Author:Zhou, ChainCatcher
According to Bloomberg, the world's top quantitative trading company Jump Trading will provide liquidity to the 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 grow with the trading volume it provides for its U.S. business.
For Jump Trading , the potential value of this equity is substantial. Previous reports indicated that Kalshi is valued at approximately 110 billion USD, and Polymarket at about 90 billion USD, with the sector still expanding rapidly.
Jump may deploy its dedicated team of over 20 people to provide professional market-making services, aiming to enhance the platform's trading experience and capture long-term potential gains.
1、The Liquidity Bottleneck of Prediction Markets
Liquidity has always been a key bottleneck for the growth of prediction markets.
Kalshi and Polymarket , as the current two leading players, faced similar challenges in their early stages: trading volume surges during hot events, but non-popular contracts often have shallow depth and wide spreads. Large user orders can experience significant slippage or even fail to execute.
Among them, Kalshi introduced the professional institution Susquehanna International Group (SIG) as its primary institutional market maker in 2024 .
SIG established a trading department focused on event contracts. As a veteran options giant with professional algorithms and continuous quoting capabilities, it significantly improved Kalshi 's trading experience, particularly excelling in sports and economic data contracts.
In addition, Kalshi also provides some contra-side liquidity through internal affiliated trading entities to stabilize pricing and fill gaps.
Simultaneously, the platform launched a liquidity incentive program, offering cash rewards, reduced fees, and relaxed position limits to eligible participants, further attracting algorithmic players and large investors.
Polymarket 's situation is more crypto-native. As an on-chain order book platform based on Polygon , it initially relied mainly on decentralized incentive mechanisms to aggregate liquidity.
Official documentation states that the platform uses a Maker Rebates program to return a portion of fees daily in USDC , attracting automated market-making bots and independent liquidity providers. These algorithmic players actively place orders on new markets or热门 contracts, earning spreads and rebate income.
However, this model also带来 fragmentation and instability. For example, when event热度 subsides, market-making bots actively withdraw orders, causing spreads to widen rapidly and depth to shrink quickly.
Furthermore, Polymarket has also experimented with internal market-making teams and community-driven LP mechanisms. But overall, its liquidity is characterized by sufficient depth for blockbuster events, while relying on retail investors and algorithms for short-term profit-seeking at other times.
The commonality between the two platforms at this stage is that liquidity highly depends on a few key participants and incentive-driven retail力量.
2、Equity in Exchange for Liquidity?
The prediction market sector, although experiencing explosive growth in 2024–2025 (especially driven by elections and sports events), is本质上 still a relatively liquidity-scarce emerging market, far from the depth and stability of traditional finance.
Kalshi and Polymarket were able to reach equity-for-liquidity agreements with Jump Trading because their demands are highly aligned as the sector matures, which was almost impossible in the early stages.
Now, after several years of development, the two platforms have accumulated considerable trading volume and valuation, but have also exposed the limits of incentive mechanisms.
Past reliance on cash subsidies, fee rebates, and community algorithmic players could briefly boost depth during blockbuster events but struggled to form lasting professional capacity.
The platforms also understand that these measures alone are insufficient to support the transition from event-driven to daily infrastructure.
They need sustained, low-latency, risk-controlled institutional-grade market-making capabilities, which is precisely the forte of traditional quantitative giants.
Kalshi and Polymarket currently have ample funding, but cash cannot buy the long-term commitment of top market makers. Equity cooperation is different; it directly aligns interests: the platforms exchange minority equity for Jump Trading's core resources,相当于 sharing future growth红利 with partners in advance.
Especially for Polymarket, as an on-chain native platform, it has higher requirements for market makers' crypto infrastructure and on-chain execution experience.
It is reported that Jump Trading established a crypto division in 2021 and has deeply participated in the DeFi and Solana ecosystems. Therefore, it has accumulated rich practical experience in on-chain order books, low-latency market making, cross-chain asset management, and risk control, which aligns well with Polymarket 's Polygon + USDC settlement model.
Jump Trading 's motivation is equally clear. As a quantitative company with strong infrastructure across multiple asset classes like stocks, options, and crypto, it also sees the structural opportunity in prediction markets.
The model of exchanging equity for professional capacity is essentially a hybrid innovation combining venture capital with traditional market-making business.
It allows platforms to lock in support from top players without diluting too much equity, and also enables Jump to leverage potential valuation upside with minimal cash cost.
3、Is the Market-Making Business Easy?
Providing market-making services for prediction markets is currently a worthwhile opportunity for top quantitative institutions to布局, but it is far from easy or guaranteed profit. It is more like a high-potential, high-risk strategic investment rather than a daily cash cow business.
Because the profit path in prediction markets seems clear, but practical operation is full of challenges.
The positive aspect is that market makers can earn spreads through continuous quoting, cash or USDC incentives provided by the platform, cross-platform arbitrage, and profits from structural mispricing common in event contracts.
These Alpha sources are becoming increasingly scarce in mature financial markets but are still relatively abundant in prediction markets, especially in the retail-dominated phase, where edge returns can sometimes reach high levels.
Some industry views suggest that the risk-adjusted returns of this asset class may be superior to traditional high-frequency or options trading.
However, as mentioned earlier, liquidity for prediction events is highly fragmented.
Market makers must provide two-sided quotes around the clock, but profits are almost non-existent during idle times, and during peak times, they are shared with more algorithms and professional traders.
Some observations indicate that market-making profit margins have been compressed from the common 4-5% in sports and entertainment events to around 2% .
Additionally, breaking news, black swan events, or information asymmetry can instantly cause directional inventory losses, and the contract expiration settlement特性 makes hedging tools extremely limited; regulatory uncertainty further increases the difficulty, such as ongoing state-level legal disputes over Kalshi 's sports contracts, and the compliance pressure facing Polymarket 's U.S. business restart.
However, for Jump Trading , it possesses low-latency infrastructure, cross-asset risk models, and strong capital, enabling it to efficiently capture spreads and arbitrage.
More importantly, the equity value of Kalshi or Polymarket likely still has room for appreciation. This is essentially a play of leveraging a high-growth sector with low cash cost.
For small and medium-sized or independent market makers, the situation is much more difficult. Not only is the infrastructure门槛极高 and the learning curve steep, but they are also easily squeezed out by large institutions on 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 "布局 is greater than immediate returns." Jump Trading 's entry is a footnote to this judgment: top institutions are willing to heavily invest teams and resources precisely because they see the long-term structural opportunity of prediction markets as an emerging asset class.
