A Los Angeles ice cream shop, 28 Wishes, has reported a drop in sales by approximately 20% whenever temperatures fall below 70°F (21°C). Since April, the shop's owners have invested about $20 daily in Kalshi weather contracts, resulting in monthly profits that can reach as high as $1,500.
While these contracts can help mitigate financial losses on colder days, they do not guarantee total coverage. The settlement is based on the temperature recorded at a designated weather station, not directly tied to the shop's customer activity. Thus, it riskily compensates when the shop may not experience diminished sales or conversely fails to pay out when colder weather reduces foot traffic.
This type of uncertainty is labeled as basis risk. To minimize this effect, businesses must carefully select the appropriate weather station, the temperature threshold, and the relevant time frame that will closely align with their revenue-producing conditions.
Prediction markets have made it possible to trade thousands of different events; however, simply having a tradable contract does not equate to a practical commercial hedge. 28 Wishes utilizes prediction markets to offset some of the income loss associated with decreasing temperatures.
Insurers typically compensate against verified losses through indemnity insurance. On the other hand, parametric insurance is triggered by pre-established conditions, yet it is still designed for customers possessing an insurable interest. In prediction markets, traders do not need to endure an exact loss; businesses can hedge against income reductions by engaging with weather forecasters, sports analysts, market makers, and casual traders. The aggregated risk appetite of these various participants can provide liquidity for risks that typically require custom underwriting.
Market exchanges deliver the framework necessary for trading, yet they do not offer exposure analysis, contract structuring, or distribution services essential for transforming these contracts into effective business hedges. When natural liquidity is insufficient, specialized capital providers become responsible for pricing, holding risks, and managing correlated exposures among different clients.
Kalshi currently lists over 8,000 active markets, and during the 2026 World Cup, trading on the platform reportedly reached $27 billion. The question remains if this framework can consistently facilitate solid business hedging.
Take, for instance, a bar in New York anticipating a $50,000 profit loss if the Knicks do not reach the conference finals. Purchasing 62,500 ‘Knicks miss’ contracts at 20 cents each amounts to a total investment of $12,500. Should the Knicks be ousted, the contracts would pay out $62,500, yielding a net gain of $50,000 before fees. Conversely, if the Knicks advance, those contracts would expire worthless, costing the owner $12,500.
For the hedge to be effective, the $50,000 estimate must be accurate. The bar owner needs to estimate the number of home games that could be impacted, calculate how much each game contributes to the total, take predetermined staffing and supplies into account, and potentially adjust coverage as the playoff series progresses. Few bar owners will manage such dynamics alone, as changes in pricing and exposure can be challenging to track. Some may opt for AI tools for analysis and adjustments; others might consult experts in the field. Regardless, compliant execution can be organized by licensed distributors.
Volume figures can sometimes underplay a business's ability to hedge effectively. In June, Susquehanna’s Jeremy Maletz remarked that the firm could quote tens of millions in risk for a contract, despite only around $100,000 in previous trading volume. Such capacity relies on the price discovery in the market and confidence in the assessment made by Susquehanna. Hence, specialized market makers can offer significantly greater capacity than prior activity might indicate.
This capacity must also be managed strategically across clientele. For example, a situation where multiple bars hedge against the same Knicks outcome results in a concentrated exposure for the provider. A similar challenge arises when numerous businesses hedge against a single storm, election, or policy alteration. Providers must aggregate these positions, evaluate correlations, implement limits, and determine when to hedge or diminish exposure.
Capital availability is another limiting factor. A single Kalshi event-contract sits collateralized against its maximum possible risk. If a market maker procures 100 million ‘No’ contracts priced at 99 cents while ‘Yes’ is valued at one cent, they must commit $99 million. If the ‘No’ contracts win, they settle at $100 million, netting a $1 million profit before fees; if ‘Yes’ prevails, the market maker incurs a loss of $99 million.
These assets remain tied up until settlement occurs. Thus, holding a long-term position can immobilize substantial capital for limited potential returns.
Settlement confidence for commercial clients is crucial. In April 2026, sudden temperature shifts recorded at Paris Charles de Gaulle Airport led to profitable positions on Polymarket. However, Météo-France filed a police complaint after finding potential interference with automated data processing systems involved in the temperature settlement.
For businesses, elements such as the sources used for settlement, how corrections are managed, fallback data, and dispute protocols significantly affect whether the hedge benefits them as anticipated. A July advisory from the CFTC emphasized that registered exchanges must ascertain reliable settlement sources before contract listing, as well as evaluate their objectivity to prevent manipulation.
Regulatory frameworks will influence the global scalability of these markets. The same hedge might be freely available in one area while facing restrictions elsewhere. This sets the stage for intermediaries that navigate various regulatory environments to facilitate compliant access for businesses.
Early commercial applications seem most credible for quickly expiring, richly data-driven exposures that leverage objective settlement sources with enough regularity to become standardized. Long-tail risks often necessitate tailored analysis and may immobilize resources for extended periods.
An intermediary layer is likely to integrate software solutions rather than relying solely on advisory services. A platform could harness a business’s operational data to evaluate exposure, recommend appropriate hedge sizes, and automate execution within predetermined parameters. Companies that merge proprietary information with direct exchange access are expected to scale operations more effectively than traditional consultancy models.
Waterhouse VC is collaborating with White Swan Data to establish a prediction-market strategy designed to enhance liquidity on regulated exchanges, initially focusing on sports. While pricing, portfolio management, collateral oversight, and execution principles cross over into other domains, the nuances of data collection and pricing efficiency will not necessarily carry over seamlessly. As commercial interest evolves, niche teams specializing in particular risk sectors are anticipated to develop.
