Survey: 79% of Prediction Market Users Lost Money, Half Borrowed to Fund Bets
A BadCredit.org survey of prediction market users found that 79% lost money over the past year, with 27% losing more than $500. The survey also found that 51% of users borrowed money — via credit card, personal loan, or other borrowing — to fund their trades, and 88% of those who borrowed lost money. Over half (53%) said they began using prediction markets to earn extra income or because they were struggling financially. The findings, widely reported in early October, provide consumer-harm evidence that contrasts with the platforms' investment-tool framing and may be cited by state regulators pursuing enforcement.
A survey of prediction market users conducted by BadCredit.org found that 79% of respondents lost money over the past year, with more than one quarter (27%) losing more than $500. The survey's most striking findings concerned borrowing: 51% of users said they used a credit card, personal loan, or other borrowed money to fund their prediction market trades, and of those who borrowed, 88% lost money. The survey also found that 53% of users said they started using prediction markets as a way to earn extra income or because they were struggling financially — a motivation profile more consistent with problem gambling than with the sophisticated hedging and forecasting use cases that platforms emphasize in their regulatory arguments. The findings were widely reported across US media outlets in early October 2026.
The survey data sits in tension with a Pew Research Center finding from September that most prediction market sports traders break even or post small losses based on an analysis of nearly 12,000 trading accounts. The two findings are not necessarily contradictory — they measure different populations through different methods. Pew analyzed actual account-level trading data, which captures all activity including small, frequent traders who may net close to zero; the BadCredit.org survey asked users to self-report their outcomes, which may over-represent users who feel their losses acutely and under-represent casual break-even participants. But the divergence matters for the policy debate: Pew's account-level data supports the platforms' argument that the loss profile is modest, while the BadCredit.org survey supports the state regulators' argument that prediction markets cause meaningful consumer financial harm, particularly through borrowing-to-bet behavior.
The borrowing findings are the most consequential from a consumer-protection standpoint. A product where half of users borrow money to participate, and where 88% of those borrowers lose, has a harm profile that regulators treat seriously. The practice of borrowing to gamble is a recognized marker of problem gambling, and licensed gambling operators in most US states and in regulated jurisdictions internationally are subject to responsible-gambling requirements designed to identify and intervene in exactly this behavior: deposit limits, affordability checks, self-exclusion tools, and restrictions on credit-card funding. CFTC-regulated prediction market platforms are not currently subject to the same responsible-gambling framework, because the CFTC regulates them as commodity exchanges rather than as gambling operators. The survey data gives state regulators a concrete consumer-harm argument: if prediction markets produce gambling-like harm (borrowing, losses, financial distress motivation), they argue the platforms should be subject to gambling-style consumer protections — which only applies if the platforms are regulated as gambling, which is the precise question in the ongoing litigation.
For the broader regulatory debate, the survey is a data point that both sides can use selectively. The platforms will note that self-reported survey data is less reliable than account-level analysis, that the survey population may not be representative, and that the existence of losing traders is inherent to any market (for every loss there is a corresponding gain, and sophisticated traders profit from less sophisticated ones). State regulators will note that the borrowing-to-bet rate and the financial-distress motivation profile are consistent with gambling harm rather than investment activity, and that the absence of responsible-gambling protections on CFTC-regulated platforms leaves consumers unprotected. The survey does not change the legal question — the CFTC preemption argument does not turn on whether users profit or lose — but it adds to the political and public-interest case that state AGs are building, and it is the kind of evidence that could influence how the CFTC itself approaches responsible-trading requirements as the sector institutionalizes.
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Recent updates
Google Adds Prediction Market Ad Policy — CFTC License Required, Ads Blocked in Five States
Google published a dedicated prediction markets section in its advertising policy on October 15, requiring domestic prediction market operators to hold a CFTC Designated Contract Market license to run ads — a requirement that favors Kalshi and Polymarket US over unlicensed rivals. Google also bars prediction market ads targeting five states where enforcement is most active: Connecticut (added recently), Michigan and New York (July), Ohio (June), and Nevada (earlier in the year). The policy makes Google's advertising platform mirror the state-by-state enforcement map, using commercial access as a de facto regulatory layer.
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Kalshi announced a partnership with Signify Group on October 15 to monitor social media for abuse and threats directed at athletes, coaches, and sports personnel across the NFL, NBA, MLB, NHL, and ATP Tour. Kalshi notified the CFTC of a new rule, dated October 13, that codifies athlete harassment as an 'impermissible activity' on its platform, with sanctions up to financial penalties and permanent account closure. The move follows a September appeal by five major leagues and their players' unions for lifetime bans on abusive bettors, and positions Kalshi ahead of the integrity concerns that sports leagues and regulators have raised about prediction market sports contracts.
CFTC Moves to Classify Event Contracts as Swaps — Attempting to Settle the Core SCOTUS Question by Rule
The CFTC, led by Chairman Michael Selig, moved on October 9 to explicitly classify event contracts as swaps under the Commodity Exchange Act, which would place prediction market contracts squarely under federal swap regulation. The scope covers sports, political, cultural, and weather contracts, while a separate provision excludes casino-style gambling and sportsbook wagers. Selig said the aim is to remove 'any ambiguity' about these contracts. The action targets the exact question dividing the federal circuits and now before the Supreme Court — whether event contracts are 'swaps' — and represents an attempt by the federal regulator to settle that question by rule while the courts remain split.