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Liquidity Rewards Can Move Quotes Without Making Forecasts Worse
Kalshi rewards resting orders, which can change quoting incentives. Learn what the rules establish and what evidence would show less accurate market odds.
Kalshi’s liquidity rewards can change the prices traders are willing to quote, but the program rules do not establish that the resulting odds are less accurate. A subsidy may narrow a spread and improve price discovery, or support a quote for reasons other than the trader’s probability estimate. Demonstrating worse forecasts requires timestamped prices and resolved outcomes—not merely evidence that rewards exist.
Select the evidence available; see which claim it supports.
What Does Your Evidence Support?
Choose findings from an evaluation, not what you suspect rewards do.
For interpretable comparisons, account for event news, time to resolution and differences between contracts. This tool does not perform those tests.
No Demonstrated Quote Or Accuracy Effect
Program rules establish an incentive to supply liquidity. Without quote comparisons and resolved-outcome testing, they do not establish that rewards moved prices or made forecasts worse.
Next evidence needed: timestamped bids, asks and depth around reward changes, followed by outcome-based evaluation across comparable contracts.
Sources: Kalshi program rules and CFTC staff advisory. Selections describe your evidence; no measured Kalshi accuracy effect is supplied here.
A 60-cent YES contract paying $1 if the event occurs is conventionally read as a roughly 60% implied probability—not a guarantee. Kalshi describes contract prices as reflecting traders’ views of an event’s chances. Liquidity rewards add another consideration: compensation for keeping orders available. Kalshi’s explanation of prediction markets
Kalshi Rewards Resting Orders, Not Just Trades
As of October 3, 2026, Kalshi’s help page says its Liquidity Incentive Program pays for eligible resting orders even if they never fill. Resting orders are unexecuted orders available on the book. Market pages identify active reward periods and their schedules. This is a reward for available liquidity, not simply for executed trading volume. Kalshi can modify or end the program. Program details
The scoring mechanics determine which visible orders benefit:
| Mechanic | What The Rules Say | Reading Implication |
|---|---|---|
| Size and price | Scores reflect size and distance from a reference price, normalized against qualifying orders on the same side. | Depth can reflect reward competition. |
| Book-based reference | On each side, Kalshi walks down from the best bid until cumulative size reaches one fifth of the target size. | A tiny best bid need not set the reference. |
| Distance discount | Orders at or better than the reference receive full credit; lower bids are discounted by a factor raised to their distance in ticks. | Reward value depends on price placement. |
| Two-sided requirement | Snapshots lacking target size on either the YES or NO side are excluded. | Visible orders do not always earn rewards. |
Ticks are the market’s minimum price increments. At a discount factor of 1, there is no distance penalty. Excluded snapshots reduce the reward pool actually paid out.
These mechanics appear in the program help page and Kalshi’s July 15, 2026 amendment, effective July 30. The reference comes from the order book, not an external forecast of the event.
The practical implication is narrower than “rewarded odds are wrong”: visible size can reflect competition for a reward pool, not just strong conviction. A resting order nevertheless remains exposed to execution. Compensation does not eliminate trading risk.
A Subsidy Can Support An Otherwise Unattractive Quote
Consider a deliberately simplified example—not an observed Kalshi trade.
A trader believes YES has a 60% chance and places a resting bid for 100 contracts at 61 cents. Suppose all 100 subsequently fill. If that probability remains valid conditional on execution, the expected settlement value is $60 against a $61 purchase cost: a $1 expected loss before fees and other costs.
If keeping the bid on the book before execution generates an expected incremental reward of $2, the combined activity could have positive expected value despite the unfavorable purchase price. The subsidy can therefore support a quote the trader would otherwise reject.
| Hypothetical Component | Value |
|---|---|
| Expected settlement value | $60 |
| Purchase cost | $61 |
| Expected trading result before fees | −$1 |
| Assumed incremental reward | $2 |
| Combined expected result before other costs | +$1 |
This is not a reward-adjusted probability calculation. Kalshi’s rewards depend on relative scores, time on the book, qualifying snapshots and competition—not a guaranteed payment per filled contract. Execution may also occur precisely when new information makes the quote unfavorable.
The example does not imply that YES odds must rise. Incentives operate on both sides. Additional bids and offers could narrow a spread around an unchanged midpoint; uneven participation could instead move that midpoint. A changed quote, a narrower spread and a worse forecast are three different findings.
The CFTC Warning Does Not Establish Kalshi Forecast Distortion
On August 12, 2026, the CFTC’s Division of Market Oversight reported procedural or substantive deficiencies in an increasing number of incentive-program filings, particularly for event contracts. Its announcement concerns program disclosure and compliance—not a finding that Kalshi’s odds were manipulated.
The underlying staff advisory recognizes that properly designed incentives can improve liquidity and price discovery. It also warns that excessive compensation and certain reward structures can encourage abusive trading or unintended market effects.
Those warnings support scrutiny of incentives, not a blanket verdict against rewarded markets. Neither document quantifies an effect on Kalshi’s forecast accuracy.
Quote Effects And Forecast Accuracy Need Separate Tests
For an individual market, read the active reward schedule alongside the timestamped YES bid, ask and available size. The midpoint is halfway between the best bid and ask. A stale last trade is not interchangeable with a current midpoint, and subsidized depth is not evidence of forecast accuracy. See reading beyond Kalshi’s headline odds.
To test whether rewards changed quotes, compare spreads, depth and midpoints around reward starts, changes and ends. Account for event news and time to resolution. A narrower spread with a stable midpoint is different from a shift in the market’s implied probability.
To test whether forecasts became worse, compare timestamped probabilities with resolved outcomes across comparable contracts, using calibration and forecast-error measures. Rewarded markets may differ systematically from unrewarded ones, so a raw comparison is not enough to attribute a difference to rewards.
Calibration and Brier score measure different things. Neither can be inferred from a bigger order book. A finding that quotes moved would establish a pricing effect; it would still leave the accuracy question open unless outcome-based testing showed deterioration.
The cited program rules and regulatory documents provide no measured direction or magnitude for Kalshi’s forecast-accuracy effect. There is therefore no defensible fixed number of percentage points to subtract from rewarded odds. Read the executable prices and their timing, and keep the incentive mechanism separate from evidence about whether those prices forecast outcomes well.