Prediction market fees and value accrual: who captures the spread

Every trade has friction. Someone captures that friction as revenue. In prediction markets, fee structures determine who profits beyond correct predictions. Traders pay for execution. Liquidity providers earn for supplying capital. Protocols capture value for coordinating the system. Understanding these economics separates sustainable prediction market businesses from subsidized experiments.

Where fees come from in prediction markets

Prediction markets generate fees at three points in a trade's lifecycle.

Trading fees apply when positions open or close. These might be percentage-based (0.1% of notional) or fixed per contract ($0.01 per share). Frequency traders care deeply about trading fees. Each round trip compounds the cost drag.

Resolution fees apply when markets settle. Some protocols charge to distribute winnings, covering the cost of oracle services and dispute resolution. These fees affect realized returns but don't impact trading decisions.

Spreads represent the implicit cost of trading. The gap between bid and ask prices goes to whoever provides liquidity. Wide spreads benefit market makers. Tight spreads benefit traders. The competitive dynamics of liquidity provision determine where spreads settle.

Fee structures across major platforms

Fee models diverge across prediction market platforms, reflecting different business strategies and regulatory constraints.

Polymarket

Polymarket charges zero explicit trading fees. Revenue comes from different channels.

Liquidity providers earn spreads by posting orders. Active market makers quote bids and asks, profiting from the difference when both sides execute. Polymarket doesn't take a cut of these spreads directly.

The platform generates revenue through market creation fees and potential future monetization paths. Venture funding has subsidized operations during the growth phase.

For traders, zero fees mean execution costs come entirely from spreads. Liquid markets on major events often show one to two cent spreads. Illiquid markets can gap to five cents or wider.

Kalshi

Kalshi operates as a CFTC-regulated exchange with explicit fee structures. Trading fees run approximately $0.01 to $0.02 per contract, varying by market type. High-frequency traders accumulate significant fee drag. Casual participants barely notice.

The regulated structure attracts institutional participants willing to pay for compliance. Retail traders may find fees reduce edge on small-scale strategies.

Decentralized alternatives

Decentralized prediction protocols implement on-chain fee mechanisms through smart contracts.

Augur's original design charged fees on market resolution, distributed to reporters who correctly identified outcomes. The system aligned incentives: accurate reporting earned rewards, inaccurate reporting lost stakes.

AMM-based platforms often embed fees in swap transactions. A 0.3% fee on each trade accumulates in the liquidity pool, increasing share value for LPs.

Who gets paid: the value distribution chain

Fees flow through a distribution chain with multiple beneficiaries.

Liquidity providers capture spreads and AMM fees. Their returns depend on trading volume, spread width, and capital efficiency. High volume with tight spreads can generate attractive yields. Low volume or adverse selection can produce losses.

Market makers earn similar returns on order book platforms but actively manage positions. Professional market makers use sophisticated strategies to maintain profitability while providing tight spreads.

Oracles and reporters receive compensation for truth-finding. Resolution services cost money to operate and secure. Fee allocations to oracles fund the infrastructure that makes prediction markets trustworthy.

Protocol treasuries accumulate fees for development, operations, and ecosystem growth. Well-designed treasuries fund ongoing improvement. Poorly managed treasuries become slush funds or get drained through governance attacks.

Token stakers may receive fee distributions if the protocol implements revenue sharing. This creates direct cash flow claims, transforming governance tokens into productive assets.

Financial market implications

Fee structures shape prediction market competitiveness against traditional alternatives.

Spreads determine viability for hedging. If prediction market spreads exceed implied volatility spreads in options markets, sophisticated traders will use options instead. Fee competition with traditional derivatives matters for institutional adoption.

Low fees enable tighter spreads. Platforms that minimize extraction leave more room for market makers to quote aggressively. Competition among venues could drive fees toward marginal cost, benefiting traders at the expense of platform revenue.

Fee revenue creates protocol valuation anchors. Prediction market tokens with clear claims on fees can be valued using cash flow models. Those without fee capture mechanisms rely on speculation and governance premium alone.

Economic implications

Beyond individual platform dynamics, prediction market fee economics have broader effects.

Real yield opportunities emerge. Prediction market liquidity provision can generate returns from genuine economic activity: the value of price discovery and liquidity services. This differs from yield farming schemes that redistribute token emissions.

Fee extraction levels affect information quality. High fees discourage marginal participants. If prediction markets charge too much, only highly informed traders participate, potentially reducing the diversity of information sources that makes markets accurate.

Rent-seeking risks mirror traditional finance. If prediction markets consolidate into a few dominant platforms, those platforms may raise fees without competitive pressure. The promise of decentralization partly addresses this: permissionless alternatives can emerge if incumbents extract too much.

Evaluating fee sustainability

A framework for assessing prediction market fee economics:

Calculate all-in trading costs. Add explicit fees plus typical spreads plus gas costs for on-chain transactions. Compare across platforms for equivalent market types.

Identify fee beneficiaries. Trace where fees flow. Do they reach token stakers? Treasury? Market makers only? The distribution reveals who captures value from protocol activity.

Compare fee revenue to token incentives. If a protocol pays more in token rewards than it earns in fees, the economics are unsustainable. The difference must come from somewhere, typically future token holders through dilution.

Assess competitive positioning. Can the platform maintain current fee levels if competitors undercut? Network effects and switching costs determine pricing power.

Related Concepts

• Prediction market mechanics: How order books and AMMs work
• Token buybacks vs dividends: Comparing value accrual mechanisms
• Protocol revenue: How to measure what protocols actually retain
• Real yield: Distinguishing sustainable returns from emissions