You’re probably hearing a lot about prediction markets right now. Polymarket and Kalshi are processing billions in trading volume. But if you’re thinking about integrating prediction markets or building your own platform, you need to understand what’s actually happening under the hood.
This article is part of our comprehensive prediction market overview, where we explore the technical architecture, regulatory landscape, and implementation strategies for building prediction market platforms at scale.
So let’s dig into price discovery, liquidity provision, and settlement systems. This is how these markets actually work.
How Do Prediction Markets Achieve Price Discovery Through Trading Activity?
Price discovery works through the continuous interaction of buy and sell orders. Prediction markets are based on the theory that when people have financial stakes, they collectively predict outcomes more accurately than any single expert.
When traders spot an informational edge, they can immediately profit by buying underpriced contracts or selling overpriced ones. This creates rapid price convergence towards the true probabilities.
What Is the Efficient Market Hypothesis in Prediction Markets?
Eric Zitzewitz, economics professor at Dartmouth, puts it this way: “Financial markets are generally pretty efficient, and the evidence suggests that the same is true of prediction markets. There’s no virtue-signalling in an anonymous market when you’re betting.”
The difference between prediction markets and polling is the financial incentive. Polls capture what people say they believe. Prediction markets capture what people believe enough to actually risk money on.
How Do Order Books Enable Transparent Price Formation?
Most modern prediction markets use either a central limit order book (CLOB) or an automated market maker (AMM). Continuous Double Auction or Automated Market Makers provide the foundation.
In a CLOB system like Polymarket’s, the order book displays all active buy and sell orders. When orders match, trades execute. The bid-ask spread narrows as information gets incorporated into prices.
Arbitrage keeps prices efficient. Complete sets of binary contracts must sum to $1.00. If prices drift from this invariant, arbitrageurs profit from the mispricing, which forces prices back into alignment.
Why Do Financial Incentives Improve Forecast Accuracy?
Research on the 2024 US presidential election found that prices in modern prediction markets strongly led traditional polls. Financial skin in the game changes behaviour. People get serious when their money’s on the line.
What Is the Difference Between Order Book and Automated Market Maker Trading Mechanisms?
A central limit order book (CLOB) is a real-time display of all active buy and sell orders, divided into bids and asks. AMMs provide liquidity in pools where prices are determined by algorithms based on asset ratios.
They’re fundamentally different approaches to the same problem: how do you make sure trades can happen?
How Does Central Limit Order Book (CLOB) Order Matching Work?
The matching engine follows price-time precedence rules. Orders execute based on price level first, then timestamp. Simple.
Polymarket’s Order Book is hybrid-decentralised, with off-chain matching whilst settlement executes on-chain. This gives you the speed of centralised matching with the trustlessness of on-chain settlement. Best of both worlds.
What Is the pm-AMM and How Does It Differ from Standard AMMs?
Paradigm introduced the pm-AMM, an automated market maker specifically designed for prediction markets.
Standard AMMs allocate capital across all price ranges, even the unlikely ones. The pm-AMM assumes outcome tokens follow Gaussian score dynamics, which lets it concentrate capital where actual trading occurs rather than wasting it on extreme price ranges.
The pm-AMM uses the Loss-vs-Rebalancing (LVR) framework to make LVR proportional to pool value. This creates more predictable economics for liquidity providers. If you’re providing liquidity, you want to know what you’re signing up for.
When Should Platforms Choose CLOBs vs AMMs for Liquidity?
AMMs have revolutionised market-making by automating the process. This solves the cold start problem for new markets.
AMMs excel at price discovery for “long-tail” assets with lower trading volumes. CLOBs shine when you have mature markets with serious trading activity.
The pattern is clear: start with AMMs to bootstrap liquidity, then migrate to CLOBs as markets mature and volumes increase.
How Do Market Makers Provide Liquidity in Prediction Markets?
Market makers keep trading smooth by offering both bids and asks. They profit from the bid-ask spread whilst bearing inventory risk from price movements. It’s their job to be there when you want to trade.
If you’re planning to implement liquidity provision via APIs, understanding these market maker economics is essential for running sustainable market operations.
What Are the Economics of Market Making Profitability?
The spread is the difference between the best ask and the bid. Market makers profit from this spread whilst exposed to inventory risk. When the price moves against their holdings, they lose money.
For AMMs, Loss-Versus-Rebalancing (LVR) can decrease LP earnings by 10-12% annually. That’s not trivial.
How Do Platforms Bootstrap Liquidity in New Markets?
Platforms rely on institutional market makers or internal liquidity. The strategy typically involves providing initial liquidity themselves or offering fee incentives to attract market makers.
Someone needs to go first. Usually that’s the platform putting up initial capital.
What Trading Volumes Indicate Mature Market Maker Participation?
Between January to October 2025, prediction market platforms generated over $27.9 billion in trading volume. Weekly trading volume reached an all-time high of $2.3 billion in the week of 20 October 2025.
You don’t hit those numbers without institutional players providing deep liquidity. Those volumes require serious infrastructure and serious capital.
How Do Settlement and Payout Systems Process Binary Contract Resolution?
Settlement systems use oracle data to determine event outcomes and trigger smart contract payouts. Binary contracts pay $1 per winning share and $0 for losing shares. Straightforward.
CTFExchange.sol is the primary execution contract handling order verification and signature validation. Shares are ERC-1155 tokens using Gnosis’s Conditional Tokens Framework.
For a detailed technical exploration of oracle verification and resolution systems, see our dedicated guide covering centralised versus optimistic oracle approaches.
What Is the Conditional Tokens Framework and How Does It Enable Settlement?
Each market requires three parameters: questionId, outcomeSlotCount (always 2 for binary markets), and Oracle Address.
These inputs generate a conditionId via keccak256 hashing, creating unique token identifiers for YES and NO shares.
Polymarket uses off-chain order matching with on-chain settlement via Polygon using USDC. Fast matching, trustless settlement.
After resolution, token holders call redeemPositions to burn shares and claim collateral. Only winning outcome holders receive payouts. Losing shares are worthless.
How Do Oracles Resolve Event Outcomes Trustlessly?
The platform employs the UMA Optimistic Oracle, featuring a $750 bond and 2-hour dispute window.
Any user can propose an outcome by posting a $750 bond. If nobody challenges it within 2 hours, the proposal is accepted. Disputes trigger voting among UMA tokenholders.
This optimistic approach assumes proposals are correct unless someone’s willing to put up money to prove otherwise.
What Happens When Settlement Edge Cases Occur?
The UMA optimistic oracle includes market cancellation mechanisms that refund collateral when consensus can’t be reached. If nobody can agree on the outcome, everyone gets their money back.
What Are Binary Outcome Tokens and How Do Complete Sets Work?
Prediction markets operate as fully-collateralised binary options with the invariant that YES + NO = $1.00. Always.
Events have two opposing shares: YES and NO, with prices between $0 and $1. The contract price directly represents market-implied probability. A YES share trading at $0.65 reflects a 65% consensus likelihood. The maths is that simple.
How Does Complete Set Minting Enable Short Selling?
Shares come into existence through BuyCompleteSets. Complete sets always pay exactly $1 at resolution.
SellCompleteSets enables short-selling without traditional borrowing. You create shares by minting a complete set, selling the side you don’t want, and holding your position. No need to borrow from anyone.
What Arbitrage Mechanisms Enforce Price Bounds?
BuyCompleteSets and SellCompleteSets ensure the combined value of YES and NO shares remains close to $1.00.
If YES trades at $0.70 and NO trades at $0.35, the sum is $1.05. An arbitrageur can buy a complete set for $1.00, sell both sides, and pocket $0.05 in risk-free profit. Easy money.
This mechanism is self-enforcing. The economic incentive automatically maintains price consistency without anyone needing to police it.
How Does Order Inversion Unify Liquidity?
Automatic Order Inversion means every buy order for YES automatically appears as its inverse—a sell order for NO at the complementary price.
Bidding to buy YES at $0.60 is mathematically identical to offering to sell NO at $0.40. This prevents fragmentation and ensures deep liquidity. The order book stays unified.
What Revenue Models Support Prediction Market Platform Sustainability?
Polymarket charges no trading fees, monetising through data partnerships with Intercontinental Exchange instead.
Kalshi employs variable fee schedules ranging from 0.6% to 1.75%. Kalshi generated estimated $24M revenue in 2024 (up 1,221% year-over-year).
Different approaches, both working.
How Do Transaction Fees Balance User Costs and Platform Revenue?
Kalshi operates with CFTC-licensed exchange status and ~1% effective take rate. Polymarket operates globally, avoiding U.S. compliance costs but limiting U.S. access.
Trade-offs everywhere.
What Are the Economics of AMM Liquidity Provider Returns?
The static pm-AMM ensures uniform LVR across all prices, though LVR increases as expiration approaches. If you’re designing LP incentive programmes, LVR is the metric that matters. Ignore it at your peril.
Do Trading Volumes Support Sustainable Business Models?
Polymarket’s trading volume surged from $73M (2023) to ~$9B (2024). Analysts project prediction markets could reach $95.5 billion by 2035.
At current volumes, the business model economics work. These are real, sustainable businesses now.
What Trading Volumes Indicate Prediction Market Maturity in 2025?
Prediction market platforms generated over $27.9 billion in trading volume between January and October 2025. Weekly trading volume reached an all-time high of $2.3 billion in the week of 20 October 2025.
These aren’t hobby projects anymore.
What Are 2025 Trading Volume Milestones for Prediction Markets?
October 2025 volumes on Kalshi and Polymarket exceeded $7.4 billion, with Kalshi capturing approximately 66% of market share.
The Iowa Electronic Market (established 1988) pioneered presidential election contracts with modest volumes. Modern platforms operate at a completely different scale. We’re talking billions, not millions.
How Does Volume Distribution Compare Across Platforms?
Kalshi leads in sports betting with $1.1B monthly volume (October 2025). Polymarket dominates politics with $350M compared to Kalshi’s ~$75M.
The platforms have found different niches. And liquidity attracts more traders, which attracts more liquidity.
Do Current Volumes Support Institutional-Grade Infrastructure?
You can now execute large orders without moving the market much. This wasn’t possible five years ago. Hell, it wasn’t possible two years ago.
The infrastructure requirements are substantial: order matching engines that can handle activity surges, settlement systems that reliably process payouts, and oracle systems that accurately resolve outcomes.
What Technical Infrastructure Supports Prediction Market Mechanics at Scale?
These volumes require sophisticated infrastructure. High-volume prediction markets use hybrid architectures combining off-chain order matching for speed with on-chain settlement for trustlessness.
Why Do Platforms Use Hybrid Off-chain/On-chain Architectures?
The operator handles off-chain order management and submits matched trades to the blockchain for execution.
This gives you instant trade execution whilst maintaining trustless settlement. The operator’s privileges are limited to order matching—they can’t set prices or execute unauthorised trades. They’re just matching buyers with sellers.
How Does EIP-712 Enable Secure Off-chain Order Creation?
EIP-712 lets users create cryptographically signed orders off-chain that can be verified on-chain without submitting a transaction until the order actually matches.
This eliminates gas costs and latency. Only matched trades hit the blockchain for settlement. Everything else stays off-chain.
The trade-off is reliance on the operator for matching. If the operator goes offline, new trades can’t execute. But existing positions remain safe on-chain.
What Are the Scalability Bottlenecks for High-Volume Events?
Price-Time Priority means orders execute based on price level first, then timestamp. This requires high-performance databases that can handle the load.
Polymarket uses off-chain order matching and on-chain settlement via Polygon using USDC. Polygon provides the throughput needed without Ethereum mainnet gas costs. Because nobody wants to pay $50 in gas fees to make a $10 bet.
Polymarket partnered with Chainlink to enhance accuracy. Chainlink Data Streams deliver low-latency, verifiable oracle reports to settlement processes.
Frequently Asked Questions
How do prediction markets prevent manipulation and insider trading?
Complete set arbitrage enforces price bounds automatically. Large trades create price impact that limits profitable manipulation—if you try to move the market, you pay for it in slippage. On regulated platforms like Kalshi, oversight monitors suspicious trading patterns.
What is the minimum liquidity required to launch a prediction market?
It depends on the market type. AMM approaches enable markets to launch with minimal initial capital. You want to target sufficient depth to absorb typical trade sizes without more than 5% slippage. Otherwise your traders are getting ripped off.
How do smart contracts ensure trustless settlement without counterparty risk?
Collateral held in smart contracts guarantees payouts for all issued shares. Outcome resolution triggered by oracle data removes human discretion. Complete sets maintain 1:1 collateral backing. The smart contract code is immutable and publicly auditable—you can verify it yourself.
What happens if an oracle provides incorrect outcome data?
The UMA optimistic oracle includes dispute periods. If nobody challenges a proposal within 2 hours, it automatically accepts. Disputes trigger voting among UMA tokenholders. Market cancellation mechanisms refund collateral in unresolvable cases. So you get your money back if things go sideways.
Can prediction markets operate with zero market makers?
AMMs enable market operation without active human market makers. CLOBs require either market makers or sufficient organic trader activity. Low-liquidity markets experience wide spreads and poor price discovery. Someone needs to be providing liquidity.
How do platforms handle front-running in decentralised order books?
Polymarket’s operator handles off-chain order management, eliminating timing games. EIP-712 signed orders enable secure off-chain order creation. Orders are executed on-chain only after matching, which prevents front-running. Your order isn’t visible until it’s already matched.
What is Loss-vs-Rebalancing and why does it matter for AMM liquidity providers?
LVR measures expected value lost to arbitrageurs due to stale AMM prices—a metric that can decrease LP earnings by 10-12% annually. That’s real money. The pm-AMM design minimises LVR through Gaussian score dynamics optimisation. LVR is the metric you should use for designing LP incentive programmes.
How do order books unify YES buy orders with NO sell orders?
Automatic Order Inversion means every buy order for YES automatically appears as its inverse—a sell order for NO at the complementary price. The unified order book prevents fragmentation and ensures deep liquidity. The system maintains the invariant that YES + NO = $1.00 without anyone having to think about it.
What trading volumes are needed for prediction markets to compete with polling?
Modern platforms processing billions in volume show superior performance to polls. Prediction markets strongly led traditional polls in predicting the 2024 US presidential election. Institutional participation with $27.9B+ of volume validates their mainstream forecasting credibility.
How do platforms balance decentralisation with user experience and performance?
Hybrid architectures provide centralised UX with decentralised settlement guarantees. Off-chain order matching enables instant trade execution. On-chain settlement ensures trustless finality. The trade-off is reliance on the platform operator for matching versus the latency and cost of going fully on-chain.
What are the operational costs of running a prediction market platform at scale?
Infrastructure costs include cloud hosting, database systems, and blockchain node operations. Market making requires liquidity incentives and initial capital. Oracle services need data feeds and dispute resolution systems. Compliance requires regulatory reporting and KYC/AML systems. It’s not cheap.
How do prediction markets handle partial fills and order cancellations?
Limit orders remain in the order book until fully filled or manually cancelled. Partial fills execute available volume and leave the remainder as an open order. Market orders execute immediately at the best available prices. Smart contracts enforce order validity periods. Standard stuff.
Understanding Prediction Market Mechanics for Integration
So that’s how these markets actually work under the hood.
Price discovery happens through financial incentives that push traders to incorporate information into prices. Prediction markets outperformed traditional polls during the 2024 presidential election because traders were putting money where their mouths were.
Liquidity provision works through market makers capturing spreads whilst managing inventory risk, or through AMMs that provide passive liquidity. The playbook is clear: start with AMMs to bootstrap liquidity, migrate to CLOBs as volumes grow.
Settlement systems process payouts using smart contracts and oracles. The Conditional Tokens Framework ensures trustless collateral management, whilst UMA’s optimistic oracle handles outcome resolution.
The business model sustainability is validated by actual volumes. $27.9 billion in trading volume between January and October 2025 demonstrates that institutional market makers are participating at scale. These are real businesses now.
If you’re evaluating prediction markets for integration or building your own platform, the infrastructure requirements are clear: you need hybrid off-chain/on-chain architecture to balance UX with trustlessness, robust oracle systems for accurate resolution, and either deep liquidity from market makers or AMM designs like the pm-AMM that minimise LVR.
The platforms that have achieved scale have invested heavily in this infrastructure. Now you understand why.
For a comprehensive overview of the entire prediction market ecosystem including platform comparisons, regulatory considerations, and implementation pathways, see our understanding the market landscape guide.
