Prediction-market platforms' courtship of Wall Street stands to bring in deeper professional liquidity and intensify competition, but will also mean it's harder for many traders to make money.
Roughly 27% of dollar profits were captured by just 3% of accounts that are "persistently skilled," repeatedly moving market prices towards outcomes that eventually occurred, according to an academic working paper analyzing $13.76 billion of Polymarket trades.
Skilled accounts earned consistent profits by reacting more quickly to publicly available news, arbitraging inconsistent pricing across related contracts and trading against behavioral errors. But as more institutions chase the same discrepancies, prices adjust faster and the available edge becomes scarcer.
"If you have a lot of skilled people, then they compete, and in doing so, they make prices more correct," said Theis Jensen, Yale economist and co-author of the paper.
That means strategies that depend on wide spreads and straightforward arbitrage across related contracts may find it more difficult to profit.
"It's harder as markets get more efficient and spreads get tighter. It's going to be harder to find these mispricing and arbitrage opportunities," Julie Hoover, Bank of America equity research analyst, told CNBC.
As competition intensifies, Jensen expects the proportion of traders considered to have an edge to shrink from 3% to potentially below 1%.
"I think it's only going to be the very, very best — say hedge funds — that are able to beat prediction markets," he said.
Hoover, however, said smaller skilled traders could still retain an edge in niche markets, as the sheer breadth of contracts allows traders to develop highly specialized expertise and even become market makers.
Large institutions also face scale constraints in thin markets. Relatively small orders can move the price enough to "evaporate the institution's own edge", according to Jensen, making large firms less likely to enter lower-liquidity markets where specialists may retain an advantage.
Counterintuitively, the participants without a persistent edge may stand to benefit from more sophisticated competition through better pricing.
Better-calibrated prices reduce the risk that such players repeatedly overpay by taking the wrong side of pricing errors.
"In an efficient market, it's harder to make mistakes consistently," Jensen said.
He said the maturation of prediction markets could make them more of a "fair gamble": participants may still lose on any individual contract, and frequent traders remain likely to lose after transaction costs, but quoted prices should more closely reflect the risks they are taking.
While the professionalization of prediction markets come as a mixed bag to users, there's a clear benefit for the platforms. Greater institutional trading volume can expand transaction fee opportunities, while better-calibrated prices can strengthen the appeal of event contracts as hedging, forecasting and market-data tools.
Prediction markets are already seen by many as reliable. Federal Reserve researchers found that Kalshi's macroeconomic contracts matched or, in some cases, even outperformed conventional forecasting benchmarks: its headline CPI forecast outperformed the Bloomberg consensus, while its core CPI and unemployment forecasts performed on par with the market data institution.
"Everyone will start referencing the data, and then people will start trading the data," Hoover said.
Disclosure: CNBC and Kalshi have a commercial relationship that includes customer acquisition and a minority investment.
Facts Only
* Roughly 27% of dollar profits were captured by just 3% of accounts described as "persistently skilled" in Polymarket trades.
* Skilled accounts earned consistent profits by reacting quickly to public news, arbitraging inconsistent pricing across related contracts, and trading against behavioral errors.
* Competition among skilled traders makes prices more correct.
* Strategies depending on wide spreads and straightforward arbitrage may find it more difficult to profit as markets become more efficient and spreads tighten.
* The proportion of traders considered to have an edge is expected to shrink from 3% to potentially below 1%.
* Larger institutions face scale constraints in thin markets, as small orders can move prices enough to "evaporate the institution's own edge."
* Participants without a persistent edge may benefit from better pricing, reducing the risk of repeated overpayment.
* Kalshi's macroeconomic contracts matched or outperformed conventional forecasting benchmarks, such as the Bloomberg consensus for headline CPI.
* The maturation of prediction markets could lead to quoted prices more closely reflecting actual risks.
Executive Summary
Full Take
The narrative suggests a tension between increased market efficiency and trader profitability. The emergence of professional liquidity from Wall Street into prediction markets acts as a powerful force, driving price correction—a process that ultimately benefits the wider participant by reducing pricing errors. This echoes the concept that competitive friction increases systemic accuracy, implying that the pursuit of perfect pricing, even at the cost of immediate arbitrage for some actors, serves a higher equilibrium. The implication is that value shifts from exploiting ephemeral mispricings to understanding and navigating the underlying data itself; the advantage moves from tactical manipulation to fundamental insight.
The observation that large institutions are constrained by scale, while smaller players can specialize, points toward an inherent structural asymmetry where specialized knowledge remains valuable in thin markets. The potential benefit for non-skilled participants stems not from winning arbitrage, but from benefiting from a less error-prone environment. If markets become truly efficient, the risk shifts from exploiting informational gaps to managing transactional costs and acknowledging inherent uncertainty, aligning with the prediction markets' potential role as sophisticated hedging tools rather than simple betting arenas. The success of platforms like Kalshi in forecasting suggests that data-driven outcomes are achievable, which bolsters the argument that better-calibrated pricing creates a more reliable framework for all market participants.
What is the true cost when expertise shrinks below 1%? If prediction markets function as sophisticated tools for risk management, then the erosion of simple arbitrage strategies represents an evolution rather than a loss; it suggests a transition from speculative trading to data application. This raises the question: in systems driven by complex forecasting validated against real-world outcomes, is the primary utility of the market still profit extraction, or has it fundamentally shifted toward verifiable risk assessment? How does this shift redefine agency when the 'edge' migrates from quick reactions to deep contextual understanding?