Prediction Markets··9 min read

Liquidity in Prediction Markets: Depth, Spreads, and Why Small Markets Are Hard to Trade

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Abstract

Liquidity in prediction markets reflects the volume and tightness of available buy and sell orders at any given price level, measured by order book depth and bid-ask spreads. Small prediction markets struggle to attract the participation needed to sustain tight spreads and deep order books, creating barriers to entry for traders and a self-reinforcing cycle of illiquidity. This paper examines how liquidity mechanics differ in prediction markets compared to equity and commodity exchanges, and why market size directly constrains trading efficiency.

Core Concept

Liquidity in prediction markets describes the ease with which a trader can execute an order at a price close to the current market level without meaningfully moving that price. Two metrics capture this: order book depth (the quantity of contracts available at each price level) and the bid-ask spread (the difference between the highest price someone will pay to buy and the lowest price someone will accept to sell) [1].

In equity and futures markets, liquidity emerges from high transaction volumes, many competing market participants, and continuous price discovery. Prediction markets, markets where outcomes are binary or categorical and settle based on a real-world event, operate under different constraints. They are event-specific, with fixed lifespans ending on a settlement date. A market on the 2028 U.S. Presidential election has a four-year window; a market on tomorrow's weather expires in 24 hours. Participants cannot hold positions indefinitely; liquidity is borrowed against time running out.

This structural constraint matters because it limits the population of potential traders. A major stock attracts institutional investors, retail speculators, and market makers worldwide. A prediction market on a niche local election, an obscure weather outcome, or a low-probability geopolitical event may attract only dozens or hundreds of traders, and often only during specific windows near the settlement date. Fewer participants mean fewer orders at each price level, wider spreads, and higher costs to execute.

How Depth and Spreads Work Mechanically

Order book depth is the cumulative quantity of contracts posted at successive price levels away from the midpoint (the average of the best bid and best ask). A deep order book, say, offering 100 contracts each at 0.48, 0.49, 0.50, 0.51, 0.52, allows a trader to execute a large order with only small price concessions. A shallow book, 5 contracts at 0.48, 10 at 0.50, 3 at 0.52, forces the same trader to accept worse prices or split the order across time [1].

The bid-ask spread is the simplest liquidity measure: if the best bid is 0.49 and the best ask is 0.51, the spread is 0.02 (or 2 cents per contract). A trader buying 100 shares immediately after another sells 100 pays an implicit cost of 2 cents times 100, or 2 dollars. The spread compensates market makers for the risk of holding inventory and the possibility of adverse price movement between buying and selling. In highly liquid markets (S&P 500 futures, major currency pairs), spreads are tiny fractions of a cent. In thinly traded options or small-cap stocks, spreads can be 1-5% of the price.

Prediction markets inherit this logic. Most regulated prediction markets in the United States operate on exchanges such as Kalshi, where contracts are binary: Yes or No, each worth $1 at settlement. A contract trading at $0.65 represents a market-implied 65% probability. If the best bid is $0.63 and the best ask is $0.65, the spread is 2 cents, 2% of the contract value. Traders paying the ask price buy at a 2% disadvantage immediately [1].

Small markets widen spreads through a feedback loop. Few orders at any price level mean market makers face high inventory risk: if a maker buys 100 contracts at $0.65, finding 100 willing sellers at $0.67 within a reasonable time is uncertain. To compensate for that risk, the maker widens the spread, perhaps quoting $0.63 to buy and $0.69 to sell. Wider spreads discourage retail traders, who need tight prices to justify the trading. Fewer trades mean less information reaches the market, increasing uncertainty and reinforcing wide spreads [1].

Worked Example: A Niche Market vs. a High-Attention Market

Consider two concurrent Kalshi markets: (A) "Will the S&P 500 close above 6,500 by December 31, 2026?" and (B) "Will the mayor of Springfield win re-election in 2027?"

Market A (S&P 500 prediction) attracts tens of thousands of traders. Professional traders arbitrage it against S&P 500 futures and options. Hedge funds, algorithmic traders, and retail participants compete on price discovery. At any given moment, the order book shows hundreds or thousands of contracts offered at successive cent levels. The spread is typically 1 cent (0.01), representing 1% or less of the contract value. A trader executing a 5,000-contract order moves the market by a few cents at most.

Market B (Springfield mayoral election) attracts perhaps 200 participants, mostly local residents and a handful of political speculators. Interest spikes two weeks before the election, then recedes. On a slow Tuesday in mid-2027, the order book might show: 5 contracts offered at $0.42 (no buy interest), 12 contracts wanted at $0.38. The spread is 4 cents (0.04), or roughly 10% of the contract value. A trader wanting to buy 500 contracts must accept steeply worse prices or place a limit order and wait, possibly until hours before the election, when more traders enter.

The depth difference is stark. Market A might have 50,000 contracts on offer between prices $0.45 and $0.55; Market B has perhaps 500 across that same range. Both are legitimate markets offering real price discovery, but Market B's illiquidity imposes a hidden cost: anyone entering early pays wide spreads or faces long waits; anyone entering late pays momentum prices driven by late-arriving information rather than fundamental value.

Limitations

Several factors limit the scope of this analysis.

First, the above example is illustrative; actual order-book depths and spreads for Kalshi markets are not published in a full, time-stamped dataset [1]. Researchers and traders must observe real-time quotes, and data on historical order-book states is proprietary to the exchange. Statements about typical spreads or depths in small prediction markets are therefore based on trader observation and market design theory, not systematic measurement.

Second, illiquidity does not render small prediction markets useless. If a trader's time horizon is long (weeks or months) and the price is wrong by a large margin, the spread and depth constraints may matter little. A trader confident that the Springfield mayor will lose can accumulate a position over time at an average price that still offers good expected value. The constraint is real for high-frequency or large-volume traders; it is less binding for patient capital.

Third, the self-reinforcing cycle of illiquidity is not inevitable. Market design choices matter. Automated market makers (AMMs), in which a smart contract provides liquidity at a pre-set pricing function, can reduce spreads in small markets by eliminating the need for a human market maker to carry inventory risk. Some prediction markets employ AMMs to bootstrap liquidity; however, AMMs shift the cost from spreads to slippage (the difference between expected and executed price), which can be equally unfavorable for large trades [1].

Fourth, regulatory and jurisdictional differences affect liquidity. Prediction markets in the United States are heavily restricted; only Kalshi and a few others operate under CFTC exemptions [1]. Markets in Europe, Australia, and other regions operate under different rules and may have different depths. Cross-exchange arbitrage is limited by regulation and operational friction, preventing global liquidity pools.

Summary

Liquidity in prediction markets is a function of participation, time, and order-book structure. Small markets, those with few participants or niche outcomes, necessarily suffer wider spreads and shallower books, raising trading costs and discouraging participation. This creates a barrier to entry and a slow-to-exit problem for any trader in a newly opened market. The mechanisms are the same as in any market: fewer orders, wider spreads, deeper price impact. What is unique to prediction markets is the structural constraint of event-specificity and fixed settlement dates, which limits the population of available traders and prevents the deep liquidity pools that high-volume markets enjoy.

Traders using prediction markets should account for liquidity costs upfront, treating tight spreads as a statistical advantage only for large, highly public events, and budgeting for wider spreads in small or specialized markets. Market designers seeking to improve prediction markets for research or public forecasting should recognize that depth and spreads are not flaws to fix merely by adding traders; they are inevitable consequences of market size, and any mechanism to improve them must either expand participation or transfer inventory risk to an entity willing to absorb it.

Key definitions

Bid-ask spread: The difference between the highest price a buyer will pay (bid) and the lowest price a seller will accept (ask). Expressed in dollars or as a percentage of the contract price, it represents the immediate cost of entering or exiting a position.

Order book depth: The total quantity of contracts (or volume) posted for sale or purchase at each successive price level away from the midpoint. Deep order books allow traders to execute large orders without significant price concessions.

Market maker: A trader or institution that simultaneously posts buy and sell orders to profit from the spread; provides liquidity in exchange for bearing inventory and price risk.

Binary contract: A financial contract that pays a fixed amount if a specific condition occurs (e.g., $1 if a candidate wins) and zero if it does not.

Price impact: The adverse movement in the market price resulting from a trader's own order; larger orders typically incur larger price impact, especially in illiquid markets.

Settlement date: The time at which a prediction market resolves and all contracts expire, paying out or expiring worthless based on the real-world outcome.

Liquidity: The ability to buy or sell a large quantity of an asset quickly, at a price close to the current market level, without incurring excessive costs.

References

Educational research on historical data only. Not investment advice, not a signal, and never a performance promise. Past results do not predict future performance. Every reference is link-verified before publication and every paper is re-audited weekly against the library's editorial standard.

Last reviewed by the PropLedger research pipeline: 2026-08-30. Educational research on historical data, not financial advice.

Educational research on historical data only. Not investment advice, not a signal, and never a performance promise. Past results do not predict future performance. Every reference is link-verified before publication and every paper is re-audited weekly against the library's editorial standard. Found an error? Email support@prop-ledger.org and the paper is corrected or withdrawn.
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