28 Wishes, an ice cream shop in Los Angeles, experiences a decline in sales of about 20% when temperatures drop below 70°F (21°C). Since April, the shop’s owners have invested approximately $20 daily in Kalshi weather contracts, resulting in profits reaching as high as $1,500 monthly.
While these contracts can provide payouts to help counteract lost sales on colder days, they come with limitations. The payouts are determined by temperatures recorded at a specific weather station, which may not reflect the actual sales fluctuations at the shop. As a result, the hedge could yield profits while the shop remains busy or fail to pay out during a slow sales period due to colder weather. This risk misalignment is referred to as basis risk. To mitigate this, businesses must carefully select a weather station, temperature threshold, and timing that align well with their revenue impacts.
Prediction markets enable thousands of events to be traded, but not all tradeable contracts operate as effective commercial hedges.
28 Wishes employs prediction markets to alleviate some revenue losses incurred on chilly days.
Indemnity insurance covers specific losses, while parametric insurance is settled based on an agreed trigger. Traditional indemnity policies require a customer to have an insurable interest, unlike event-contract traders, who don’t need to experience any actual loss. Businesses can hedge against lost revenue by trading with weather forecasters, sports analysts, market makers, and casual investors. This collective risk appetite enhances liquidity, which often remains elusive in situations requiring tailored underwriting.
Market exchanges supply the necessary infrastructure for trading but do not provide the exposure analysis, structuring, or distribution essential for transforming contracts into effective hedges. When natural liquidity is scarce, specialized capital providers must step in to price and hold the associated risk, managing interconnected exposures across multiple clients.
Kalshi reports over 8,000 live markets, while $27 billion in trades were reported on its platform during the 2026 World Cup. The key challenge lies in determining whether this infrastructure can consistently facilitate business hedging.
For instance, a bar in New York expects to lose $50,000 in profit if the Knicks fail to reach the conference finals. By purchasing 62,500 ‘Knicks miss’ contracts at 20 cents each—totaling $12,500—they stand to gain $62,500 if the Knicks are eliminated, reflecting a net profit of $50,000 before fees. However, if the Knicks proceed, these contracts would expire worthless, costing the bar $12,500.
This strategy hinges on the reliability of the $50,000 loss estimate. The owner must assess the risk related to home games and their contributions, along with management commitments for staffing and supplies, and adjust coverage as the series progresses. Few bar owners are likely to navigate this process without assistance as market conditions fluctuate. While some may employ AI tools for analysis and adjustments, others might seek advice from domain experts. In either case, regulated distributors can facilitate compliant transactions.
The volume of past trades might not accurately reflect a business's hedging capacity. In June, Susquehanna’s Jeremy Maletz indicated that the firm could quote tens of millions in risk contracts while only having around $100,000 in prior trading. This is feasible if market dynamics allow Susquehanna to confidently price the risk. Consequently, specialized market makers can often provide significantly more capacity than past trading activity would suggest.
However, this capacity must be managed effectively across clients. For example, if ten different bars hedge against the same Knicks outcome, it results in a concentrated exposure for the provider. The same holds true when businesses hedge against identical weather events, elections, or policy changes. This demands aggregation of positions, correlation assessments, limit setting, and weighing when to hedge or scale back exposure.
Capital constraints are another consideration. A standard Kalshi event-contract position typically requires collateral equal to its maximum potential loss. If a market maker purchases 100 million ‘No’ contracts at 99 cents while ‘Yes’ contracts are priced at one cent, they must commit $99 million. If the ‘No’ position succeeds, the contracts settle at $100 million, yielding a $1 million profit before fees. Conversely, if ‘Yes’ prevails, the market maker incurs a loss of $99 million.
These capital commitments remain tied up until settlement. Holding a long-dated position can lock away substantial funds for comparatively modest returns.
Commercial users also require reliability in the settlement process. In April 2026, unexpected temperature fluctuations at Paris Charles de Gaulle Airport led to profitable positions in Polymarket. Météo-France subsequently filed a police complaint about potential interference with its data systems after investigating the incident.
For businesses, the sources of settlement data, how corrections are handled, availability of fallback data, and dispute resolution procedures all play critical roles in the effectiveness of a hedge. A July advisory from the CFTC emphasized that registered exchanges should identify their settlement sources before listing contracts, ensuring they are reliable, objective, and resistant to manipulation.
Regulatory environments will shape global market scaling. The same hedge may be available in one jurisdiction while restricted elsewhere, thus creating a demand for distributors who can navigate varying regulatory terrains and secure compliant access.
The early commercial applications that appear most viable involve short-term, data-rich risks with objective settlement mechanisms and consistent occurrences. Catered solutions for catastrophe and other long-term risks necessitate custom analysis and tie up capital for extended durations.
The intermediary role is expected to be increasingly software-driven rather than purely advisory. A digital platform could analyze a business’s operational data to estimate exposure, pinpoint appropriate contracts, suggest hedge sizes, and execute trades within established limits. Companies that merge proprietary data with direct exchange links are likely to achieve greater efficiencies than traditional advisory services.
Waterhouse VC is currently collaborating with White Swan Data on a prediction-market strategy aiming to enhance liquidity on regulated exchanges, with a primary focus on sports. While crucial elements such as pricing, portfolio structuring, collateral management, and execution are applicable across domains, the specific data needs and pricing advantages may not be interchangeable. As commercial demand evolves, specialized teams are anticipated to emerge around distinct risk categories.
