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Market Intelligence Prediction Markets 18 min read • March 2026

Wall Street Moves In: How Institutional Traders Are Reshaping Prediction Markets

Jump Trading, SIG, and DRW have entered prediction markets with specialized desks and equity stakes. An 80x surge in monthly volume signals something structural—and it changes the calculus for every operator pricing event outcomes.

By the Metrics
$63B+
2025 industry volume, up from $15.8B in 2024
1,108%
Kalshi year-on-year volume growth in 2025
~75%
US prop firms now trading or evaluating prediction markets
Problem
Prediction markets grew 300%+ in 2025, but thin liquidity, missing back-office infrastructure, and regulatory inconsistency are blocking serious institutional scale.
Approach
We analyzed Acuiti’s Q4 2025 global prop trading survey, platform volume data from Kalshi and Polymarket, and institutional deal flow from Jump Trading, SIG, and Robinhood.
📈
Outcome
Operators and trading platforms that understand the structural arbitrage dynamics and infrastructure gaps will be best positioned as prediction markets mature into a recognized asset class.
in 𝕏

In February 2026, Jump Trading announced it was acquiring small equity stakes in both Kalshi and Polymarket in exchange for providing market liquidity. It was a quiet announcement by the standards of financial news—but the structural signal it sent was anything but quiet. One of the most sophisticated quantitative trading firms on Earth was not just trading prediction markets. It was staking ownership in the infrastructure.

That deal did not happen in isolation. Susquehanna International Group had already become Kalshi’s first designated market maker. DRW had built a specialized prediction markets desk. Robinhood and SIG jointly acquired a CFTC-licensed exchange and rebranded it. And nearly half the global proprietary trading industry was either already trading prediction markets or actively evaluating the space.

This is not a trend. It is a structural shift—and understanding its mechanics matters for anyone pricing event outcomes for a living.

From $100M to $8B a Month: The Volume Surge That Got Wall Street’s Attention

The scale of prediction market growth in 2024–2025 is difficult to overstate. Monthly trading volumes grew from under $100 million in early 2024 to over $8 billion by December 2025—an 80x surge in under two years. That kind of growth curve, in that compressed a timeframe, does not happen through organic retail expansion alone. It requires institutional capital, quantitative strategies, and professional liquidity provision.

The annual numbers confirm the trajectory. Industry-wide prediction market volume rose from approximately $15.8 billion in 2024 to more than $63 billion in 2025—a 300%+ year-on-year increase, according to Citizens Bank’s December 2025 analysis. Within that total, two platforms account for nearly everything.

Kalshi posted $23.8 billion in 2025 volume—a 1,108% year-on-year increase, equivalent to roughly 12x growth—executing 97 million trades, up 1,680% from the prior year. Polymarket, operating its crypto-native permissionless model, reached $22 billion. Combined, Kalshi and Polymarket control approximately 97.5% of global prediction market share. The rest of the market is rounding error.

Platform 2025 Volume YoY Growth Market Share
Kalshi $23.8B 1,108% ~38%
Polymarket $22B N/A ~35%
Rest of market ~$17B ~27%
Industry total $63B+ 300%+

Kalshi raised $1 billion at an $11 billion valuation in November 2025. Polymarket was simultaneously seeking a $9–15 billion valuation. Citizens Bank characterized prediction markets as an “emerging asset class” in its December 2025 report, projecting potential for a multitrillion-dollar annual market as institutional participation scales. That projection is not speculative boosterism—it is an extrapolation of the participation curve already visible in the data.

Jump, SIG, and DRW: How the Biggest Names in Trading Are Staking Their Claim

Institutional entry into prediction markets has followed a recognizable pattern from other emerging asset classes: first the quants arrive quietly, then the infrastructure deals get announced, then the mainstream platforms follow. Prediction markets are now in the infrastructure phase.

Jump Trading: Equity for Liquidity

Jump Trading had been quietly making markets on Kalshi since November 2025—several months before any public announcement. By February 9, 2026, Jump formalized a novel arrangement: small equity stakes in both Kalshi and Polymarket in exchange for providing liquidity on both platforms. This “equity-for-liquidity” model is structurally significant. Jump is not simply a fee-paying customer of these platforms. It is a co-owner with direct financial incentive to grow platform volume and market depth.

This model has precedent in early-stage crypto exchange development, where market makers took equity positions in exchanges to align incentives with platform growth. The fact that it is now appearing in CFTC-regulated prediction markets signals that institutional participants view this as a long-duration structural position, not a short-term arbitrage play.

SIG: The Designated Market Maker

Susquehanna International Group became Kalshi’s first official designated market maker (DMM)—a formal designation that carries both privileges and obligations. SIG receives reduced trading fees and higher position limits in return for committing to continuous two-sided liquidity provision across Kalshi’s contract universe. DMM structures are well-understood in equity and options markets; their arrival in prediction markets signals that Kalshi is operating with institutional-grade market structure in mind.

SIG and DRW have both built specialized prediction markets trading desks deploying the same quantitative playbooks used in equities and derivatives. These are not generalist trading teams that spun up a prediction market book as a side project. They are purpose-built teams targeting what SIG and DRW internally categorize as TradFi-Event Arbitrage strategies.

Robinhood and SIG: Building the Infrastructure

In January 2026, Robinhood and SIG jointly completed the acquisition of MIAXdx, a CFTC-licensed designated contract market (DCM) and derivatives clearing organization (DCO). They rebranded it as “Rothera,” targeting a Q2 2026 launch for futures and prediction contracts. This is not two firms trading on existing infrastructure. This is two major financial institutions building new regulated infrastructure from the ground up—with CFTC licensing already in place.

Robinhood’s prediction markets product had already become its fastest-growing revenue line, with 9 billion contracts traded by more than 1 million customers within its first year. The Rothera acquisition signals that Robinhood sees prediction markets as a long-term product category, not a feature.

The structural signal: When firms of Jump’s, SIG’s, and Robinhood’s sophistication make equity investments in prediction market infrastructure—not just trade on it—they are signaling a multi-year horizon view. These are not speculative trades on a trending product. They are infrastructure bets on an emerging asset class.

Nearly Half the Global Prop Trading Industry Is Now Evaluating Prediction Markets

Individual firm moves are visible and dramatic. The Acuiti Q4 2025 survey of senior global prop trading executives provides the broader picture—and it shows that institutional interest is not concentrated in a handful of early movers. It is distributed across the industry.

45% of global prop trading firms are either already trading prediction markets (10%) or actively considering it (35%)—Acuiti Q4 2025 survey of senior global prop trading executives

Ten percent of global prop firms are already actively trading prediction markets. Another 35% are actively considering entry. Combined, just under half the entire global proprietary trading industry is engaged with the space—within roughly 18 months of meaningful volume appearing on these platforms.

The US vs. Europe gap is the most analytically useful data point in the Acuiti survey. Approximately 75% of US-based prop firms are evaluating or trading prediction markets. Only 37% of European prop firms have reached the same stage. That is more than a 2x participation gap between two geographies with broadly equivalent quantitative trading sophistication. The gap is not about market structure familiarity or technology capability. It is entirely attributable to regulatory clarity.

Geography Trading or evaluating prediction markets Key driver
United States ~75% CFTC regulatory clarity in 2025
Europe 37% Fragmented regulatory frameworks, no CFTC equivalent

The forward-looking view from the survey reinforces the structural thesis. Forty percent of surveyed prop trading firms believe prediction markets will become a meaningful part of the institutional proprietary trading landscape within 3–5 years. Ultra-low latency and algo-first firms are the earliest institutional adopters—mirroring the adoption curve seen in early-stage crypto markets and alternative data markets before them.

Arbitrage, Not Speculation: Why Quants Are Here and What They’re Hunting

The framing of prediction markets as a “sophisticated gambling” product misses the primary institutional motivation entirely. Quants are not entering prediction markets because they have strong views on election outcomes or sporting events. They are entering because these markets are structurally inefficient in ways that generate measurable, repeatable alpha through arbitrage—and because they provide genuine hedging utility that traditional financial instruments cannot replicate.

The most visible alpha opportunity is cross-platform mispricing. Institutional traders have identified 4–6 cent arbitrage spreads between Polymarket and Kalshi on events expiring within 24 hours. On a binary contract priced at $0.50, a 5-cent spread represents a 10% return on capital in under 24 hours—risk-free if the position can be executed before the spread closes. This is not subtle alpha. It is the kind of structural inefficiency that gets exploited rapidly once professional market makers identify it.

The fact that these spreads still existed in Q4 2025 reflects the nascency of institutional participation and the fragmented liquidity across platforms. As professional market makers deepen their presence, cross-platform arbitrage will compress toward zero—exactly as it did in equity markets when electronic market makers arrived, and in crypto markets when institutional arbitrageurs entered.

Beyond pure arbitrage, macro funds and hedge funds are using prediction markets for a more sophisticated purpose: hedging specific regulatory, legislative, and geopolitical event risks that traditional equity and bond markets cannot efficiently price. A fund holding a large position in a healthcare company ahead of an FDA decision faces event risk that equity options cannot hedge precisely. A prediction market contract on that specific FDA decision can. This creates genuine utility for institutional capital that goes beyond arbitrage trading.

The liquidity constraint: Even as institutional participants enter, slippage on large trades ($50K+) remains 1–2%. For a prop desk running $1M+ positions, that is $10,000–$20,000 in friction per trade—a material drag that limits position sizes and strategy scope until market depth improves.

Where the Market Still Falls Short: Liquidity, Back-Office, and Consistency

The institutional influx is real and accelerating. The infrastructure to support it at scale is still being built. Understanding where the gaps are is as important as understanding the opportunity—because these gaps define the timeline and the winners.

The primary friction point is liquidity depth. Monthly volumes of $8 billion sound substantial, but that volume is distributed across hundreds of contracts on multiple platforms. On any individual contract, market depth is thin relative to what institutional prop desks require. Slippage of 1–2% on trades over $50K is a persistent constraint. Firms running large position sizes cannot scale their prediction market books without accepting meaningful execution drag, or waiting for liquidity to improve.

The second major gap is back-office infrastructure. Prediction market contracts do not fit neatly into the risk management, settlement, and reporting systems that prop desks use for equities, derivatives, and crypto. Adapting these systems—or building parallel infrastructure—requires investment that only makes sense if the firm has conviction in the asset class at scale. The 35% of global prop firms “actively considering” entry are largely blocked at this stage: the alpha opportunity is visible, but the operational infrastructure to capture it at scale is not yet in place.

The two leading platforms have taken structurally different approaches to institutional readiness. Kalshi’s compliance-focused, CFTC-regulated model is purpose-built to attract institutional capital. Its designated market maker program, formal regulatory status, and compliance infrastructure make it the entry point of choice for firms that require regulatory certainty. Polymarket’s crypto-native permissionless model retains dominance in global retail and political event markets, but its regulatory ambiguity creates friction for institutional participation at scale.

The Rothera exchange—the rebranded MIAXdx acquisition by Robinhood and SIG—is explicitly designed to close the infrastructure gap. As a fully licensed DCM and DCO, Rothera is targeting institutional-grade futures and prediction contracts with the settlement infrastructure that prop desks need. Its Q2 2026 launch will be a significant test of whether a purpose-built institutional prediction market venue can attract the liquidity depth that existing platforms have not yet achieved.

The CFTC Decision That Opened the Floodgates—and What Comes Next

The US vs. Europe adoption gap—75% vs. 37% of prop firms engaged—has a single explanation: CFTC regulatory clarity. In 2025, the CFTC withdrew a proposed rulemaking that would have blocked political and sports event contracts. That single decision removed the existential regulatory uncertainty that had kept institutional capital on the sidelines. It did not create the prediction market opportunity. It unlocked institutional participation in an opportunity that was already there.

more US prop firms are engaged with prediction markets than European firms—75% vs. 37%—a gap driven entirely by CFTC regulatory clarity that Europe has not yet replicated (Acuiti Q4 2025)

Kalshi’s $11 billion valuation is, in part, a bet on the permanence of that regulatory clarity. The market is pricing Kalshi as the institutional entry point into a newly regulated asset class—not as a speculative platform that regulators might shut down. The $1 billion raise at that valuation in November 2025 reflects institutional investor confidence in the regulatory environment, not just in Kalshi’s specific product.

The next regulatory question is predictable: will the CFTC framework extend to sports-event and gaming-adjacent contracts at the same level of clarity that now applies to political and economic event contracts? The answer will determine whether the adoption gap with Europe widens further, and whether the institutional vs. retail composition of prediction market participants shifts. If sports contracts receive equivalent regulatory treatment, the overlap with traditional sports betting becomes a product design question, not a regulatory one—and that has direct implications for sportsbook operators.

What Institutional Influx Means for Operators, Sportsbooks, and Sports Betting Platforms

The institutional arrival in prediction markets is not an abstract financial markets story. It has direct operational implications for anyone running an event-outcome pricing operation—which includes every sportsbook in every regulated market.

The first implication is price efficiency. Institutional market makers entering prediction markets will compress arbitrage spreads over time, improving the accuracy of implied probabilities on prediction market contracts. As Kalshi and Polymarket become more efficiently priced, they become increasingly reliable signals for event outcome probability—signals that are now being generated by some of the most sophisticated quantitative models in the world.

For sportsbooks, this creates a choice. The firms that treat prediction market implied probabilities as a data layer for their own odds-making will gain a systematic edge in identifying mispriced lines before institutional market makers close the gap. The firms that ignore prediction markets will find themselves in a progressively less favorable position as those markets deepen and become more efficient.

The “equity-for-liquidity” model pioneered by Jump Trading is also strategically significant beyond the prediction market context. It demonstrates that platform equity—not just trading fee economics—is now the currency for attracting institutional market makers to new trading venues. Any operator building a prediction market product, or integrating one, needs to think about market maker incentives in these terms.

Volume Growth
80×
Monthly volume surge in under 2 years — structural shift, not cyclical trend
Arbitrage Window
4–6¢
Cross-platform spread on 24-hour contracts — still exploitable, closing fast
3–5 Year View
40%
of prop firms forecast prediction markets become a meaningful institutional asset class — majority opinion within 5 years

For sportsbooks specifically, the institutional validation of event-outcome pricing as a serious asset class raises the bar for proprietary pricing models. When Jump Trading and SIG are deploying quantitative strategies against prediction market contracts, the quality of the information embedded in those prices goes up. Operators that can integrate prediction market implied probabilities into their pricing engines will have an edge in identifying value before the market finds it. Those that cannot will be increasingly exposed to sophisticated arbitrage from the institutional side.

The practical implication is not that sportsbooks need to become prediction market operators. It is that prediction market data has become a signal input that serious pricing operations cannot afford to ignore—in the same way that sharp money flows and line movement data became indispensable signal inputs a decade ago.

Data Sources & Research References

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