Between January and October 2025, prediction markets generated over $27.9 billion in trading volume, with weekly peaks hitting $2.3 billion. These aren’t fringe political forecasting tools anymore. They’re mainstream financial platforms where people trade billions on everything from elections to Supreme sneaker releases.
If you’re evaluating event-driven finance opportunities, you need to understand that prediction markets are financial instruments, not gambling platforms. The regulatory distinction matters. The infrastructure requirements matter. And the expansion beyond politics into consumer products—like Kalshi’s StockX partnership that lets you trade on sneaker prices—shows this space is moving fast.
This article is part of our comprehensive guide to prediction markets, covering the fundamentals: event contracts, price discovery, market-implied probability, and revenue models. You’ll get the conceptual foundation you need before you evaluate platforms, APIs, or start building your own implementations.
What Are Prediction Markets and How Do They Differ from Sports Betting?
Prediction markets are CFTC-regulated financial instruments where participants trade event contracts on real-world outcomes. That regulatory difference is everything. Prediction markets operate under CFTC oversight as Designated Contract Markets. Sports betting falls under state gaming commissions.
Event contracts are swaps that provide payment dependent on occurrence or non-occurrence of events with commercial, financial, or economic consequences. They’re regulated financial derivatives with price discovery mechanisms, not fixed-odds wagers set by bookmakers.
What’s the key difference? Prediction markets aggregate information through trading to reveal consensus probability estimates. Betting relies on bookmaker odds.
Kalshi received CFTC approval in 2020 to operate as a regulated exchange—the first legal prediction market exchange in the US. Any contract listed has to be certified by the CFTC first.
Binary contracts work like this: Event contracts pay $1 per contract for correct predictions and $0 for incorrect ones. You’re trading with other traders, not the platform itself.
Take Kalshi’s StockX partnership. It allows trading on sneaker price contracts—regulated event contracts, not bets. Greg Schwartz, StockX CEO, said: “As the marketplace that turned sneakers into an asset class, it’s only fitting that StockX would partner with Kalshi as it expands into the world of current culture collectibles.”
From a technical perspective, prediction markets need order matching engines, settlement systems, and outcome verification infrastructure that’s completely different from gambling platforms. This regulatory framework gives them legitimacy separate from gambling.
How Do Event Contracts Work in Prediction Markets?
Event contracts are binary Yes/No positions on specific outcomes with prices ranging from $0.01 to $1.00. You buy Yes contracts if you think the event will happen, or No contracts if you don’t. Winning contracts pay exactly $1.00 when the market resolves. Losing contracts expire worthless.
The math is simple. An event contract has a nominal value—often $1—and traders can buy “yes” or “no” positions for some fraction of that value. If you buy “yes” positions on 1,000 contracts for 25 cents each and the event occurs, you earn $1 per contract—that’s a $1,000 return on your $250 investment.
Binary contracts settle at $1 if the event happens and $0 if it doesn’t. Since most contracts pay $1 when the event occurs and $0 when it doesn’t, the price directly represents probability. A contract at $0.63 means a 63% chance of the outcome happening.
The mechanism enforces one simple rule: YES + NO = $1.00. When opposing orders match, the exchange collects $1.00 in collateral and distributes positions. Buy a Yes contract at $0.70, you’re paying 70 cents. If the event occurs, you receive $1 (that’s a 30-cent profit). If it doesn’t, you lose the 70 cents.
Here’s a real example. You buy 100 Yes contracts on a StockX Supreme Box Logo Hoodie at $0.65 ($65 cost). If the Supreme hoodie exceeds the threshold on StockX, you receive a $100 payout ($35 profit). If it doesn’t, you lose $65.
Markets resolve based on verifiable real-world outcomes: election results, StockX verified prices, weather data. Settlement is automated once the outcome source confirms results.
How Do Prediction Markets Achieve Price Discovery and Market-Implied Probability?
Trading activity aggregates diverse information into a single consensus probability reflected in contract price. A contract at $0.70 implies the market estimates a 70% probability the event occurs. Traders with superior information buy underpriced contracts or sell overpriced ones, moving prices toward true probability. Prices adjust in real-time as new information emerges. For a deeper exploration of market mechanics and price discovery, we cover the operational details that enable these probability signals.
Contract prices act as real-time probability estimates that aggregate diverse information. Because markets aggregate thousands of views, their signals can outperform polls or pundits.
The efficiency mechanism is economic. Traders with informational edge can immediately profit by buying underpriced or selling overpriced contracts. This ensures prices rapidly converge to true probabilities, particularly during volatile periods.
Prediction market prices are very close to the mean belief of market participants if traders are risk-averse and beliefs are spread out. As trades occur, the market converts everyone’s inputs into a single number that updates constantly.
Different participant types bring distinct value. Domain experts provide deep subject knowledge, data quants offer model-based signals, news arbitrageurs react fast to breaking information, and general traders contribute broad sentiment.
Example: “Will Pokémon Charizard cards average above $500 on StockX in Q2 2026?” starts at $0.50 (50% probability). A new partnership announcement moves the price to $0.72 (72% probability) as traders reassess likelihood.
Real-world applications extend beyond politics. Corporate planning uses product launch predictions. Risk management leverages weather events affecting supply chains. Financial services use regulatory decision probabilities.
How Accurate Are Prediction Markets Compared to Traditional Polling?
Prediction markets have historically outperformed traditional polling in political forecasting. Markets aggregate diverse information including polling data, insider knowledge, and real-time developments. Polls capture single snapshots. Traders profit from identifying errors, creating an incentive for accuracy. But prediction markets can be manipulated with sufficient capital, particularly in low-liquidity markets.
Polymarket was superior to polling in predicting the 2024 presidential election, particularly in swing states. Polymarket had Trump winning at 95% before midnight election day, several hours before the Associated Press called it. Polymarket forecasts respond much more dynamically to events than polling data—after the Pennsylvania assassination attempt, Trump’s odds shot up in Polymarket while polling remained unchanged.
Why do markets outperform polls? Prediction markets leverage liquidity and informed traders to minimise subjective biases and narrative distortion.
But there are limitations. For events further in time (elections more than a year away), prices bias toward 50% due to traders’ “time preferences”—their unwillingness to lock funds long-term.
Research found that similar markets were “not only consistently priced differently, but also that changes in daily closing prices were largely unrelated,” suggesting activity was based on “within-market pricing dynamics rather than reaction to new political information.”
Low liquidity creates vulnerability. Thin markets produce volatile prices. Insider trading concerns emerged with suspicious trading patterns before events.
If you’re building applications that leverage prediction market forecasts, you need liquidity thresholds—minimum trading volume requirements—to ensure reliability.
How Do Prediction Markets Generate Revenue?
Transaction fees charged as a percentage of trade volume (typically 1-5%) form the primary revenue model. Market maker spreads provide secondary revenue—the platform provides liquidity and profits from the bid-ask spread difference. Data licensing generates tertiary revenue—selling real-time probability feeds to enterprises, media, and researchers. Emerging models include tokenisation layers like DFlow charging API access fees.
Prediction market companies earn revenue through transaction fees. Some platforms charge as little as $0.01 per contract, others take a cut of profits.
Kalshi employs variable fee schedules: approximately 0.6% for tail events to 1.75% at mid-market pricing. Polymarket charges no trading fees on its primary platform.
The volume justifies the model. Trading on prediction markets exceeded $3.6 billion during the week of Nov. 10, primarily on Kalshi and Polymarket.
Data licensing represents growing revenue potential. Media organisations, corporations, and researchers pay for market-implied probabilities. Traditional finance’s growing interest reflects recognition that event data has matured into a monetisable asset class.
If you’re building prediction market features, understanding unit economics matters. You need to weigh transaction processing costs, settlement infrastructure costs, and oracle verification costs against fee revenue potential.
What Real-World Applications Are Emerging Beyond Politics?
The Kalshi-StockX partnership enables prediction markets on sneaker prices (Jordan, Supreme), collectibles (Pokémon cards), and designer goods—expanding beyond politics into everyday consumer culture. Corporate applications include internal prediction markets for project forecasting. Financial markets leverage event-driven finance for hedging around regulatory decisions and earnings announcements. Entertainment sees trading on award shows and box office performance.
StockX and Kalshi announced a strategic collaboration enabling event contract trading tied to sneaker releases and collectibles. This marks Kalshi’s first foray into product-based event contracts.
The markets span three categories: Top-Traded Brands During Major Events, Average Sales Prices For Upcoming Product Releases, and Monthly Average Sales Prices For Top-Selling Products.
Featured items include Jordan sneakers like the Jordan 8 Retro “Bugs Bunny”, Supreme products, Pokémon Mega Evolution Charizard X, Pop Mart Labubu collectibles, and the New Balance 204L “Mushroom Arid Stone.”
The expansion logic is sound. Physical collectibles have hardcore, data-obsessed communities, and until now there’s never been a liquid global marketplace to price them. Adults turned collectible toys into a $7 billion industry in the US.
Tarek Mansour, Kalshi CEO, said: “Sneaker, apparel, and collectible drops on StockX have become defining cultural moments with clear, measurable outcomes—the very kind of real-world events Kalshi was built for.”
The collaboration introduces event contracts based on aggregated StockX data, allowing traders to speculate on measurable outcomes without owning physical assets.
Here’s a practical use case. A sneaker reseller owns 50 pairs of Jordan 11s bought at $180 each. They buy No contracts betting the price stays below $200. This protects against price decline—it’s hedging inventory risk.
If you’re building industry-specific prediction markets, you’ll need integration with domain-specific data sources: sports stats APIs, box office tracking, retail price indices.
What Technical Infrastructure Supports Prediction Markets?
Core components include an order matching engine (CLOB or AMM), settlement system, oracle integration for outcome verification, and market surveillance for compliance. Platform architectures split between centralised (Kalshi using traditional exchange infrastructure) and decentralised (Polymarket using blockchain settlement). For a detailed platform architecture comparison, we analyse the technical trade-offs between these approaches. Developer integration layers include REST APIs for trading and WebSocket feeds for real-time prices. Operational requirements cover KYC systems, regulatory reporting, and liquidity provision.
Prediction markets operate as fully-collateralised binary options on central limit order books, with the mechanism enforcing YES + NO = $1.00.
Polymarket uses a hybrid model: off-chain order matching with on-chain settlement via Polygon. Kalshi operates with fiat USD settlement and self-certifying outcomes.
Kalshi is a CFTC-regulated U.S. exchange trading event contracts in USD, while Polymarket operates as a crypto platform using USDC. Polymarket relies on Ethereum-based smart contracts to record trades transparently and automate settlement.
Market surveillance systems monitor for manipulation patterns. Real-time monitoring prevents insider trading. Anomaly detection algorithms flag suspicious activity. These compliance systems are required infrastructure for CFTC-regulated platforms.
DFlow adds a composability layer. DFlow is the most powerful trading infrastructure on Solana, enabling applications to access financial markets. The DFlow Prediction Markets API gives builders programmatic access to tokenised Kalshi markets on Solana.
Once a prediction becomes an SPL token on Solana, it gains DeFi composability: it can be borrowed, lent, swapped, or collateralised. Kalshi is backing the ecosystem with a $2M grants program to fund new applications.
Centralised platforms offer lower latency and fiat settlement. Decentralised platforms enable permissionless innovation and DeFi composability. Your choice depends on regulatory risk tolerance and performance requirements.
How to Evaluate Prediction Market Opportunities
Start with regulatory risk tolerance. CFTC-regulated Kalshi provides compliance certainty. Unregulated alternatives carry jurisdictional uncertainty. Your organisation’s risk appetite determines which platforms are viable.
Technical requirements matter. Real-time feeds, historical data, settlement integration capabilities vary by platform. API quality—documentation, SDKs, uptime—affects development velocity.
Use case alignment is fundamental. Do the available markets match your data needs? Breadth of market coverage determines whether prediction market data can support your application.
The build vs integrate decision breaks down simply. API integration has subscription costs and time-to-market considerations. Custom development involves infrastructure costs, regulatory compliance burden, and ongoing maintenance overhead.
Here’s your integration decision tree: Need CFTC compliance? Go with Kalshi API. Need DeFi composability? Use DFlow or Polymarket. Need custom markets? Build smart contracts. Need enterprise features? Pursue a platform partnership.
Your risk assessment checklist covers multiple dimensions. Regulatory compliance (CFTC status, state restrictions). Market integrity (manipulation prevention, insider trading controls). Technical reliability (API uptime, settlement finality). Liquidity depth (minimum volume thresholds for reliable signals).
Think about prediction markets strategically—focus on probability signals for decision-support systems rather than trading mechanics. Probability feeds serve as data infrastructure components.
Your next steps depend on organisational needs. For a comprehensive prediction markets overview covering all aspects of this emerging space, explore the full landscape. Dive into comparing Kalshi and Polymarket platforms for architectural evaluation. Explore API integration for build-vs-buy analysis. Understand compliance requirements for regulatory assessment.
Long-term considerations shape strategic positioning. Prediction market maturity shows expansion from political forecasting to mainstream financial infrastructure. Institutional adoption trends indicate growing enterprise use cases.
FAQ Section
What is the minimum investment required to trade on prediction markets?
Most CFTC-regulated platforms like Kalshi allow trading with as little as $10-$25 minimum deposit. Individual event contracts can be purchased for as little as $0.01 per contract. For enterprise integrations, API access and data licensing may have separate minimum commitments depending on usage volume.
Can prediction markets be used for hedging real-world risks?
Yes. The Kalshi-StockX partnership demonstrates this: sneaker resellers can hedge inventory price risk by taking opposite positions in prediction markets. Similarly, corporations can hedge regulatory approval risk, weather-related supply chain disruptions, or competitive product launch outcomes. The key requirement is availability of relevant event contracts with sufficient liquidity.
Are prediction markets legal in Australia?
Australian prediction markets face a complex regulatory landscape. While Kalshi operates legally in the United States under CFTC regulation, it doesn’t currently serve Australian customers. You should consult legal counsel regarding jurisdiction-specific compliance requirements before integration or development efforts.
How do prediction markets prevent insider trading?
CFTC-regulated platforms implement restricted lists preventing individuals with material non-public information from trading on events they can influence. Athletes can’t trade on their own performance. Corporate executives can’t trade on earnings before public release. Technical implementation includes identity verification during KYC onboarding and automated trading restrictions based on user role classifications.
What happens if a prediction market event becomes ambiguous or disputed?
Resolution mechanisms vary by platform. Centralised platforms like Kalshi use internal verification teams applying predefined resolution rules based on authoritative data sources (StockX verified prices, official election results). Decentralised platforms like Polymarket use optimistic oracles with dispute periods where participants can challenge proposed resolutions by posting bonds. If disputes can’t be resolved, markets may be voided and positions refunded.
Can prediction markets manipulate real-world outcomes?
This concern applies to low-liquidity markets where concentrated trading could create misleading probability signals that influence decision-makers. Historical examples include suspicious trading patterns before events and feedback loops where published market probabilities shape participant behaviour. Platforms mitigate this through liquidity thresholds, surveillance systems, and market integrity monitoring.
How volatile are prediction market prices compared to traditional financial instruments?
Prediction market volatility depends on liquidity depth and information flow. High-liquidity political markets show relatively stable prices with short-term spikes following major news. Low-liquidity niche markets can exhibit higher volatility due to thin order books. Unlike traditional securities, prediction markets have bounded price ranges ($0-$1), limiting absolute volatility but enabling large percentage swings.
What developer skills are required to integrate prediction market APIs?
Integrating Kalshi or DFlow APIs requires standard web development skills: RESTful API consumption, WebSocket handling for real-time feeds, authentication best practices, and basic financial data processing. For blockchain-based integrations (DFlow/Polymarket), you’ll also need Solana or Ethereum development experience, wallet connectivity, transaction signing, and smart contract interaction patterns.
How do prediction markets compare to betting exchanges like Betfair?
Prediction markets and betting exchanges share order-book mechanics enabling peer-to-peer trading, but differ fundamentally in regulatory status and purpose. Prediction markets operate as CFTC-regulated financial derivatives designed for information aggregation and risk management. Betting exchanges operate under gaming regulations focused on entertainment wagering. Technical architectures are similar, but compliance requirements and permitted event types differ significantly.
What is the future outlook for prediction market adoption?
Industry trajectories suggest expansion from political forecasting into mainstream financial infrastructure. Key growth drivers include institutional adoption for corporate forecasting, vertical expansion (the StockX partnership demonstrates consumer products), regulatory maturation, and technological advancement (DeFi composability, tokenisation layers). Projections reflect potential integration into enterprise decision-support systems and risk management platforms.
