Los Angeles Restaurant Group Hedges $60,000 on Kalshi as Prediction Markets Expand into Business Risk Management

A Los Angeles restaurant group traded $60,000 on Kalshi to hedge against the Dodgers failing to reach the World Series. Kalshi said the unnamed hospitality group operates 25 bars, including several near Dodger Stadium. The Dodgers lost to the Milwaukee Brewers in the NLCS last week. The position returned almost twice the original stake. The proceeds helped replace revenue the business would have received from additional Dodgers playoff games.

Prediction markets are likely to gain wider use as financial hedges, extending the platforms' role beyond sports betting into financial management and business decisions. Joshua Mitts, a Columbia University law professor who studies prediction markets and securities law, described the restaurant group's trade as functionally similar to customized insurance. Mitts said businesses might participate in similar markets to cover other risks if suitable markets were available.

Pew Research Center data show that trading volume on prediction markets doubled between May and July of this year, driven primarily by sports. The data put trading volume above $58 billion on Kalshi and nearly $22 billion on Polymarket. Prediction markets attract some users through looser regulations and fewer restrictions because the platforms are classified as financial exchanges rather than gambling platforms. Small businesses seeking unconventional risk management tools appear to be among the platforms' growing user base.

In June, New York City bar The Jeffrey promised to cover each patron's tab up to $100 if the Knicks won an NBA finals game. The Jeffrey placed a $5,000 bet on Kalshi that the then-underdog Knicks would win. A $13,500 payout allowed The Jeffrey to cover patrons' tabs and make an $8,500 profit. Last month, an unnamed third-party insurance company placed five Kalshi trades that could pay $3 million if Louisiana State University wins this season's national championship. The potential payout matches the bonuses Louisiana State University's football program would owe coach Lane Kiffin. Earlier this summer, a Los Angeles ice cream shop reported earning nearly half the shop's monthly rent, up to $1,500, by betting on cold fronts to hedge against weak demand.

Independent financial economist Lauris Zminsky launched the Blanket app in partnership with Kalshi in August. Blanket uses artificial intelligence to suggest specific yes-or-no Kalshi markets that could offset risks for individual businesses. Zminsky described Blanket as a reasoning and discovery tool that directs users toward Kalshi. Zminsky said the idea was to build a self-service tool for small-business owners that could also help Kalshi scale Kalshi's S&P insurance hedging initiative.

Dartmouth College economics professor Eric Zitzewitz said prediction markets suit risk management because the platforms incorporate large samples, particularly for sports-related financial hedging. Zitzewitz said abundant data, repeated events and frequent games can make sports markets well calibrated. Zitzewitz said users pay fees for the insurance-like protection but should receive fair underlying prices.

Mitts said prediction-market hedges are most effective for yes-or-no outcomes, such as weather events or games. Mitts warned that hedges dependent on access to information could make a business owner a systematic loser if a competitor has better information. Prediction-market hedges are also likely to work best in major metropolitan areas with large trading markets, while still addressing individual small businesses' specific risks.

Zitzewitz said prediction markets' large samples and real-time information gathering can also inform business decisions. Zitzewitz said an e-commerce retailer could use trades on future Amazon credit card receipts to obtain an accurate forecast of consumer spending in the coming month. Zitzewitz said that signal is probably better than GDP growth and is certainly more current.

However, Mitts argued that the same lightly regulated environment attracting users may prevent prediction markets from becoming reliable substitutes for insurance. Insurers have extensive resources to prevent insurance fraud and catch fraudsters. Prediction markets' decentralized structure and evolving regulatory environment mean the platforms lack equivalent resources and safeguards against abuse. Mitts contrasted prediction markets with a small number of state-regulated insurers operating in organized ways.