and the market pays each of them different incentives.

Incentives and rewards, more or different, do not automatically produce better predictions.

• Liquidity programs can deepen a market without adding information.

• Volume incentives like airdrops can increase trading without improving the price.

On the other hand, money and profit don't explain every trade.

Non-Monetary Motivations to Place Trades

A public scoreboard, or the attachment of belief to money, can also bring participants into the same market for entirely non-monetary reasons.

Prediction markets need these kinds of players too, because they attract more passion-first, informed participation, which pushes markets toward accuracy.

Remember: Reputation too can do the work that cash usually does.

So, incentives (monetary and otherwise) bring participants to the market. But none of that matters if there's no liquidity in the market to trade with and against.

How Does Liquidity Affect Prediction Markets?

Liquidity is the money waiting on the other side of a trade.

When liquidity is scarce, even good information can struggle to enter the market because entering or exiting a position costs too much.

How Liquidity Affects Price Discovery

Three measures matter here:

1. Spread: The gap between the best available buy and sell prices

2. Depth: How much can be traded near the current price

3. Slippage: How far the average execution price moves during a trade

Consider a contract trading around $0.60. A trader believes it should trade at $0.70 and wants to buy $10,000 worth.

• Deep market: The $10,000 order executes around $0.60 or $0.61, so the information can enter the price.

• Thin market: The same order pushes execution toward $0.68 or $0.70, where much of the expected edge disappears in the trade itself.

This creates a feedback loop: real liquidity is a must for information to reach its truest price.

The reverse also holds. Thin markets > discourage larger informed positions > leave more room for stale prices > make individual trades look more informative.

Now, the liquidity has to come from somewhere.

Market Makers, LPs, and Liquidity Incentives

Liquidity is not free. Someone has to commit capital and stand ready to trade when everyone else wants to buy or sell.

That job falls mainly to market makers and liquidity providers (LPs). Their business works only if what they earn covers what they risk.

This creates a basic tension: the traders most valuable for price discovery can also be the most expensive to trade against.

Market design also decides how that burden gets distributed.

1. In an order book, participants or professional market makers post bids and asks using their own capital.

2. An AMM makes liquidity continuously available through a pool and a pricing rule, shifting the cost and risk toward the capital backing that pool.

Subsidies, maker rebates, and liquidity rewards can help de-risk market-making.

But they also cost money, which brings back the same question from incentives: whether the market is attracting useful information or simply paying for activity.

Solving for Liquidity Is Prediction Markets' #1 Priority

Liquidity is a paid economic function for prediction markets.

The tighter and deeper a market needs to be, the more someone must bear the capital cost and trading risk required to keep it that way.

This also explains why high volume and high liquidity are not the same thing.

• Volume measures how much has traded.

• Liquidity measures how easily the next trade can happen without moving the price sharply.

And that matters because prediction markets are ultimately trying to extract information.

Now, all this committed capital is waiting for one moment: settlement.

Incentives, market makers, committed capital, and continuous liquidity can all fail if the question is vague or the resolver is unreliable.

How Do Resolutions and Payouts Shape the Economics?

Every trade rests on one promise: the contract will pay according to what actually happened.

That promise makes or breaks any prediction market.

How Markets Define and Resolve Outcomes

Resolution starts with wording. A market therefore needs to define its:

1. 问题

2. Resolution criteria

3. Data source

4. Resolver

5. Dispute process

6. Final payout

Each of these choices moves money at settlement.

And of course, the more room these steps leave for interpretation, the more resolution risk traders carry. Different markets handle this differently.

And even perfect resolution leaves one more economic question: how much capital must remain locked up to earn that payout?

Payouts, Collateral, and Capital Efficiency

Most spot prediction markets use fully collateralized contracts.

• Every YES and NO pair locks $1 of collateral until settlement.

• A YES buyer at $0.60 contributes $0.60, and the corresponding NO side contributes $0.40.

• The winning contract eventually redeems for $1.

There's no leverage at play, and hence no default risk. But it comes with a cost: capital stays tied to the prediction.

Function of Time in Payouts

Assume a YES contract priced at $0.90. If correct, it returns $1, for a maximum gain of $0.10.

But time changes the economics.

If the market resolves in:

• 1 month: ~250% annualized

• 1 year: ~11%

• 2 years: ~5.4% annualized

The actual profit remains 10 cents per contract in every case. What changes is how long the 90 cents stays committed to earning it.

Think about this before placing bets on the 2028 election.

Jokes aside, prediction market platforms are building solutions to this.

How Venues Offset Locked Capital

Prediction market platforms are working out ways to reduce the cost of waiting:

Apart from these, there are a few risks traders should keep in mind:

• Payout timing (~2 hours to days) adds to the delay.

• Cashing out stablecoin settlements carries risk.

Taken together, these costs and risks set a higher bar for prediction markets.

A market needs traders, liquidity, collateral, and a credible settlement process just to produce one usable forecast.

So why build a market at all when a poll or survey can simply ask people what they think?

Prediction Markets vs Polls vs Other Ways to Aggregate Opinions

Prediction markets earn their complexity when trading adds something that asking cannot. With money involved, participants aren't just guessing. They're deciding whether their belief is strong enough, at the current price, to put something at stake.

This is the trade-off.

Prediction markets demand more from participants than the alternatives: capital, risk, and enough conviction to trade.

In return, there are incentives to be won.

When Are Prediction Markets Actually the Most Useful?

Prediction markets are the most useful when:

When these conditions are weak or absent, simpler mechanisms can work just as well or better.

So, what's next?

What's Ahead for Prediction Markets

Prediction markets are starting to look more like financial infrastructure built around uncertainty.

Three shifts matter:

• More markets: Permissionless creation can make markets viable for narrower questions, from economic releases to product launches.

• More capital efficiency: Yield-bearing collateral, shared margin, and faster settlement can reduce the cost of holding long-dated positions.

• More composability: Market prices can become inputs for other contracts, trading strategies, hedges, or financial products.

The last shift changes the economics most.

Prediction markets will become more useful as they connect and as failures become less isolated. Markets need to make information cheaper to express, capital cheaper to commit, and prices useful elsewhere.

Regardless, the 2028 election will draw the same billions of viewers, if not more, and a larger share of them will watch for that number. If we can improve today's infrastructure to serve those billions better, it will all be worth it.

FAQs on the Economics of Prediction Markets

1. Are prediction markets just gambling?

Partly. The payout works like a bet: $1 if right, $0 if wrong. Two things set a prediction market apart:

• Traders bet against each other rather than against a bookmaker.

• The price updates with every trade, so it doubles as a public forecast.

2. Can one big bettor rig the odds?

Only briefly, and only in small markets.

In a busy market, pushing the price away from fair value invites informed traders to bet the other way, and the manipulator pays for it.

3. Why does liquidity matter in prediction markets?

Liquidity makes it cheaper to buy or sell without moving the price sharply. More depth and tighter spreads let information enter prices with less slippage.

4. Who decides whether a prediction market bet wins?

It depends on the platform.

• Regulated exchanges like Kalshi settle each contract using written rules and a named data source.

• Polymarket's global site uses UMA, where anyone can propose an outcome and disputes go to a token-holder vote.

• Hyperliquid's HIP-4 makes market creators stake 500,000 HYPE, which can be taken if they settle wrongly.

5. Are prediction market prices always accurate probabilities?

No. Low liquidity, trading fees, locked capital, market constraints, and resolution risk can push a contract's price away from the underlying probability.

6. What happens to the money when a prediction market ends?

Once the outcome is resolved, winning contracts typically redeem according to their payout rules, while losing contracts expire worthless.

7. Does more trading volume mean the odds are more accurate?

No. Volume measures how much has traded, not how accurate the price is.

8. Can insiders cheat on prediction markets?

Yes, and regulators now prosecute it. In April 2026, US authorities charged an Army soldier with using classified details of the Maduro operation to bet on Polymarket.