Research · February 2026
Maker vs. Taker Returns in Prediction Markets: An Empirical Analysis
By Alpha Beta
Abstract
We analyze 199,494 trades across 2,000 prediction markets on Kalshi, spanning 1,292 distinct events and approximately 26.2 billion contracts in volume. Our findings reveal a systematic structural advantage for makers (liquidity providers) over takers (aggressive order submitters) at the vast majority of price levels.
Key Findings
Makers Earn Positive Excess Returns
Across our dataset, makers demonstrated win rates significantly higher than the price-implied probability at most price levels. This advantage stems from passive spread capture and inventory management—not directional forecasting skill.
We confirm this through direction symmetry analysis: the Cohen's d between maker YES and NO position returns is approximately 0.02, indicating virtually no directional bias in maker profitability. Makers profit structurally, regardless of which side they quote.
Takers Face Adverse Selection
Takers show negative excess returns at 80 of 99 price levels. The underperformance is most pronounced in low-price (longshot) buckets, where the favorite-longshot bias creates the widest gap between implied and realized probabilities.
This finding has direct implications for trading strategy design: any systematic approach that relies on aggressive (taker) order flow must account for this structural headwind.
Category-Level Decomposition
Market category significantly moderates the maker-taker dynamic:
| Category | Taker Excess Returns | Interpretation |
|---|---|---|
| Crypto | +1.30pp | Takers have informational edge |
| Finance | +2.34pp | Informed trading flow |
| Sports | -0.64pp | Makers advantaged |
| Esports | -5.56pp | Strongest maker advantage |
| Entertainment | -3.06pp | Significant maker edge |
| Media | -3.02pp | Significant maker edge |
In crypto and finance categories, takers show positive excess returns—suggesting informed trading flow that overwhelms the spread. In entertainment, esports, and media categories, the maker advantage is pronounced, indicating less informed taker flow and wider exploitable spreads.
Hourly Analysis
Peak trading hours (17-20 ET) show the highest volume with relatively favorable excess returns for makers. Off-peak hours (0-5 ET) show lower liquidity and wider spreads, creating different risk-reward profiles for market making strategies.
Implications for Strategy Design
These findings inform our approach to systematic prediction market trading:
- Market making should be category-adaptive. A 2-cent spread is optimal for sports; crypto requires 6 cents to compensate for informed flow.
- Favorite-longshot bias creates directional opportunity. The systematic mispricing of longshots, particularly in crypto markets, offers a distinct alpha source separate from market making.
- Position sizing must account for edge uncertainty. Standard Kelly criterion oversizes positions when applied to estimated (not known) edges. Half-Kelly or empirical Kelly adjustments produce better risk-adjusted outcomes.
Methodology
All analysis uses complete order book data from Kalshi's public trade feed. We compute excess returns as the difference between actual win rate and price-implied probability at each cent price level. Statistical significance is assessed via bootstrapped confidence intervals and Cohen's d effect sizes.
This research is for informational purposes only and does not constitute investment advice. Past performance is not indicative of future results.