Kalshi is moving to put more safeguards around its youngest adult customers just as academic research intensifies questions about how losses are distributed across prediction markets. The exchange announced a default $2,500 monthly deposit limit for users aged 18 to 20 on October 27, alongside warnings for traders who concentrate too much of their account balance in one position.
Chief Executive Tarek Mansour has repeatedly called for industry-wide rules on responsible trading, but the immediate product changes were announced by co-founder and chief operating officer Luana Lopes Lara. They arrive against a disputed estimate that the taker side of Kalshi trades lost $583.5 million between the platform’s 2021 launch and May 2026, and separate academic findings that show persistent disadvantages for some participants buying low-priced event contracts.
Those studies do not prove that the same amount was lost by identifiable retail customers, nor has Kalshi acknowledged the Roosevelt Institute’s estimate as an accurate measure of casual users’ performance. Yet the underlying debate raises a question relevant to investors, regulators and customers: how effective can voluntary risk tools be when a marketplace’s participants vary sharply in experience, price sensitivity and access to data?
What Changed in Kalshi’s October 27 Customer Protections?
In its October 27 announcement, Kalshi said customers aged 18 to 20 will automatically enter a special account category with additional safeguards and educational materials. Their accounts will begin with a $2,500 monthly deposit limit. The changes are being rolled out over the following days, rather than necessarily appearing in every eligible account immediately.
The $2,500 amount is a default setting, not an absolute statutory ceiling. Additional reporting indicates that users may modify or opt out of the setting, subject to extra disclosures and cooling-off procedures. Presenting the figure as a hard maximum on total trading losses would be misleading, especially because the announcement concerns deposits rather than the gross notional amount a customer can trade.
Kalshi also says a new trader with a balance greater than $1,000 will see an alert if more than 25% of that balance is committed to a single trade. For example, a trader with $1,200 who tries to place $360 into one contract would be allocating 30% and trigger the warning. A warning encourages reconsideration; it is not described as an automatic rejection of the order.
The company is partnering with Parent ProTech and ConnectSafely to publish guides for families. It also points to previously available trading breaks, optional spending limits, self-exclusion, age verification and an Inner Circle feature that can send designated relatives or trusted contacts activity alerts.
Kalshi prohibits customers under 18. The new safeguards target adults aged 18 to 20, a group that can access the federally regulated exchange even though conventional U.S. sports-betting platforms in many states require customers to be at least 21. The age distinction is part of a broader legal and consumer-protection debate, not evidence that Kalshi is admitting it operated unlawfully.
What Does the $583.5 Million Loss Study Actually Measure?
A July 7 Roosevelt Institute analysis by Brad Lipton and Toyosi Odusola estimated $583.5 million in cumulative losses attributable to the taker side of trades on Kalshi from July 2021 through May 15, 2026. It attributed approximately $371.6 million to sports contracts, or about 64% of that estimated total.
The analysis used transaction data previously accessible through Dune. It treated the person or entity accepting an existing order as a proxy for a relatively unsophisticated customer, and the side placing a resting order as a proxy for a more professional liquidity provider.
That assumption matters as much as the headline number. In an order book, a market maker typically provides liquidity through resting quotes and a taker executes against an available quote. But a casual user can place a resting limit order, and a professional trading firm can take liquidity. Maker and taker are descriptions of trade execution, not verified classifications of customer identity.
In a July 10 rebuttal, Kalshi challenged the study on precisely those grounds. It argued that treating every taker transaction as an ordinary person’s loss and every maker transaction as a professional trader’s gain misclassifies both groups. The company also objected to comparisons implying that an exchange necessarily operates like a sportsbook taking the other side of every customer’s position.
The Roosevelt figure is therefore a disputed estimate produced through one classification method, not an audited figure for verified retail-account losses. Equally, the existence of a classification problem does not establish that ordinary customers are uniformly profitable. Kalshi itself has acknowledged that its platform has more net-losing users than winning users, while disagreeing about what that means for consumer welfare.
The study also reported that 63.2% of taker-side proceeds flowed through just 6.3% of matched orders with a dollar value of at least $200. This measures concentration by order size within that dataset; it does not mean 6.3% of people captured 63.2% of all profits.
Why Do Other Researchers Find a Disadvantage for Longshot Trades?
Separate academic work reaches a related conclusion without requiring every maker to be labeled a professional or every taker a retail customer. The 2026 CESifo working paper Makers and Takers: The Economics of the Kalshi Prediction Market, by Constantin Bürgi, Wanying Deng and Karl Whelan, examines transaction-level prices across more than 300,000 contracts.
The authors find that very low-priced contracts tend to win less frequently than their prices imply, while more expensive contracts tend to perform better. This is a form of favorite-longshot bias, a familiar pattern in betting and other markets where participants can overpay for a small chance of a large payout.
The mathematics is simple. A hypothetical event contract costing ten cents and paying one dollar if correct needs to win one time in ten to break even before fees. If equivalent contracts actually win only 8% of the time, the expected gross payout is eight cents for a ten-cent cost, producing an illustrative expected loss of 20% on money staked. That is a worked example, not a measured return for a specific Kalshi product.
The researchers also find differences between the returns earned by traders who post prices and those who accept them. They describe an informational and price-selection gap that can coexist with useful aggregate forecasting. A market may produce reasonably accurate probabilities while many participants pay unfavorable execution prices.
Another working paper, The Microstructure of Wealth Transfer in Prediction Markets, studied 72.1 million Kalshi transactions from June 2021 through November 2025. Its abstract reports mean excess returns of minus 1.12% per trade for liquidity takers and plus 1.12% for makers in the sample. These are trade-level findings under the paper’s definitions, not a calculation of each customer’s lifetime investment return.
The studies use different samples, definitions and observation windows. They cannot be added together to create a new estimate of total consumer losses.
Can an Exchange Business Model Resolve the Consumer-Risk Question?
Mansour’s argument is that Kalshi operates as a marketplace matching opposing participants and collecting fees, rather than a traditional sportsbook whose own balance sheet directly benefits when a customer loses. He has argued that this creates stronger incentives to welcome informed traders and that responsible trading rules should apply across the industry.
That structural distinction is real, but it does not settle how users experience loss. A platform can earn fees without being the counterparty to customers’ losing positions, while some participants still repeatedly pay more than the expected value of a contract. Whether its incentives are sufficiently protective must be judged from its actual products and results, rather than the exchange-versus-sportsbook label alone.
Kalshi’s October 21 Reuters interview with Mansour explored the claimed social value of prediction markets, including information aggregation and hedging. Critics question whether expanding markets around sports and entertainment primarily improves forecasting or encourages gambling-like behavior. These are competing interpretations of the same evolving market structure.
The independent research also shows why a deposit limit is only one measure of risk. It restricts how much new money reaches an account under the default setting, but it does not directly restrict how frequently a user trades, the prices accepted, the transaction fees paid or the probability of suffering a large loss on an individual contract.
Kalshi’s response to analysis of repetitive trades in a different product offers another reminder that market microstructure needs precise interpretation. In DaveFinances’ reporting on Kalshi’s crypto perpetual-futures activity, the company said a concentration of repeated order sizes reflected liquidity provision, not proof of wash trading. The issue was distinct from consumer-loss research, but both cases illustrate how order-level data can be overinterpreted.
Will the New Guardrails Satisfy Regulators and Customers?
The October 27 changes come amid a contested division of regulatory authority. Kalshi operates a market regulated by the Commodity Futures Trading Commission, while several states contend that certain sports event contracts also fall within their gambling laws. Some lawmakers have separately called for tighter age requirements.
Those disputes are not hypothetical. A federal appellate decision concerning Nevada sports contracts underscored disagreement over the reach of state gaming rules, while Utah’s October lawsuit against Kalshi entities and market-maker affiliates extended questions about legal responsibility to participants beyond the exchange operator. Those cases concern jurisdiction and related conduct, not a judicial finding that Roosevelt’s retail-loss estimate is correct.
Investors evaluating prediction-market businesses should separate regulatory compliance from demonstrated consumer protection. A federally supervised exchange can face conduct questions about disclosure, trader suitability, incentives and the effectiveness of controls that identify excessive risk taking.
Kalshi has not publicly demonstrated that the new $2,500 default reduces loss rates, nor has it supplied a before-and-after study showing fewer compulsive trading episodes or improved financial results for customers aged 18 to 20. These are testable outcomes as the policy rolls out, not assumptions that follow from a product announcement.
A useful evaluation would track how many accounts retain the default, how frequently users change it, how often concentration warnings lead to smaller orders, how many sessions trigger trading blocks and whether risky patterns decline relative to comparable older users. Such figures could also help distinguish a precautionary feature from one that materially changes behavior.
Kalshi’s latest move establishes that young-adult protection is becoming a visible part of competition and regulatory positioning in prediction markets. It does not settle the disputed dollar estimate of ordinary-user losses, nor does it prove that optional limits can eliminate them. The next question is whether transparent outcome data will show that the safeguards meaningfully reduce harm while preserving the legitimate uses of event-contract trading.
Johan Shamshad is a financial markets writer at Dave Finances covering cryptocurrencies, trading platforms, brokers, fintech, financial regulation, and developments across global markets. He previously worked at Gulf News, adding newsroom experience to his coverage of fast-moving financial and digital-asset markets.
His work focuses on identifying market-moving events, company developments, regulatory changes, product launches, and shifts in trading and financial infrastructure.
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