Author: Zhou, ChainCatcher

According to Bloomberg, the world's leading quantitative trading firm Jump Trading will provide liquidity to two leading prediction market platforms Kalshi and Polymarket in exchange for a small equity stake.

It is reported that its agreement with Kalshi involves a fixed equity share, while its stake in Polymarket will dynamically increase with the trading volume provided by its U.S. operations.

For Jump Trading , the potential value of this equity is substantial. Previous reports indicated that Kalshi was valued at about 11 billion dollars, and Polymarket at about 9 billion dollars, with the sector still expanding rapidly.

Jump may utilize its dedicated team of over 20 people to provide professional market-making services to enhance the trading experience on the platform and capture long-term potential profits.

1, Liquidity Bottlenecks in Prediction Markets

For a long time, liquidity has been a key bottleneck to the growth of prediction markets.

Kalshi and Polymarket , as the two leading players, faced similar challenges in their early stages: trading volume surged during popular events, but non-popular contracts often had shallow depth and large spreads, causing significant slippage, especially for large orders that were hard to execute.

Among them, Kalshi introduced a professional institution Susquehanna International Group (SIG) as its main institutional market maker in 2024 .

SIG established a trading department focusing on event contracts, and as a seasoned options giant, it possesses specialized algorithms and continuous order capabilities, greatly improving the trading experience on Kalshi , especially in sports and economic data contracts.

Additionally, Kalshi also provides part of the opposing side liquidity through affiliated trading entities to stabilize pricing and fill gaps.

At the same time, the platform launched a liquidity incentive program, offering cash rewards, lower fees, and relaxed position limits to qualified participants, further attracting algorithmic players and large investors.

Polymarket ’s situation has more native features of cryptocurrency. As an on-chain order book platform based on Polygon , it mainly relied on decentralized incentive mechanisms to gather liquidity in its early days.

According to official documents, the platform returns part of the fees in the form of USDC daily through the Maker Rebates program, attracting automated market-making robots and independent liquidity providers. These algorithmic players actively place orders for new markets or popular contracts, earning spreads and rebate profits.

However, this model also brings fragmentation and instability; for example, when the excitement of events fades, market-making bots may actively withdraw orders, resulting in rapid widening of spreads and quick shrinkage of depth.

Additionally, Polymarket has also experimented with internal market-making teams and community-driven LP mechanisms, but overall, its liquidity shows that blockbuster events have sufficient depth, while it relies on retail investors and algorithmic short-term profit-taking in normal times.

The commonality between the two platforms at this stage is that liquidity highly depends on a few key participants and incentive-driven retail forces.

2, Exchanging Equity for Liquidity?

Although the prediction markets sector is expected to experience explosive growth from 2024 to 2025 , especially driven by elections and sports events, it is essentially still an emerging market with relatively scarce liquidity, far from the depth and stability of traditional finance.

Kalshi and Polymarket reaching a collaboration to exchange equity for liquidity centers on their highly aligned demands as the sector matures, which is nearly impossible in the early stages.

Now, after several years of development, both platforms have accumulated significant trading volumes and valuations, but they have also exposed the limits of incentive mechanisms.

Past reliance on cash subsidies, fee refunds, and community algorithmic players has been able to temporarily boost depth during blockbuster events, but it has been challenging to form sustainable professional capacity.

The platforms also realize that relying solely on these methods is insufficient to support the transition from event-driven to daily infrastructure.

What they need is sustained, low-latency, rigorously risk-controlled institutional-level market-making capabilities, which is precisely the area traditional quantitative giants excel in.

Kalshi and Polymarket currently have ample funding, but cash cannot buy the long-term commitment of top market makers. Equity cooperation, on the other hand, directly links interests: the platform exchanges a small equity stake for Jump Trading’s core resources, which essentially shares future growth dividends with partners in advance.

Especially for Polymarket, as an on-chain native platform, the requirements for market makers regarding cryptocurrency infrastructure and on-chain execution experience are even higher.

It is reported that Jump Trading established its crypto department in 2021 , deeply engaging in DeFi and the Solana ecosystem. Therefore, it has accumulated rich practical experience in on-chain order books, low-latency market-making, cross-chain asset management, and risk control, making it highly compatible with Polymarket ’s Polygon + USDC settlement model.

Jump Trading ’s motivation on this side is also clear. As a quantitative firm with strong infrastructure in stocks, options, cryptocurrencies, and other asset classes, it also sees structural opportunities in the prediction market.

The model of exchanging equity for professional capacity is essentially a hybrid innovation of venture capital and traditional market-making business.

It allows the platform to secure top player support without excessively diluting their equity while enabling Jump to leverage potential valuation upside with minimal cash costs.

3, Is Market-Making Business Easy?

Providing market-making services for prediction markets is an opportunity worth laying out for top quantitative institutions, but it is far from easy or guaranteed profit; it is more akin to high-potential, high-risk strategic investment rather than a reliable cash cow business.

While the profit paths for prediction markets seem clear, implementing them is full of challenges.

The positive aspect is that market makers can earn spreads through continuous order placement, cash or USDC incentives provided by the platform, cross-platform arbitrage, and by capturing common structural mispricing in event contracts.

These Alphas from mature financial markets are becoming increasingly scarce but are still relatively abundant in the prediction markets, especially in stages dominated by retail, where marginal profits can sometimes reach high levels.

Some industry opinions suggest that the risk-adjusted returns of such asset classes may outperform traditional high-frequency or options trading.

However, as mentioned earlier, the liquidity of predicting events is highly fragmented.

Market makers must provide two-way quotes around the clock, but there is almost no profit during idle times, while during surges, more algorithms and professional traders share the pie.

Some observations indicate that market-making margins have decreased from the common 4-5% seen in sports entertainment events to about 2% .

Moreover, sudden news, black swan events, or information asymmetry can instantly lead to directional inventory losses, while the characteristics of contract expiration settlement leave extremely limited hedging tools; regulatory uncertainties further exacerbate challenges, such as Kalshi ’s sports contracts remaining in legal disputes at the state level, and Polymarket ’s U.S. business restart facing compliance pressures.

However, for Jump Trading , it has low-latency infrastructure, cross-asset risk models, and strong capital, enabling efficient capture of spreads and arbitrage.

More importantly, the equity value of Kalshi or Polymarket is likely still to have upside, which is essentially a play to leverage low cash costs to unlock high-growth sectors.

On the other hand, for small or independent market makers, the situation is much more challenging. Not only is the infrastructure threshold extremely high and the learning curve steep, but they are also easily squeezed by large institutions compressing spreads.

Overall, this business is highly concentrated among a few top players, making it difficult for retail investors or small teams to get a share.

Conclusion

At present, market-making services are still in a stage where "layout outweighs immediate returns." Jump Trading ’s entry is a testament to this judgment: top institutions are willing to invest heavily in teams and resources, as they see prediction markets as a long-term structural opportunity in emerging asset classes.

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