Hedge Funds Tap Prediction Markets for Data Edge

Prediction markets have exploded in popularity, with platforms like Polymarket and Kalshi becoming household names for betting on everything from elections to economic data. But for the sophisticated world of hedge funds, the real value isn't in placing bets—it's in the data those bets generate.

While some proprietary trading firms are actively dabbling in these platforms, the so-called 'smart money' is increasingly viewing prediction markets as a powerful new source of intelligence. This shift mirrors the post-GameStop era, when funds rushed to track retail sentiment on Reddit forums. Now, they're ingesting data on trading volumes and price movements to gain an edge in traditional markets.

The data is readily available, often for free, and offers a real-time pulse on market sentiment. As these platforms mature, they're becoming less about gambling and more about a new class of alternative data.

Despite headlines about big payouts for individual gamblers, most hedge funds have avoided trading directly on prediction markets. The reasons are practical: many funds require deeper markets to execute large bets on macro developments, and the nascent space often struggles to meet their scale.

Furthermore, getting compliance teams to sign off on using these platforms can be a significant hurdle. The focus for the majority of funds is therefore on the data exhaust—the information generated by the activity on these platforms.

Similar to the rush to track retail traders discussing stocks on Reddit forums after the GameStop phenomenon in early 2021, funds are, at the very least, ingesting data on activity on platforms like Polymarket and Kalshi. And these platforms make it easy, with a free data feed on trading volumes.

Some proprietary trading firms are now dabbling in the space. For example, Susquehanna posted job openings for prediction market traders, signaling a growing interest in direct participation. However, the broader industry trend remains focused on data acquisition.

Data companies and forecasting firms are building products specifically designed to harness the information flowing from prediction markets. Dysrupt Labs, an Australian data and forecasting company that works with hedge funds and family offices, is a prime example.

The firm pulls prediction market data into its internal algorithms to decide if the 'informed minority' is in line or drifting away from the consensus expectations. This approach provides a unique signal that traditional data sources might miss.

Prediction markets are 'the fastest way to model a known unknown,' and the average drift from the consensus generates up to 12 basis points of uncorrelated gains.

According to Dysrupt Labs CEO Karl Mattingly, the signal they can generate from recurring economic releases, like inflation or jobs data, can give users an 'early view on if the prevailing view is going to change in the next two to four days.' Their research found that 95% of the time, the consensus from traditional sources like economists and consultants aligns with prediction markets. However, that other 5% represents a critical opportunity.

Mattingly emphasizes that 'Financial markets need better and faster information,' and prediction markets offer a 'really fast way of looking at things.' This speed is crucial for capturing value before the broader market adjusts.

The infrastructure supporting this data flow is expanding rapidly. Platforms like Polymarket and Kalshi provide accessible data feeds, making it easier for funds and data firms to integrate this information into their workflows.

Polymarket has signed partnerships with exchange and clearing house Intercontinental Exchange and Dow Jones. These collaborations are expected to produce more sophisticated data products tailored for institutional use in the future.

Despite this progress, the newness of the platforms means hedge funds are still figuring out the most effective applications. The data is novel, but its utility in complex macro models is still being tested.

The broader interest has largely been at the intersection with sports betting, according to Daryl Smith, head of research at data consulting firm Neudata. This suggests that while the technology is promising, widespread adoption for macro event analysis is still in its early stages.

Currently, the most concrete use cases are emerging in specific niches. A data executive at a smaller hedge fund noted that they primarily use prediction market data to track general interest in gambling. This serves as a helpful signal for the success of stocks like DraftKings and Flutter Entertainment, the parent company of FanDuel.

However, for broader macro strategies, adoption is slower. Macro managers aren't yet building Kalshi's odds on events like a Chinese invasion of Taiwan into their models. The data remains intriguing but unproven for high-stakes geopolitical forecasting.

As Daryl Smith from Neudata stated, 'We have not seen compelling evidence of demand for macro event-related prediction markets data.' This highlights the gap between potential and current practice.

The challenge for the industry is to demonstrate consistent, actionable insights that justify the integration of this new data stream into established investment processes. Until then, many funds will remain observers rather than participants.

The evolution of prediction markets from gambling platforms to data sources is accelerating. As platforms like Polymarket secure partnerships with major financial institutions, the quality and accessibility of data will only improve.

For hedge funds, the opportunity lies in distinguishing signal from noise. The ability to detect early shifts in consensus—before they are reflected in traditional economic forecasts—could provide a valuable edge in an increasingly competitive market.

While direct trading remains limited, the data arms race is already underway. Firms that successfully integrate these alternative data streams into their models may find themselves ahead of the curve in the next cycle of market innovation.