A significant surge in hiring indicates that trading firms are increasingly recognizing Polymarket as a legitimate trading platform rather than just a niche betting site.
With increased trading volume on Polymarket and Kalshi, quantitative firms are now capitalizing on market inefficiencies instead of merely predicting outcomes.
By Oliver Knight|Edited by Aoyon Ashraf Jun 6, 2026, 1:00 p.m. 6 min read
Major quantitative firms are establishing dedicated trading desks for prediction markets. (Modified by CoinDesk)Key Points:
- Top quantitative trading firms like DRW, Wintermute, and IMC are launching specialized desks for trading on platforms like Polymarket and Kalshi, treating them as a credible asset class.
- These firms focus less on predicting outcomes and more on exploiting short-term pricing discrepancies using high-speed arbitrage and market microstructure strategies developed in traditional finance and crypto.
- While established sports betting entities still dominate pricing accuracy, institutional players are rapidly entering as trading volumes rise and new infrastructure, including onchain exchanges like HyperLiquid, is being established ahead of major events such as the 2026 World Cup.
DRW, a Chicago-based trading powerhouse, has a long history of profiting from asset class mismatches and is now creating a dedicated desk for prediction markets, specifically targeting Polymarket and Kalshi.
This initiative signals a clear shift in perception among sophisticated quantitative trading firms, which traditionally rely on complex mathematical models and analytics, toward viewing prediction markets as a valid trading venue rather than a marginal betting option.
Having been influential in derivatives, fixed income, and crypto sectors since 1992, DRW recently advertised a position that involves real-time price monitoring across both platforms, identifying mispricings, and swiftly acting to capitalize on them before the prices align. The listed strategies include microstructure arbitrage, cross-platform arbitrage, and news-driven momentum trading at high speeds, techniques that have been refined in the cryptocurrency derivatives market and are now being adapted to sports and political events.
DRW is not the only firm making moves. Wintermute, which facilitates billions in daily crypto trading, is currently seeking algorithmic traders with expertise in prediction markets. Similarly, IMC is on the lookout for quantitative traders skilled in binary event contracts. Traditional crypto exchanges like OKX and Crypto.com have also been posting job openings recently.
This wave of hiring suggests that institutional trading firms are increasingly convinced that prediction markets have evolved into a serious asset class with significant profit potential.
Capitalizing on Pricing Inefficiencies
What is driving this newfound interest? The answer lies in the trading volumes on these platforms.
In 2025, Polymarket alone processed between $22 billion and $40 billion across various political, economic, and sports markets, a stark increase from virtually no volume just three years prior, with an increasing share concentrated in sports.
Last week, Polymarket's market for the UEFA Champions League Winner saw $256 million in trading, the 2026 NBA Champion market reached $399 million, and the 2026 NHL Stanley Cup market recorded $79 million after significant fluctuations that saw the Carolina Hurricanes' implied probability rise from below 10% to about 50% as they progressed in the Eastern Conference.
These three markets alone generated over $730 million in sports outcome volume, nearing the annual trading volume of some mid-sized European sports betting exchanges.
However, despite this growth, market observers suggest that traditional firms are not necessarily entering to achieve better prediction accuracy.
Harry Crane, a statistics professor at Rutgers University specializing in prediction market calibration, commented, "I don't expect the institutional capital to significantly enhance the accuracy of these markets, especially in sports. The pricing accuracy is primarily driven by specialized sports betting groups, who excel at evaluating sports outcomes."
Crane posits that firms like DRW are more likely leveraging trading strategies developed in conventional financial markets to exploit pricing discrepancies.
He explained, "If they are profitable, institutions are likely applying techniques that focus on short-term market dynamics and technical trading aspects to take advantage of transient market fluctuations without necessarily understanding the event outcomes."
In essence, DRW is not attempting to predict the Champions League winner; rather, it seeks to profit from price movements before the outcome is determined.
A recent illustration occurred in the betting market for the next prime minister of the UK. On May 14, Andy Burnham's odds surged from 24 cents to 43 cents on Polymarket amid speculation regarding a potential Labour leadership challenge. However, Betfair, a London-based betting exchange with over a billion pounds in annual volume, had already priced Burnham at the equivalent of 50 cents while Polymarket still lagged at 24 cents.
It took Polymarket several hours to adjust its prices.
For casual bettors, this discrepancy might have been a mere curiosity, but for a skilled quant trader, it represented a classic cross-market inefficiency ripe for exploitation.
Theoretically, a trader could have purchased $10,000 worth of Burnham contracts on Polymarket at 24 cents upon noticing the discrepancy and later secured $7,900 in profit within hours by selling when Polymarket's price aligned with Betfair's, achieving profit without the event needing to occur.
This method has been employed by traditional trading firms for decades: identifying mispriced assets across exchanges and engaging in arbitrage or purchasing the undervalued asset and waiting for it to appreciate.
Nonetheless, prediction markets introduce added complexity, as Betfair settles in sterling while Polymarket operates in cryptocurrency, necessitating infrastructure capable of facilitating capital movement across different currencies, exchanges, and settlement systems.
This complexity aligns well with the capabilities of large trading firms like DRW.
Factors Driving Interest
In addition to arbitrage opportunities, traders highlight two structural aspects that enhance the appeal of prediction markets today.
The first is information lag; traditional betting exchanges often react more swiftly than decentralized prediction platforms, creating brief periods where prices have yet to fully adjust.
The second is liquidity fragmentation; markets for the Champions League, NBA, and Stanley Cup may trade simultaneously across Polymarket, Kalshi, and traditional sportsbooks, meaning no single venue necessarily reflects the overall market consensus.
For traders focused on outcome forecasting rather than market structure, the tools being employed increasingly resemble those found in quantitative finance.
Soccer traders frequently utilize "Dixon-Coles Poisson" models, developed in a 1997 academic paper, to estimate team strengths and generate probability distributions for potential scorelines. This approach is akin to how meteorologists assign precise probabilities to various outcomes instead of making a single forecast.
Similarly, basketball traders often adopt "Bayesian Hierarchical" models that adjust team strength assessments as new information becomes available.
Both models aim to identify discrepancies between a model's estimated probability and the probability implied by market prices.
A trader whose model assesses Arsenal's Champions League chances at 47% while contracts trade at 43 cents may opt to buy, profiting if the market eventually aligns with that estimate.
This concept is known as closing line value, or CLV.
Crane elucidates the significance of CLV: "It incorporates all available pre-game information, such as injuries and lineup changes, and the most astute players tend to wait until closer to game time to place bets, as that's when the limits are typically highest."
Emerging Competition
Despite the influx of institutional firms, Crane remains doubtful that they will dominate the sports prediction market simply due to their larger financial resources.
"Currently, the sharpest players in the sports betting markets are not the institutions," he asserted. "The most skilled players have been entrenched in these markets for decades, and prevailing market prices are likely shaped by the same groups and information sources that existed long before prediction markets emerged."
Nevertheless, the migration of talent is already in progress.
Crypto market makers are delving into sports analytics and expected-goals models, while traditional sports betting experts are increasingly being recruited by crypto firms seeking the expertise that took years to cultivate.
This is not merely theoretical; HyperLiquid, the onchain perpetuals exchange that reached over $10 billion in daily volume at its peak, is preparing to launch prediction markets ahead of the 2026 World Cup, which will feature 64 games in six weeks and produce thousands of correlated binary outcomes.
The necessary infrastructure is being established, and trading desks are being filled with models analyzing potential outcomes.
The primary question remains whether institutions can outpace seasoned sports bettors by uncovering their edge and applying advanced trading models from traditional finance. However, competition over latency, market structure, and cross-platform inefficiencies has already commenced.
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