Prediction Markets Have Become an Asset Class. Derivatives Come Next

Opening a small business means accepting risks an owner cannot control. The day's revenue can depend on events outside the business, leaving the owner to absorb a loss even when everything else runs as planned.

Pravesh Mansharamani, co-founder of Totalis, believes prediction markets could help small businesses manage those risks. His team builds trades that combine multiple predictions and pay out only when every condition is met. That work has led him to ask whether similar products could help business owners offset losses from events beyond their control.

This question has a personal dimension for Mansharamani. He grew up in India in a business family and saw firsthand how events outside an owner's control can affect small businesses. That perspective helps explain why he sees a future in which prediction markets have practical uses for businesses as well as traders.

The risks that come with opening the doors

For a restaurant owner, bad weather can turn an ordinary day into a loss that no change to the menu or service can prevent. In one survey, 12% of small employer firms reported revenue losses related to a natural disaster. Mansharamani says, "Let's suppose you open a restaurant in New York. If it rains or floods in New York, I didn't choose that when I signed up for a restaurant, but now I have to worry about it."

Weather is only one example. Issues like electricity outages create similar problems: owners can prepare, but may still have to absorb the losses. The same idea could extend to an outdoor event canceled by extreme weather, a hotel affected when a major conference is called off or a delivery business disrupted by a prolonged storm.

Mansharamani sees prediction markets, where people trade contracts tied to event outcomes, as a possible way to give smaller businesses more options for managing those exposures.

For market builders, that starts with understanding the business well enough to define a useful contract. A restaurant owner worried about flooding would need a trade that pays when the defined flooding event occurs. Mansharamani's argument is that identifying the risk is only the beginning: the financial product must correspond to the loss the event creates for the business.

Trader conversations shaped the product

Mansharamani's interest in derivatives, financial contracts whose value depends on an underlying asset or outcome, grew out of testing different ways to use prediction markets. His early experiments included a social product for small groups and a single interface for trading across platforms. Conversations with traders helped him see a larger opportunity to build additional financial products around those markets.

He compares prediction markets with stocks, where options and futures offer more choices than simply buying or selling shares. His logic is simple: "If prediction markets are a new asset class, there are going to be new derivatives on top of them." In practice, that means products that let people combine predictions or tailor their exposure rather than trade one event at a time.

Mansharamani and Eric Liu, his co-founder at Totalis, tested that thesis with a prediction-market prototype built at a hackathon. Interest from traders persuaded them to keep building and eventually commit to the company.

The team moved toward combo trades, often called "parlays" a structure familiar from sports betting that combines several predictions into one position. Instead of trading each event separately, a user can express a view about several outcomes happening together. Every condition must be met for the combined position to pay out.

Trader feedback helped Mansharamani and Liu turn a broad derivatives thesis into a product that drew early user interest. For the proposed small-business use case, the same principle would apply: any combination would need to match an owner's real-world exposure.

The right event still needs the right price

That work highlighted why traders may want markets tailored to specific outcomes.

For the proposed small-business use case, the relevant question may not simply be whether it rains, but whether severe weather hits a particular place during its busiest hours. Applying that approach to business risk management would mean offering events that correspond directly to what affects an owner's income.

In that future use case, once the relevant events were available, a business owner would still need a clear price for the combined trade.

For its existing combo product, Totalis handles pricing through requests for quotes, or RFQs. A user selects a combination, receives a price and decides whether to accept it.

In a future business use case, the question would be straightforward: does the price make sense relative to the potential loss, and are the payout conditions clear?

The business exposure is the test

Pravesh Mansharamani's argument begins with risks owners already carry. His experience building Totalis informs what prediction markets would have to offer small businesses: a product that pays under conditions matching an owner's actual loss.

That is the test behind his ambition whether these products can help businesses manage risks they already face.

Disclaimer:​ This article examines prediction-market technology and a proposed business risk-management application. It does not recommend placing bets or making trades, and it is not financial, investment or legal advice. The small-business examples are illustrative, not documented Totalis customer outcomes. Trading can result in financial loss.