Top Wall Street Quant Firm Jump Trading Enters Prediction Market, Is the Era of Retail Investors Over?

Author: Zhou, ChainCatcher

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

It is reported that their agreement with Kalshi involves a fixed equity share, while their stake in Polymarket will grow dynamically with the trading capacity they provide for their US operations.

For Jump Trading, this equity could be quite valuable. Previously, it was reported that Kalshi is valued at approximately $11 billion, and Polymarket at around $9 billion, with the sector still expanding rapidly.

Jump may deploy its dedicated team of over 20 professionals to offer professional market-making services, enhancing trading experience on these platforms and capturing long-term potential gains.

1. Liquidity Bottlenecks in Prediction Markets

Liquidity has always been a key bottleneck for the growth of prediction markets.

As the current leading players, Kalshi and Polymarket face similar challenges in early stages: during major events, trading volume surges, but non-hot contracts often have shallow depth and wide spreads, making large orders prone to significant slippage or even failure to execute.

Kalshi, in particular, plans to introduce a professional institution, Susquehanna International Group (SIG), as its primary market maker in 2024.

SIG has established a dedicated trading division focused on event contracts. As an established options giant, it possesses advanced algorithms and continuous order placement capabilities, significantly improving Kalshi’s trading experience, especially for sports and economic data contracts.

In addition, Kalshi also provides some counterparty liquidity through internal related trading entities to stabilize prices and fill gaps.

Furthermore, the platform has launched liquidity incentive programs, offering cash rewards, reduced fees, and relaxed position limits to qualified participants, further attracting algorithmic traders and large players.

Polymarket’s situation is more native to crypto. As an on-chain order book platform based on Polygon, it initially relied heavily on decentralized incentive mechanisms to gather liquidity.

According to official documentation, the platform uses a Maker Rebate program that refunds part of the fees daily in USDC, attracting automated market-making bots and independent liquidity providers. These algorithmic traders actively place orders on new markets or hot contracts to earn spreads and rebate income.

However, this model also introduces fragmentation and instability. When event interest wanes, market makers tend to withdraw orders, causing spreads to widen rapidly and depth to shrink quickly.

Additionally, Polymarket has experimented with internal market-making teams and community-driven LP mechanisms, but overall, its liquidity tends to be sufficient during major events and relies on retail and algorithmic traders for short-term profit-seeking during normal times.

Both platforms at this stage share the commonality that liquidity heavily depends on a few key participants and incentivized retail traders.

2. Trading Equity for Liquidity?

Although prediction markets are expected to experience explosive growth in 2024–2025, driven by elections and sporting events, fundamentally, they remain emerging markets with relatively scarce liquidity, far from the depth and stability of traditional finance.

The ability of Kalshi and Polymarket to reach an equity-for-liquidity partnership with Jump Trading hinges on their aligned needs as the sector matures—something nearly impossible in early stages.

Today, after several years of development, both platforms have accumulated significant trading volume and valuation, but they also reveal the limits of their incentive mechanisms.

Relying on cash subsidies, fee rebates, and community algorithmic traders can temporarily boost depth during major events, but they are insufficient for sustainable, everyday infrastructure.

What they need are continuous, low-latency, risk-controlled institutional market-making capabilities—areas where traditional quant giants excel.

While Kalshi and Polymarket are currently well-funded, cash alone cannot secure long-term commitments from top market makers. Equity partnerships, however, directly align interests: platforms exchange a minority stake for Jump Trading’s core resources, effectively sharing future growth dividends upfront.

Polymarket, being an on-chain native platform, has higher requirements for market makers’ crypto infrastructure and on-chain execution experience.

It is reported that Jump Trading established its crypto division in 2021, deeply involved in DeFi and the Solana ecosystem. As a result, it has accumulated extensive practical experience in on-chain order books, low-latency market-making, cross-chain asset management, and risk control. Its compatibility with Polymarket’s Polygon + USDC settlement mode is quite high.

Jump Trading’s motivation is also clear. As a quant firm with strong infrastructure across stocks, options, and crypto, it sees structural opportunities in prediction markets.

Using equity to acquire professional capacity is essentially a hybrid innovation combining venture capital and traditional market-making.

This approach allows the platform to lock in top-tier players’ support without excessive equity dilution, while enabling Jump to leverage potential valuation upside at minimal cash cost.

3. Is Market-Making a Lucrative Business?

Providing market-making services for prediction markets is a strategic opportunity worth considering for top quant firms at this stage, but it is far from easy or guaranteed profit. It’s more of a high-potential, high-risk strategic investment rather than a daily cash cow.

While the profit path for prediction market market-making seems straightforward—earning spreads through continuous quoting, platform incentives in cash or USDC, arbitrage across platforms, and structural mispricings in event contracts—the reality is challenging.

These alpha sources are increasingly scarce in mature financial markets but remain relatively abundant in prediction markets, especially during retail-driven phases, where edge profits can sometimes be high.

Some industry experts believe that risk-adjusted returns in this asset class may outperform traditional high-frequency or options trading.

However, as previously mentioned, liquidity in prediction events is highly dispersed.

Market makers must provide continuous two-sided quotes, but during quiet periods, they often see little profit, while during surges, they are quickly squeezed out by more algorithms and professional traders.

Observations indicate that profit margins have decreased from typical 4–5% in sports and entertainment events to around 2%.

Moreover, sudden news, black swan events, or asymmetric information can cause instantaneous directional losses, and the contract settlement process offers limited hedging tools. Regulatory uncertainties further complicate matters; for example, Kalshi’s sports contracts are still under state-level legal disputes, and Polymarket’s US operations face compliance pressures.

For Jump Trading, with its low-latency infrastructure, cross-asset risk models, and strong capital base, capturing spreads and arbitrage efficiently is feasible.

More importantly, the equity value of Kalshi or Polymarket likely still has upside potential—this is essentially a low-cash-cost way to leverage high-growth sectors.

For small or independent market makers, the situation is much tougher. The infrastructure requirements are high, the learning curve steep, and they are easily squeezed out by larger institutions.

Overall, this business remains highly concentrated among a few top players; retail traders and small teams find it difficult to participate meaningfully.

Conclusion

Currently, market-making in prediction markets remains a “long-term strategic layout” rather than an immediate profit driver. Jump Trading’s involvement underscores this view: top-tier institutions are willing to invest heavily in teams and resources because they see the long-term structural opportunities of prediction markets as an emerging asset class.