How Do Prediction Markets Handle Liquidity?

Liquidity — the ability to buy or sell contracts without significantly moving the price — is the single most important operational challenge in running a prediction market. Without sufficient liquidity, prices don't reflect genuine probabilities, spreads are too wide for traders, and the market fails at its core purpose.

Two Primary Liquidity Models

1. Automated Market Makers (AMMs)

An automated market maker is an algorithm that always stands ready to buy or sell contracts at algorithmically determined prices. The most common approach in prediction markets is the Logarithmic Market Scoring Rule (LMSR), developed by economist Robin Hanson.

How LMSR works:

• The AMM maintains a pool of funds and calculates contract prices based on the current distribution of outstanding positions.
• When a trader buys "Yes" contracts, the "Yes" price increases and the "No" price decreases.
• The AMM subsidizes the market — it will always lose money overall, but the cost is bounded and predictable.

Advantages:

• Guaranteed liquidity at all times, even in thin markets
• No dependence on third-party market makers
• Predictable maximum subsidy cost for the operator

Disadvantages:

• The operator bears the cost of subsidizing the market
• Price impact can be large in thinly funded markets
• Less capital-efficient than order book models at scale

Polymarket uses a variant of AMM combined with order books. Kalshi primarily uses order books.

2. Central Limit Order Books (CLOBs)

An order book model matches buyers and sellers directly, similar to a stock exchange. Traders submit limit orders specifying the price and quantity they're willing to trade, and the exchange matches compatible orders.

How it works:

• A trader wanting to buy "Yes" at $0.65 submits a bid.
• A trader wanting to sell "Yes" at $0.65 submits an ask.
• When bid meets ask, the trade executes.

Advantages:

• No operator subsidy required — liquidity comes from traders and market makers
• Tighter spreads in active markets
• More capital-efficient at scale

Disadvantages:

• New or niche markets may have no liquidity
• Depends on professional market makers to provide consistent quotes
• Cold-start problem — markets with no volume attract no market makers

3. Hybrid Models

Most production prediction markets use a hybrid approach:

• AMM bootstrapping: Use an AMM to provide initial liquidity when a market launches, then transition to order book matching as volume grows.
• Market maker incentives: Pay professional market makers rebates or subsidies to quote prices on order books.
• Platform-provided liquidity: The operator itself acts as market maker using algorithmic pricing, effectively running a managed book similar to a sportsbook.

Key Liquidity Metrics

Operators evaluating prediction market infrastructure should track:

Why Liquidity Matters for B2B Operators

For iGaming operators integrating prediction markets via API:

1. Liquidity determines product quality. If your users see wide spreads or can't get filled, the product feels broken. An API provider's liquidity infrastructure is more important than its question catalog.

2. Risk transfer depends on liquidity. When an operator takes a position from a user, it needs to hedge that risk — either through the API provider's market or its own book. Thin liquidity means unhedgeable risk.

3. AMM-based APIs are operationally simpler. If the API provider uses LMSR pricing, the operator always gets a fill at a known price. Order book-based APIs require handling partial fills, rejects, and queue priority.

4. Liquidity costs appear somewhere. Either the API provider subsidizes liquidity (and charges higher fees), or the operator accepts wider spreads (and users get worse prices). Understanding where the liquidity cost sits is essential for unit economics.

The liquidity model you choose — or that your API provider uses — directly shapes your margin structure, risk profile, and user experience.

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Last verified: March 2026