Can Quant Trading Work in Prediction Markets?

PDF Version

Prediction markets have gone from crypto curiosity to mainstream financial infrastructure. On platforms such as Polymarket and Kalshi, traders can buy and sell contracts tied to real-world outcomes: elections, Federal Reserve decisions, inflation prints, weather, sports, IPOs, geopolitical events, and almost anything else that can be resolved by a clear rule. A typical event contract pays a fixed amount—often $1—if a specified event happens and $0 if it does not, so a contract trading at 63 cents can be read, roughly, as the market pricing a 63% probability. The CFTC describes these contracts as instruments that can be used both for hedging real-world risk and for speculation.

That sounds simple. But simplicity is deceptive. Prediction markets look like betting apps, trade like binary options, behave like thin derivatives markets, and increasingly attract the same quantitative trading techniques used in equities, options, sports betting, and crypto market making. The natural question is: can quant methods actually make money here, or is this just gambling with better charts?

Why prediction markets are becoming “real” financial markets

The strongest evidence that prediction markets are maturing is not the marketing—it is the infrastructure. Kalshi is listed by the CFTC as a designated contract market, with its designation dated November 3, 2020, and later amendments allowing expanded trading infrastructure. Polymarket, after a 2022 CFTC enforcement action for operating an unregistered event-contract platform, acquired QCEX in 2025, and CFTC filings now show QCX LLC doing business as Polymarket US with designated status.

This matters because regulation and market structure are prerequisites for institutional capital. In 2024, Kalshi announced that Susquehanna Government Products, part of Susquehanna International Group, would become its first dedicated institutional market maker. Kalshi described this as the first time a major Wall Street market maker had committed to an exchange focused on event contracts. Kalshi later said SIG had set up a dedicated trading division for prediction markets and would provide materially tighter spreads and deeper markets in selected contracts.

That is the first big clue: if serious market makers are willing to quote these products, then quantitative trading is not theoretical. It is already happening.

The basic quant framework: probability versus price

A prediction-market trade is, at its core, a probability trade.

If a “YES” contract costs p, and your model estimates the true probability as q, then the expected value before fees is approximately: EV=q−p

If you buy a contract at 42 cents and your model says the event has a 50% chance of happening, your gross edge is about 8 cents per contract. That is the simple version. The real version must subtract trading fees, bid-ask spread, slippage, funding cost, settlement risk, opportunity cost, and model error.

Where quant strategies may actually work

Market Making

Market making is probably the most natural quant strategy in prediction markets. Instead of trying to predict the final outcome perfectly, the trader quotes both sides, earns the spread, updates prices as new information arrives, and manages inventory. Kalshi’s onboarding of SIG is important because it shows that professional liquidity providers see enough opportunity to build dedicated infrastructure for this market.

The edge here is not “knowing the future.” It is pricing faster and more accurately than the average participant. A market maker wants to be slightly better calibrated, slightly faster to update, and disciplined enough not to get run over when private or breaking information enters the market.

Cross-venue Arbitrage

Polymarket and Kalshi often list economically similar contracts, but they do not always have the same user base, fee structure, settlement language, liquidity, or access rules. That creates potential price gaps.

A simple example: if one venue prices an event at 48% and another at 55%, a trader may try to buy the cheap side and sell or synthetically offset the expensive side. But “risk-free” arbitrage is rarely truly risk-free. The contracts may resolve differently, one venue may have worse liquidity, withdrawals may be delayed, fees may eat the spread, and the trader may not be able to short or hedge perfectly.

Still, fragmentation is a classic source of early-market alpha. Prediction markets are fragmented by regulation, geography, custody model, user base, and contract wording. Quant traders are naturally drawn to that.

This is the most intellectually interesting category. A quant trader can build models for specific event domains:

• Elections: polling averages, turnout models, demographic shifts, early-vote data, district-level correlations.

• Macro: CPI components, nowcasts, rates futures, Fed communication, labor-market surprises.

• Weather: numerical weather models, historical station data, contract-specific resolution rules.

• Sports: sportsbook odds, player injuries, in-game win probability, exchange microstructure.

• Crypto: spot, perps, funding, options, liquidation levels, exchange flows.

This is where prediction markets can look like “stocks for events.” A traditional equity trader might buy defense stocks on geopolitical risk or short a retailer before weak earnings. A prediction-market trader can instead trade the direct event: “Will X happen by date Y?”

The Federal Reserve’s 2026 paper on Kalshi macro markets is a useful example. The authors found that Kalshi offered a real-time, distribution-rich benchmark for macro expectations and that its forecasts improved on fed funds futures while performing about as well as the New York Fed’s Survey of Market Expectations for some rate forecasts.

Event Modeling

News & Latency Strategy

Prediction markets are highly sensitive to news. When a debate happens, a court ruling drops, a central banker speaks, or a war headline breaks, prices can move immediately.

That creates room for news-scraping, NLP, and automated trading. But it also creates a brutal speed game. If your “edge” is reading a headline after everyone else, it is not an edge. A quant strategy needs structured data, low-latency execution, and a clear understanding of which news is actually resolution-relevant.

Prediction markets are not always perfectly calibrated. A classic study in The Economic Journal found that prediction markets can be reasonably well calibrated near expiration but biased farther from resolution, with favorite-longshot effects: high-probability events may be underpriced and low-probability events overpriced.

Newer research also suggests that calibration varies by domain, time horizon, and platform. A 2026 arXiv paper using 292 million trades across 327,000 binary contracts on Kalshi and Polymarket found that treating market prices as face-value probabilities can systematically mislead users, depending on the domain and horizon.

For quants, this matters because persistent behavioral biases can be tradable. For casual traders, it is a warning: a 12-cent longshot is not automatically a bargain just because the payout looks large.

Calibration & Bias Harvesting

The uncomfortable truth: most users probably lose

Prediction markets are often marketed as information tools, but for traders they are also zero-sum or near-zero-sum arenas after costs. One trader’s gain usually comes from another trader’s loss. That is not necessarily bad — options, futures, and sports exchanges also work this way — but it means skill matters.

A 2026 SSRN paper studying Polymarket reports a dataset spanning more than 2.4 million users, $67 billion in volume, and 588 million trades; it finds striking profit concentration, with the top 1% of users capturing 76.5% of all trading gains. The authors argue that gains flow largely to sophisticated traders using limit orders at advantageous prices.

That finding should be sobering. Prediction markets may be democratic in access, but not necessarily democratic in profits. Retail traders can click the same buttons as professionals, but they do not have the same models, execution systems, bankroll discipline, or information pipelines.

This pattern mirrors other markets. In equities, market makers and quant funds profit from flow, speed, and risk management. In sports betting, sharp bettors and syndicates exploit stale lines while casual bettors chase narratives. Prediction markets combine both worlds.

Are prediction markets useful as forecasts?

Yes, but with caveats.

Prediction markets can be powerful because they aggregate financially backed opinions. Traders have an incentive to reveal information by buying underpriced outcomes and selling overpriced ones. The CFTC notes that prediction markets can sometimes forecast outcomes better than polls or other forecasting methods, while also emphasizing that speculation involves financial risk.

But markets are not oracles. They can be thin, manipulated, biased, slow to incorporate certain information, or distorted by whales. During the 2024 U.S. election cycle, Polymarket became a major public reference point, with election-related volume reportedly exceeding $4.7 billion, and a French trader was reported to have made tens of millions of dollars on Trump-related bets.

That episode showed both sides of the product. Prediction markets can surface alternative information faster than traditional commentary. But they can also be heavily influenced by a small number of large, sophisticated accounts.

Why quant methods may be more useful here than in mature equities

Public equity markets are extremely competitive. Thousands of professional investors model Apple, Nvidia, Tesla, or the S&P 500 every second. Prediction markets are earlier in their evolution. Many contracts are niche, contract wording is messy, liquidity is uneven, and participants range from professional market makers to casual social-media users.

That creates inefficiencies.

A quant trader may have an edge because of new contracts, fragmented markets, narrative-driven retail flow, underpriced resolution rules, uneven liquidity, diverse data sources.

However, those same features also make the market dangerous. Thin liquidity means backtests can lie. A model that looks profitable at midpoint prices may fail after spread and slippage. A contract that seems mispriced may have subtle resolution risk. A trade that looks like arbitrage may fail because the two venues define the event differently.

The biggest risks quants must also respect (1) liquidity risk, (2) fee and spread risk, (3) resolution risk, (4) regulatory risk, (5) Information risk, (6) model risk.

So, does quant trading in prediction markets really work?

It can, but not in the simplistic way people imagine.

Quant trading works when it has one or more of the following: (1) better probability estimates; (2) faster reaction to public information ; (3) superior execution; (4) better contract interpretation; (5) cross-market hedging or arbitrage; (6) market-making discipline; (7) access to differentiated data; (8) strong bankroll and risk management

It does not work just because a trader uses Python, scrapes headlines, or asks an AI model who will win an election. The edge is not the tool. The edge is the combination of data, model, execution, and market structure.

For institutional market makers, prediction markets may become a new spread-capture business. For macro funds, they may become a source of tradable signals and hedges. For small systematic traders, they may still offer niche inefficiencies in undercovered contracts. For most casual users, they are likely a difficult game where sophisticated participants capture most of the surplus.

Conclusion

Prediction markets are becoming a real financial venue, not just a novelty. The arrival of institutional market makers, regulated U.S. exchange structures, API-driven trading, and academic validation all point in the same direction: event contracts are becoming an emerging asset class.

But emerging does not mean easy.

The best way to think about Polymarket, Kalshi, and similar platforms is not “easy money from predicting the future.” It is: a young, fragmented, high-variance derivatives market where probabilities are the asset and information is the edge.

Quant methods are useful because they impose discipline on that chaos. They help traders separate price from probability, signal from narrative, and edge from entertainment. But the market is already professionalizing. The more liquidity arrives, the more obvious inefficiencies will disappear.

In prediction markets, as in equities, the future belongs not to the loudest opinion—but to the best-calibrated price.

©2026 Ryan Financial Daily