Unusual Trading Patterns on Kalshi and Polymarket Raise Concerns About Reported Volumes and Market Integrity

Table of Contents

• You might want to know


• Main Topic


• Key Insights Table


• Afterwards...

You might want to know

Are unusually concentrated trade sizes and skewed activity across low-probability contracts indicative of inflated volume or manipulative trading?

How should exchanges, regulators and market participants interpret and respond to large volume figures that appear disconnected from resting liquidity?

Main Topic

In recent months, observers have flagged atypical trading patterns on two widely discussed prediction-market platforms, raising questions about the reliability of reported volume as a measure of market demand. On one platform, Polymarket, unusual concentration of activity on low-probability contracts has been documented on its international exchange, which is not subject to U.S. regulatory oversight. On the other platform, Kalshi, analysts and users pointed to a striking aggregation of trades at a near-identical size on an ether perpetual futures contract, producing 24-hour volume figures that appear disproportionately large relative to the amount of resting liquidity on those contracts.

These developments have prompted concern among market structure experts and some regulators because high headline volume is commonly used to signal an exchange's traction and underpin valuations. Yet when volume stems from a narrow set of trade sizes or from concentrated interest in outcomes with extremely low implied probabilities, the numbers can be misleading. Such patterns invite three distinct interpretations: legitimate concentrated interest from sophisticated traders exploiting temporary mispricings, heightened legitimate arbitrage activity in response to shifting spot prices, or potential manipulative practices such as wash trading, where matched orders artificially inflate reported turnover.

This key insight significantly impacts the understanding of reported volume: volume alone does not reveal who is providing liquidity, who is taking liquidity, or how much of the activity reflects profitable, repeatable trading versus manufactured turnover. When the ratio of 24-hour volume to open interest or to displayed resting liquidity is unusually high, it suggests rapid turnover or recurrent swapping of positions rather than sustained depth in the market.

Polymarket's international exchange displays a recurring tendency for heavier trading on contracts with low implied odds, including markets related to elections and sporting events. Examples include outsized dollar volume on very low-probability outcomes for contests such as the FIFA World Cup and various political contests where the candidate with a near-zero probability nevertheless attracted tens of millions in notional trades. Platform representatives attribute this pattern to the activity of highly active, algorithmic participants—so-called "sharps"—who exploit pricing inefficiencies. They argue that these participants enhance market efficiency by correcting mispricings and bringing market prices into alignment with fundamentals.

On Kalshi, scrutiny centered on its perpetual futures contract for ether, where a disproportionate share of trades clustered at approximately the same dollar size over a 24-hour period. Analysts who examined trade-level data noted that a very large percentage of reported volume on a given day came from trades between narrow size bands, and that these trades far outstripped the amount of resting liquidity on the order book. Kalshi has denied that wash trading is occurring and has indicated it tracked hundreds of distinct users who participated in the flagged trades. The company contends that many trades reflected arbitrage against slightly stale quotes or market-maker pricing, and that incentives such as fee waivers were designed to bootstrap liquidity in a nascent product.

Independent experts are divided on interpretation. Several said the trade patterns are more consistent with opportunistic arbitrage and aggressive liquidity taking than with outright collusion. Others cautioned that certain design choices—fee waivers, rebates, and other incentives—can unintentionally create an environment where aggressive liquidity takers and market makers effectively recycle liquidity to harvest incentives, producing high reported turnover without deep, robust order book depth. As one academic noted, even absent explicit wash trading, a structure that funnels incentives to aggressive traders can produce volume that is not reflective of sustainable market demand.

Regulatory and valuation implications are central to the debate. Both platforms have used rising volumes as a signal of growth in private fundraising and to justify elevated private-market valuations. Polymarket has been reported to be raising at valuations above $20 billion after launching a U.S. exchange, and Kalshi has been reported to be in fundraising discussions valuing it around $40 billion following its perpetual futures debut. If a material portion of reported perpetual volumes is manufactured or stems from short-lived incentive-driven flows, then headline volume and growth trajectories could overstate the underlying trading demand on which valuations and prospective public-market offerings would rest.

Market surveillance and compliance obligations are also highlighted. A designated contract market in the U.S. carries an explicit duty to monitor price movements, depth and volumes for anomalies and to guard market integrity. Observers argue that exchanges should have robust, real-time surveillance and the ability to identify and investigate clustered trade sizes, repetitive self-crossing, or patterns suggesting coordinated turnover. Regulators, too, may scrutinize suspicious patterns; reports indicated the CFTC has examined certain trades on Kalshi's ether perpetual contract, though such inquiries are often confidential and not publicly confirmed.

Both companies have responded defensively. Polymarket executives have explained the international exchange's patterns as a byproduct of a more algorithmically sophisticated participant base outside the U.S., while asserting that their U.S. exchange shows different, more retail-driven patterns. Kalshi rebutted wash trading allegations, pointed to broad user participation in the flagged trades, and emphasized structural differences such as lower leverage limits and U.S. regulatory constraints that affect order-book behavior. Nevertheless, outside commentators emphasize that structural differences alone do not eliminate the need to reassess incentive frameworks that may generate churn.

Historical analysis adds context. Prior academic work examining prediction-market trading has previously identified patterns consistent with wash trading or self-dealing on similar platforms, and while surveillance improvements and fee changes can reduce such patterns, residual oddities—particularly pronounced interest in low-probability outcomes—have persisted. Speculation surrounding potential token airdrops on blockchain-based platforms has also been offered as a partial explanation for concentrated activity, since distribution heuristics that reward high contributors could encourage users to concentrate trading to qualify for rewards, if such rewards are perceived as likely.

Ultimately, a careful, evidence-based approach is required. Distinguishing healthy arbitrage and liquidity-provision activity from manipulative or incentive-driven turnover requires detailed order-level data, an examination of counterparties, awareness of fee and rebate structures, and vigilant surveillance by both platforms and regulators. Transparency in reporting methodologies, third-party audits of reported volume figures, and clear disclosure of incentive programs would help market participants, investors and potential public-market investors better assess the sustainability of reported growth metrics.

Key Insights Table

Afterwards...

Moving forward, technological and regulatory tools can improve the ability to detect and deter misleading activity. Exchanges should consider implementing more granular, auditable volume reporting, including breakdowns by trade size, counterparty anonymized identifiers, and the ratio of taker-to-maker volume. Independent third-party analytics and periodic audits could validate headline metrics and strengthen investor confidence. Advances in on-chain analytics for blockchain-native markets can also help trace patterns of participation and flag suspiciously repetitive flows.

Regulators and market operators should collaborate to develop clear standards for measuring and reporting liquidity and turnover in novel products such as perpetual futures on digital assets and prediction markets. Machine-learning surveillance systems can be trained to detect clustering, repeated self-matching, and other signatures that deviate from expected market behavior. At the same time, careful policy design is needed so that incentives intended to seed liquidity do not unintentionally reward churn that produces misleading volume.

Greater transparency, robust surveillance, and constructive regulatory engagement are the most promising paths to ensuring that volume metrics reflect genuine market demand and that growing exchanges can scale with credibility.