The Los Angeles ice-cream shop, 28 Wishes, experiences approximately a 20% dip in sales whenever temperatures fall below 70°F (21°C). Since April, the shop's owners have invested around $20 daily in Kalshi weather contracts, reporting monthly profits that can reach up to $1,500.
While these contracts can help balance out income lost on particularly chilly days, they are not a foolproof solution. The payout is based on temperature readings from a designated weather station, not directly tied to the shop’s actual sales figures. As a result, there are scenarios where the contract could pay out even if customer turnout remains strong or might not pay when cold spells deter customers.
This discrepancy is known as basis risk. To minimize it, businesses must carefully select a weather station, set an appropriate temperature threshold, and define the time period that accurately reflects its sales conditions.
Prediction markets have expanded the number of tradeable events significantly, yet not all tradeable contracts serve as effective commercial hedges. 28 Wishes utilizes these markets to mitigate the income losses associated with declining temperatures.
On the topic of risk pooling, indemnity insurance covers specific losses, while parametric insurance settles upon reaching a predetermined trigger, still requiring the customer to have an insurable interest.
In prediction markets, traders need not face corresponding losses. Businesses hedging against revenue declines are able to trade with a variety of participants, including weather forecasters, sports analysts, market makers, and recreational traders. This wide range of risk appetites contributes to the liquidity necessary for exposures that would typically require custom underwriting.
The exchanges provide the necessary market infrastructure, although they lack the exposure analysis and structuring needed to transform contracts into effective business hedges. In cases where liquidity is sparse, specialist capital providers must assess and manage the risks while balancing correlated exposures among different clients.
Kalshi claims to offer over 8,000 live markets, with Reuters indicating that $27 billion in trading occurred on its platform during the 2026 World Cup. The real test lies in whether this infrastructure can reliably facilitate business hedging on a repeat basis.
For example, imagine a bar in New York that predicts a $50,000 profit loss should the Knicks fail to reach the conference finals. Purchasing 62,500 ‘Knicks miss’ contracts at a cost of 20 cents each would total $12,500. If the Knicks do not advance, these contracts would yield a payout of $62,500, netting the bar a $50,000 profit after expenses. Conversely, if the team succeeds, the contracts expire worthless, and the bar incurs the $12,500 expense.
Such strategies hinge on the accuracy of the bar owner's projections regarding at-risk home games and their financial impact, in addition to accounting for previously incurred costs. Adjustments may be required as the playoff series progresses. Few bar owners will navigate this alone; some may employ AI tools to monitor and adjust exposure levels, while others may consult domain experts. Regulated distributors can help facilitate compliant executions.
When considering capacity supply, prior trading volume may not accurately represent how much risk a business can hedge. For instance, Jeremy Maletz from Susquehanna noted that the firm could quote tens of millions in risk on a contract based on only about $100,000 in historical trading, provided that market price and internal validation supported it. Market makers can thus offer significantly more capacity than previous turnover data would indicate.
This capacity must be coordinated across clients. When multiple bars hedge against the same Knicks outcome, it creates a concentrated risk for the provider. Similar challenges arise when businesses attempt to hedge against the same storm, election, or policy issue. Providers must aggregate positions, evaluate correlations, establish limits, and determine the appropriateness of hedging or reducing exposure.
Capital plays a crucial role as well. Generally, an event contract with Kalshi requires collateral that reflects its potential loss. If a market maker purchases 100 million 'No' contracts for 99 cents while 'Yes' is priced at one cent, the market maker must allocate $99 million in capital. If 'No' wins, the contracts would pay out $100 million, ensuring a profit of $1 million before fees; if 'Yes' prevails, the market maker faces a $99 million loss.
This capital is immobilized until settlement. Hence, a long-term position could indefinitely tie up significant amounts of capital for relatively minor returns.
Additionally, commercial entities need assurance regarding settlement processes. In April 2026, significant temperature fluctuations at Paris Charles de Gaulle Airport impacted profitable positions on Polymarket. Following an investigation, Météo-France lodged a police report over allegations of interference with their automated data systems.
For businesses, the source of settlement, how corrections are handled, fallback data, and dispute resolution protocols all influence a hedge's effectiveness. In July, the CFTC staff issued an advisory urging registered exchanges to specify settlements prior to listing contracts, assessing their dependability, neutrality, and resistance to manipulation.
Regulatory frameworks will influence the global scalability of these markets. A hedge may be available in one jurisdiction while restricted in another, underscoring the need for distributors that can navigate diverse regulatory landscapes and ensure compliant access.
Initial commercial applications are expected to thrive primarily in short-duration, data-abundant scenarios featuring objective settlement mechanics and sufficient repetition for standardization. Conversely, catastrophe-related and long-tail risks demand tailored analysis and a longer capital commitment.
The intermediary role is expected to lean towards software solutions rather than merely advisory services. A platform could leverage a business's operational data to estimate exposure levels, suggest fitting contracts, recommend hedge sizes, and execute trades within set limits. Firms that merge proprietary data with direct exchange access may evolve quicker than traditional advisory groups.
Waterhouse VC is collaborating with White Swan Data on a prediction-market strategy aimed at enhancing liquidity on regulated exchanges, with an initial emphasis on sports betting. While the principles of pricing, portfolio management, collateral oversight, and trade execution will apply broadly, the specific data and pricing advantages will not automatically translate across different risk types. Thus, specialized teams are anticipated to emerge as market demand grows across various risk sectors.
