Forecast Markets Advancing Beyond Betting, Poised for $10B in Revenue


Published on: February 23, 2026, 12:19h.

Updated on: February 23, 2026, 12:20h.

  • Analysts predict prediction markets could achieve $10 billion revenue by 2030
  • Market research indicates the sector is advancing beyond just consumer gambling technology
  • Potential growth paths for enhanced institutional engagement identified

Revenue within prediction markets is climbing alongside transaction volume, suggesting that this emerging industry could approach $10 billion in revenue by 2030.

National Council on Problem Gambling prediction markets
Visual representation of Kalshi as featured in the Apple App Store. Analysts predict that the prediction market sector could potentially surpass $10 billion in revenue by 2030. (Image: Getty)

This perspective comes from Citizens analyst Devin Ryan, who reaffirmed the $10 billion revenue forecast made last December. He noted that as industry revenue aligns with rising transaction volume, the previously estimated $2 billion in annual revenue has now escalated to approximately $3 billion.

“Transaction fees currently serve as the main revenue source; nevertheless, this sustained activity is enabling deeper investments in infrastructure, data products, and increased institutional access,” remarked Ryan.

Concerning the ambitious $10 billion revenue target for 2030, which indicates a remarkable growth trajectory rarely witnessed in new financial or wagering products, Ryan views this forecast as a “pragmatic medium-term benchmark rather than a final destination.”

Positive Developments for Non-Sports Prediction Markets

A significant point of contention among critics and proponents of prediction markets is the industry’s alleged reliance on sports contracts, which ties it closely to sports betting.

Supporters argue that prediction markets encompass far more than just sports betting alternatives, highlighting a wide array of event contracts across cryptocurrencies, pop culture, politics, and beyond. In contrast, skeptics note that data shows sports derivatives still constitute the majority of volume on yes/no platforms.

There’s encouraging news for advocates of prediction markets, as Ryan anticipates a shift from a niche, consumer-focused gambling technology to a burgeoning asset class. This transition is crucial for the sector’s long-term growth, particularly outside the realm of sports. Notably, the Citizens analyst highlights intriguing volume trends that may ease concerns about prediction markets mimicking seasonal fluctuations typical of sportsbook operations.

Ryan points out, “January volumes hit approximately $9.6 billion at Kalshi (+45% compared to December) and roughly $7.7 billion at Polymarket (+44% compared to December), even as the NFL season concluded, with February activity maintaining similar levels.” This consistency signifies that liquidity is expanding into a broader spectrum of recurring categories, promoting growth beyond the sports sector.

However, it’s important to recognize that critics may observe the NFL playoffs in January, culminating in the Super Bowl and the Winter Olympics occurring this month.

Enhancing Institutional Interest in Prediction Markets

Crucial for the future of prediction markets is the industry’s capability to attract institutional investors—a segment that can substantially broaden applications beyond just sports contracts.

This evolutionary process is underway. Recently, Kalshi announced a partnership with Tradeweb Markets (NASDAQ: TW) to enhance institutional access to prediction markets. Tradeweb operates several leading trading platforms in credit, equities, interest rates, and money markets. Additionally, in December, Intercontinental Exchange (NYSE: ICE), a key investor in Polymarket, integrated Polymarket probabilities into its professional data feeds.

“Simultaneously, prominent market makers like Susquehanna have started dedicating resources to prediction market trading, including the recruitment of specialized experts to develop real-time pricing and risk models for event-driven outcomes,” Ryan concluded.



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