Unusual Trading Patterns on Kalshi and Polymarket Raise Questions About Volume Integrity Amid Rapid Growth
Table of Contents
You might want to know
Why are trading volumes on certain products at Kalshi and Polymarket showing unusual patterns that concern observers?
How could those volume patterns affect the exchanges' valuations and the confidence of future public-market investors?
Main Topic
Trading activity on some products of prediction market platforms Kalshi and Polymarket has prompted scrutiny from market observers who question whether reported volumes reflect genuine economic activity. On Polymarket's international exchange, which operates outside U.S. regulatory oversight, multiple-contract markets have exhibited a pattern in which contracts with low implied probabilities attract disproportionately large volumes compared with higher-probability contracts. Observers have seen this behaviour across election-related markets, sports betting markets, and central bank decision markets.
Separately, users flagged an anomaly on Kalshi's ether perpetual futures market: a concentrated number of trades around the same dollar amount (roughly $5,500), which accounted for a substantial portion of transaction dollars despite limited resting liquidity. A detailed review found that, on one examined date, nearly half of the dollar volume on Kalshi's ether perpetuals came from trades sized within a narrow band of around $5,495 to $5,505.
These patterns raised concerns for two reasons. First, they could indicate inflated reported volumes that do not correspond to meaningful open interest or genuine liquidity. Second, the patterns might suggest wash trading, a manipulative practice in which parties trade with themselves or collude to create the appearance of activity. Wash trading can distort price discovery, mislead market participants, and inflate metrics that companies cite to demonstrate traction or justify valuations.
Both Kalshi and Polymarket have publicly denied that wash trading or inorganic activity is driving the patterns. Each asserts that the observed behaviour is the result of legitimate trading strategies and active participants. Polymarket's head of revenue and analytics described a cohort of highly active traders, often called "sharps," who use sophisticated tools and algorithms to exploit small mispricings, particularly in low-odds contracts on the international exchange. Kalshi pointed to hundreds of distinct users engaged in the flagged trades and suggested that arbitrage and rapidly changing spot prices across venues, rather than collusion, explain concentrated trade sizing.
Regulatory and academic perspectives complicate the picture. A designated contract market, the classification that applies to some prediction exchanges, has obligations to monitor market integrity and to investigate suspicious price movements and volumes. Industry lawyers and compliance experts emphasize that exchanges should be surveilling real-time abnormalities and addressing them. Independent academics and market-structure researchers warned that when a substantial share of reported volume is manufactured or driven by incentive mechanisms, headline volume metrics can materially overstate underlying demand — an important distinction for retail investors and for valuation assessments if these firms pursue public listings.
Valuation context matters: Polymarket has been reported to be raising capital at a valuation above $20 billion after launching a U.S. exchange, while Kalshi has been linked to fundraising discussions valuing it at around $40 billion following the debut of its perpetual futures product. Both companies have highlighted surging trading volumes as evidence of rapid adoption and product-market fit. If volume metrics are later shown to include a large amount of incentivized or mechanically generated activity, the foundations supporting those valuation narratives could be called into question.
Specific market examples illustrate the anomalies. On Polymarket international, contracts tied to events as varied as the 2026 FIFA World Cup and foreign political outcomes showed outsized trading on extremely low-probability options. For example, more dollar volume was recorded on some single-digit-or-sub-1% chance outcomes than on higher-probability outcomes, producing counterintuitive rankings of traded volume versus implied likelihood. In one notable case, a contract related to an Ethiopian election displayed more than $50 million traded on a candidate whose probability stayed below 3%, while the declared winner had only a small fraction of that volume.
On Kalshi, persistent divergence between 24-hour traded volume and open interest on perpetual futures raised concern. For some contracts, daily volume was tens of times greater than open interest, a ratio that experts said diverges from typical perpetual futures markets. Critics argued that fee waivers, rebates, and incentives intended to attract early liquidity providers may be producing cyclical churn rather than durable liquidity, creating an appearance of activity that primarily serves to capture incentive payments rather than to serve end users.
Both firms contend that incentives and fee structures are necessary to bootstrap liquidity for novel products, especially in a regulated U.S. environment with different capital and operating constraints than offshore venues. Kalshi emphasized differences such as lower leverage limits and stricter market-making rules relative to offshore competitors as reasons for observed differences in metrics when comparing across platforms. Proponents of incentives argue such measures are common in new markets to attract makers and takers until organic participation grows.
Nevertheless, several market-structure experts said the observed patterns merit closer attention. They suggested that surveillance, clearer fee structures, and transparency around trading counterparties and algorithmic activity could help clarify whether reported volumes reflect genuine economic demand. Some observers also pointed to past academic studies that found indicia of manipulative behaviour on similar platforms, although methodologies and conclusions have varied across studies and periods.
Regulatory interest has been reported. News outlets indicated that the Commodity Futures Trading Commission (CFTC) was examining certain trades on Kalshi's ether perpetual contract; the agency does not typically comment on investigations. The CFTC's public statements highlight a low tolerance for manipulative trading practices and a recognition that new market types can create unexpected avenues for fraud or abuse if not properly monitored.
Platform responses have included pointing to surveillance tools, user counts, and procedural safeguards. Polymarket said a larger share of sophisticated algorithmic traders on its international venue helps explain concentrated activity in low-odds contracts, while noting product differences between international and U.S. exchanges. Kalshi said it monitors for self-trading and collusion and attributed some trading patterns to arbitrage opportunities that arise from asynchronous pricing across venues. Both platforms have maintained that activity reflects legitimate trading.
In sum, the debate centers on whether unusual trading patterns represent advanced legitimate strategies by algorithmic traders exploiting small pricing inefficiencies or whether they reflect artificially generated volume driven by incentives, fee structures, or collusive behaviour. The answer carries implications for market integrity, investor protection, platform valuations, and the readiness of these firms for public markets.
This key insight significantly impacts the understanding of reported trading volume: headline volume alone can be a misleading indicator of market health if a substantial portion of that volume is transient, incentive-driven, or mechanically produced rather than reflecting persistent, independent demand.
Key Insights Table
| Aspect | Description |
|---|---|
| Observed Volume Patterns | Disproportionate trading on low-probability contracts and clustering of trade sizes on specific Kalshi perpetuals. |
| Potential Explanations | Algorithmic "sharps" arbitraging mispricings, incentive-driven churn, or, in some scenarios, wash trading. |
| Platform Responses | Both companies deny manipulation, cite surveillance and legitimate trading incentives, and point to differing product structures. |
| Regulatory/Academic Concern | Researchers and regulators stress the need for monitoring, transparency, and caution in using raw volume as a valuation metric. |
| Valuation Implication | If volumes are materially manufactured, reported growth metrics could overstate underlying demand and affect investor decisions at IPO. |
Afterwards...
Going forward, the industry and regulators should prioritize robust surveillance tools, transparent reporting standards, and research into algorithmic trading behaviour on prediction markets. Advances in on-chain analytics for blockchain-native venues, combined with real-time exchange surveillance and standardized metrics that compare volume to open interest and resting liquidity, would help distinguish organic liquidity from incentive-driven churn.
Policymakers and exchanges could also explore clearer disclosures about incentive programs, fee waivers, and rebate schemes so that market participants and investors can better assess the quality of reported activity. Academic collaboration to produce reproducible methodologies for identifying manipulative signatures would strengthen public understanding and enable evidence-based regulation.
In short, improving transparency, aligning surveillance practices with evolving market structures, and investing in analytic tools that parse genuine demand from mechanically produced volume are important next steps to ensure prediction markets mature in a way that preserves market integrity and investor trust.
Last edited at：2026/10/1