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Home»Alternative Investments»Prediction markets look to institutional investors for next phase of growth
Alternative Investments

Prediction markets look to institutional investors for next phase of growth

By CharlotteMay 27, 20265 Mins Read
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By Laura Matthews, Anirban Sen, Manya Saini and Niket Nishant

NEW YORK/BENGALURU, May 27 (Reuters) – Prediction market platforms including Kalshi are making a big push to attract top institutional investors and hedge funds, a move that could ripple through traditional finance even as the trading sites still have a ways to go to gain broad use.

Such markets have exploded in popularity and growth over the past year from retail traders and are now increasingly turning their attention to the lucrative client base ​of deep-pocketed financial institutions and investment firms with the financial firepower to place large block trades.

“Hedge funds need a more nuanced and surgical way to express their ‌views in other derivative markets that they can’t access in traditional financial venues,” said Asaf Meir, CEO of Solidus Labs, which is a trade surveillance partner for Kalshi. Meir added that a lot of hedge funds and institutional investors are looking closely at opportunities to execute trades on prediction markets.

Kalshi in recent weeks executed the first customized block trade on its platform and is actively targeting even larger institutions, the company’s head of institutional business, Andy Ross, told Reuters. The firm, whose annualized trading volumes have more than tripled over the past six months to $178 billion, said trading volumes from institutional investors have grown 800% over the past six months.

Those volumes ‌have primarily ​grown from increased adoption by large asset managers, hedge funds, prime brokerages and other financial institutions, Ross said. Such big-ticket clients ⁠tend to buy up contracts tied to outcomes of scheduled ⁠events that typically happen every month – for example, monthly payroll data.

These asset managers who bet on platforms like Kalshi typically manage risk by taking offsetting positions, often trading the opposite side of the bet on the same platform. Some of these contracts often exceed several million dollars, Ross said.

“We’re seeing much more institutional interest in hedging the next few months,” said Ross.

Still, Ross said that Kalshi is in the early stages of attracting a larger base of institutional investors and addressing concerns around liquidity on its platform.

“We’re in the foothills ​of this, but we’re climbing pretty fast here,” he said.

BETTING ON INSTITUTIONAL GROWTH

As they look to attract and expand their institutional client base, prediction markets have started to forge deals with prime brokers and other liquidity providers.

Clear Street, which acts as a broker to institutional investors and hedge funds, recently struck a partnership with Kalshi to give customers access to event contracts. ⁠Proprietary trading firm Jump Trading has also been working with institutional investors, like asset managers and hedge funds, ⁠to give them access to such platforms, according to two people familiar with the matter.

London-based futures and options broker Marex, which counts Jump ​as a client, recently started working with both Kalshi and rival Polymarket to help build the infrastructure to connect investors to these exchanges, the sources said.

Polymarket did not respond to requests for comment ​for this story. On its website, it gives no comment on institutional growth on its platform.

Quantitative trading platforms and market-making firms are also looking to ‌double down on the growing business of prediction markets. For instance, AQR Capital Management, Susquehanna International Group and crypto exchange OKX are among a handful of companies that have recently advertised roles for specialist prediction market traders on third-party sites, a Reuters review found. AQR declined to comment while Susquehanna and OKX did not respond to requests for comment.

“The ability to isolate a specific risk factor in real time with greater precision and without the noise of any other investment product is one of the primary selling points for prediction markets,” said Devin Ryan, head of financial ⁠technology research at Citizens JMP.

LIQUIDITY CONCERNS

Several analysts and market experts, however, warned that the prediction markets will need to address longer-term concerns around liquidity on their platforms to attract big investment firms, since larger trades often overwhelm shallow order books and move prices more sharply.

“No hedge fund is going to go and route flow to a venue that has less than, at the ⁠very minimum, $10 million daily notional volume,” said Meir. “Institutional adoption means not a ‌block trade every now and then. It means, for the type of flows I’m used to, it’s going to take a minute, ⁠but the market is working towards it, for sure.”

Edward Ridgely, co-founder and CEO of Stand, a platform that allows users to trade ​simultaneously on Kalshi ‌and Polymarket, said that some of the top markets on Polymarket have only about $30 million of total liquidity. Therefore, if a ​large institutional investor put ⁠several million dollars into a particular market, it would result in a wild swing in prices in that market.

“What we’ve seen on our end are people like individual traders who don’t have quite the same bankroll or account size that I think normal institutions do,” Ridgely said. “There’s institutional interest, but there’s not institutional activity.”

Kalshi said it is attempting to address liquidity concerns from clients by bringing more institutions onto its platform.

Efforts will eventually pay off, some said.

“They are increasingly being treated as a legitimate alternative asset class,” Toni Gemayel, head of prediction markets at Coinbase, told Reuters. “Institutions are using these markets to hedge against specific risks that traditional instruments might only capture indirectly.”

(Reporting by Manya Saini, Niket Nishant and Pritam Biswas in Bengaluru, and Laura Matthews in New York; Writing by Anirban ​Sen; Editing by Megan Davies and Matthew Lewis)





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