Understanding prediction markets, can be as straight forward as opening an account with a platform, or digging a bit further back into their evolution. Use the links below to skip the scroll, or start from the beginning:
What are Prediction Markets
Prediction markets are infrastructures where traders buy and sell contracts, that are tied to outcomes of future events.
Although the marketplace started with election betting (on the results), they now evolved to include almost any event that has a binary outcome (yes / no).
The contract price itself is the real-time forecast of what the market thinks will happen.
Each contract is tied to a specific question with a binary outcome (“yes” vs “no”). Example question: “Will candidate X win the 2026 election?”
Each contract is priced between 0 and 1. The current price represents the market’s implied probability.
Implied probability: That’s the overall likelihood of an outcome, based on listed odds.
Example: If a contract trades at 0.34, the market implies about a 34% chance of that event realizing.
A contract costs whatever the current market price is.
If the outcome happens, it pays out $1 for every winning contract. Note: Contracts can be sold at current price, before the outcome.
If it doesn’t, it pays $0. Knowing this payoff structure allows for direct calculation of risk and reward, like any other financial product.
Prediction Markets, from event creation to settlement
Defining the event
An event must have a clear, verifiable outcome (i.e. election results, sports outcome, weather threshold) and a deadline (market expires then resolves)
Example: “Will Bitcoin close above 100,000 on 31 Dec 2026?”.
Creating the market
A platform (can be a centralized exchange or a smart contract on a blockchain) lists the contract and allows trading. Traders see:
• YES price (implied probability)
• NO price (implicit complement = 1 – YES)
Price discovery
People buy if they think the chance is higher than the current price
and sell if lower.
• Buy at $0.40 if chance is > 40 %
• Sell if chance is < 40 %
This pushes the price to the “true” probability with new information.
Settlement
At expiry:
• Contracts that turned out true pay $1
• Others pay $0
Profit = payout – cost.
If bought at $0.40 and it settles at $1, gain is $0.60 per contract.
Practical example
Event: “Will candidate X win an election?
Contract listed:
- YES price = 0.60
- NO price = 0.40
If you think new polling data strongly favors Candidate X, you buy YES.
Other traders react, price climbs to 0.75.
Decision: You sell before resolution at 0.75, pocketing the difference.
Settlement: Bought at 0.60, sold at 0.75 = 0.15 profit per contract.
Even without waiting for the event resolution, you can trade on changing probabilities, like a financial market (think options, trading the premium before expiry)
History and evolution
Political betting has existed for centuries
Dating back to the early 16th century, when people wagered on who would become the next Pope. By the late 19th century, election betting on Wall Street was already common, with significant turnover.
The academic way (late 20th century)
A research experiment (IEM) by the University of Iowa allowed trading real-money contracts on U.S. presidential election outcomes to test whether markets could forecast real events better than traditional polls.
Early online platforms (2000s)
Betfair launched in the UK in 2000 as a peer-to-peer betting exchange. It allowed traders to bet against each other instead of against a traditional bookmaker.
Intrade launched in 2001 as an online prediction market in Ireland and became one of the most recognized early platforms. It offered real-money markets on everything from political elections to entertainment awards. Regulatory pressure from the CFTC, led to its closure in 2013.
Experimentation with the product
Not all early markets survived but they all contributed ideas on how the product could evolve.
Hollywood Stock Exchange (HSX) and Popular Science Predictions Exchange (PPX) were examples of play-money markets (aggregate opinions rather than betting real-money) where users forecasted outcomes like box office returns.
Bet2Give launched in 2017 as an online prediction market where winnings were automatically donated to charities. Explored if charitable incentives affected forecasting accuracy.
Legal and compliance constraints
Early prediction markets battled with financial and gambling regulations. In the United States restrictions or complete shut downs occurred due to federal rules against unregistered futures trading. Academic exemptions provided a cushion but not for long (see PredictIt).
Blockchain revolution from 2014
Blockchain technology enabled decentralized, trustless prediction markets. Augur launched in 2014, was the first major decentralized prediction market protocol built on Ethereum. Any user could create markets on almost any event without a centralized operator. Downsides were low liquidity, slow transaction times, and usability issues, limiting widespread adoption at first.
Growth from 2020 to present
Regulation and technology helped prediction markets to mature, diversify and expand.
Platforms like Polymarket (founded in 2020) built engaging interfaces inspired by DeFi and blockchain principles, revitalizing interest and fresh volumes. They led to billion of dollars in weekly trading volumes, with fast trades and a variety of event topics.
Kalshi (founded in 2018, launched public trading in 2021) represents prediction markets as a legit financial product, as the first fully regulated market in the United States. A 2024 legal victory allowed regulated markets on events like elections.
Nuts and bolts of Prediction Markets
1. Market construct from contract to settlement
The most common contract type prediction markets is the binary yes/no:
- A contract resolves to $1 if the event happens and $0 if not.
- The current price reflects probability (0.60 means 60% chance).
Example: Market: “Will ETH close above 4,000 on Dec 31?”
- YES price: 0.45 | NO price: 0.55
- If you think ETH has >45 % chance, you might buy YES.
Other contract types:
- Categorical/multi-outcome with more than two outcomes.
- Scalar with outcomes along a continuous numeric range (price levels).
Clear rules matter
Markets must have clearly defined outcomes and verification rules (how the event result is determined and by which data source). Without this, settlement disputes can arise.
2. Order books & market makers
Prediction markets borrow from financial market mechanics but adapt to their unique payoff structures.
Order book trading
Many real-world platforms like Polymarket, Kalshi or PredictIt, use a Central Limit Order Book (CLOB):
- Traders submit limit orders to buy or sell at specific prices.
- The order book matches buyers and sellers.
- Price movements reflect what probability users attach to an event outcome.
Supply and demand
If most traders believe an outcome is more likely, they bid up the price by placing buy orders at higher levels until sellers meet those bids.
When you place a buy order on “YES at 0.20,” the platform’s interface automatically shows the inverse “sell NO at 0.80,” because 1 – 0.20 = 0.80. This reflects the idea that YES + NO must sum to full probability (100 %).
3. Automated Market Makers (AMMs)
Some platforms use algorithms similar to DeFi (AMMs):
- AMMs provide constant liquidity even with few traders.
Prices adjust automatically based on how much of each outcome is held in the market “pool”. - This is similar to how token swaps work on decentralized exchanges: the pricing curve shifts as traders buy or sell outcome tokens.
Characteristics of AMMs:
Price always available: Easier for beginners but can swing wildly with small trades
No need to match opposite orders: Limited liquidity risk but can be costly for traders
A specialized AMM called a logarithmic market scoring rule (LMSR) has been designed to handle event-linked outcomes and encourage liquidity even when few traders are active.
4. Price interpretation
In prediction markets, prices are probability signals:
- A contract priced at $0.70 implies a 70% chance of the event happening.
- Traders interpret this price as the collective forecast of all users.
Unlike fixed-odds betting, where odds are set by a bookmaker, prediction markets’ prices emerge from trades.
Trading before resolution
You can enter or exit positions before the outcome settles with profit or loss if your view changes or new information arrives.
5. Liquidity
Liquidity is important for effective prediction markets.
Low liquidity can cause:
- Wider spreads
- Slow price formation (slow response to new information)
- Poor predictive power (prices don’t reflect beliefs well)
Platforms often address this by:
- Incentivizing liquidity providers with fees or rewards.
- Using AMMs to always provide liquidity.
6. Settlement
At the end of an event, the settlement engine reads the real-world outcome according to pre-defined rules.
Winning contracts settle at $1; losing ones at $0 and traders are paid (or not).
Smart contract-based markets (blockchain) automate this process, reducing counterparty risk, while centralized markets rely on trusted data sources.
7. Event walkthrough
Event: “Will GDP grow more than 1% this quarter?”
- YES price: 0.56
- NO price: 0.44
Your view: You think strong data will be published soon, so YES is undervalued.
Your decision: Buy 100 YES contracts at 0.56 at a cost of 56.
A news release boosts confidence and YES is now at 0.78
You sell your 100 YES at 0.78 and made 78. Your realized profit is 78 – 56 = 22
Notice: You did not wait for settlement. You traded based on the probability change.
8. Variations
Multi-Outcome Markets
Some markets aren’t binary and they price other possible outcomes (candidate X wins vs Y vs Z). They use similar structures but require careful price normalization across outcomes.
Blockchain vs Centralized
- Centralized platforms handle order books and matching on their backend, with users interacting through the platform.
- Blockchain protocols let markets be created and resolved in smart contracts, improving transparency and reducing platform risk.
Trading platforms
Prediction markets fall into three major categories, that serve different users and purposes.
Regulated centralized exchanges
Kalshi offers markets on economics (inflation, GDP), politics, sports, climate, policy. It also used promotions to raise public awareness of prediction markets as financial tools.
Decentralized blockchain protocols
Polymarket offers markets on politics, economics, crypto, pop culture, sports. High volumes, reported in the tens of billions by the mid-2020s. The open nature enables anyone to suggest and create markets.
Academic or community markets
PredictIt is used by political enthusiasts and researchers to gauge probabilities with real stakes. Manifold remains useful as a learning, community forecasting, and experimentation platform better suited for beginners before risking real funds.
Prediction markets tech is being integrated into broader financial / social systems
Robinhood, Webull, Interactive Brokers and others have launched prediction market hubs, leveraging existing user bases.
Aggregators like TradeFox aim to combine markets across platforms and provide professional features like advanced order types and filtering.
Sports-focused decentralized protocols like Azuro, focus on niche verticals with shared liquidity across apps.
Practical usage by the user’s type, from retail to institutional and beyond
Retail traders use platforms like Polymarket or Kalshi to trade probabilities on upcoming elections, economic data, or sports outcomes.
Institutional and risk hedgers participate to hedge actual business risk like inflation or policy forecasts.
Data Consumers (non-traders), consume market probabilities. Financial news and analytics tools now integrate prediction market prices into dashboards.
Platform comparison
differences, strengths and weaknesses
| Platform type | Launched | Markets offered | Settlement | Regulatory status | Strengths | Weaknesses |
|---|---|---|---|---|---|---|
| Kalshi | 2021 | Economics, politics, sports, climate, policy | USD | CFTC-regulated | Legal in U.S., deep liquidity | KYC/AML, limited to U.S. |
| Polymarket | 2020 | Politics, economics, crypto, pop culture, sports | USDC crypto | Semi-decentralized | Global, high volume, open | Historical regulatory friction |
| Augur | 2018 | Users can create custom markets for almost any event | Crypto | Fully decentralized | Permissionless markets | Low liquidity, complexity |
| PredictIt | 2014 | US policy (law/lobbying or policy outcomes) | USD | Academic (under regulatory exemptions) | Political forecasting focus | Limited market types |
| Manifold | 2021 | Users can create custom markets on almost any topic, including personal, scientific, or niche interests | Play-money (virtual currency called Mana) | Unregulated (learning, community forecasting, and experimentation platform) | Safe experimentation | No real money rewards |
Some stats to understand the evolution of Prediction Markets
- 2025 prediction markets collectively traded over $50 billion in volume.
- Early 2026 continued with weekly notional volume surpassing $5 billion on major platforms.
- Kalshi and Polymarket account for 97% of total volume as of 2025.
- The number of users expanded from 4 million in 2024 to about 15 million in 2025.
- Sports (NFL, Super Bowl, March Madness) and elections remain core, with crypto, economics, science & culture growing.
- Deeper liquidity is growing, but fragmentation remains a technical and economic challenge (inconsistent pricing across venues).
- Forecasts show potential of over $100 billion+ in annualized volume with further institutional adoption and regulatory maturation.
Where else are Prediction Market prices used for?
Financial research and news analysis
Risk modeling in finance
Public policy discussions
Sentiment tracking other than polls
Are Prediction Markets the next trading frontier?
Lets find out together
Compliance and legal considerations
Prediction markets sit at the crossroads of gambling law, financial derivatives regulation, and consumer protection frameworks. This creates significant real-world complexity and ongoing legal debate.
US: Federal vs. State regulation
Prediction markets can be regulated as derivatives under the Commodity Exchange Act, overseen by the Commodity Futures Trading Commission (CFTC). Platforms like Kalshi use this regime to operate nationally.
State regulators argue that certain markets (especially on sports outcomes) are the equivalent of gambling and must comply with state gaming laws (licenses, age limits, taxes).
The CFTC is working on clearer event contract standards, signaling a more proactive federal approach to regulatory uncertainty.
International: Many countries restrict prediction markets under gambling law or financial securities rules. Some specifically ban markets on sensitive events like elections.
Legal risks for platforms and traders
Consumer protection / compliance obligations: Operators may be required under CFTC and other regimes to monitor and prevent fraudulent activities, abusive trading practices, and market manipulation. If not, they face civil liabilities and enforcement actions.
Gambling classification and licensing: In many states or countries, prediction markets especially on sports, are treated as gambling unless the operator holds the appropriate license, fees, and compliance infrastructure.
Taxation and enforcement: If deemed gambling rather than financial trading, platforms and participants could be subject to gaming taxes, licensing fees, or penalties.
Important: Even if a platform enables trading, you remain subject to local laws about online gambling and financial trading. Violating those laws could have personal legal consequences.
Concerns that matter for legitimacy, investor risk and social impact.
Insider trading: Traders with non-public knowledge about an event (political decisions, corporate actions, military operations) might profit unfairly (same as insider trading in the financial markets)
Market manipulation and wash trading: Smaller or less liquid markets are vulnerable to price distortions if wealthy traders “whales” place large orders that shift probabilities beyond what public information justifies. This undermines price accuracy / investor trust.
Additional considerations: Underage participation, gambling addiction, misunderstanding of risks, unknowingly violating rules/laws (online gambling, financial trading, taxes) that could have personal legal consequences.
Sensitive or harmful topics
Is it ethical to trade on violent outcomes, policy decisions affecting lives, or geopolitical crises? Some argue that markets create perverse incentives (financial benefit from conflict).
Transparency & information integrity
Anonymity obscures who holds large positions or whether trades are aligned with public information. This questions who truly shapes the implied probability (concentraded power)
Prediction Markets vs other forecasting systems
Why compare forecasting methods?
Prediction markets bring a price-as-probability mechanism rooted in real incentives. How? Expecting a reward for an accurate forecast, incentivizes the trader to aquire knowledge, maintain relevance with the subject, and be up to date with news.
Other methods use statistical sampling, expert judgment, or algorithmic prediction from data.
Forecasting tools vary along several dimensions that matter in practice:
Accuracy: How often forecasts match real outcomes
Timeliness: How quickly predictions incorporate new information
Cost / scalability: Resource requirements to produce forecasts
Bias / noise: Systematic distortions in the signal
Interpretability: Ease of understanding and using the forecast
“Who will you vote for?”. Polls ask people questions, then estimate likely outcomes based on sampled opinions. They measure current sentiment as a vote share.
Statistics show markets outperform polls in 93–100% of cases, with 3-10% better accuracy (Taiwan elections).
Experts draw on field knowledge, models, and historical patterns to make forecasts. These can be valuable in stable settings where historical patterns hold.
Markets outperform due to varied independent judgments and financial incentives.
Regression models, time-series models, and other methods that use historical data to forecast. Can uncover patterns, but slow to react to new information.
Markets outperform when “now” matters. Prices reflect judgements beyond past data.
AI models like neural networks and ensemble systems, can generate forecasts from complex patterns in data. Often require huge training data but can scale.
Improved accuracy in hybrid systems combining human judgment & machine forecasts.
Many new prop trading firms choose to partner with a broker for a faster, turnkey launch. Industry leaders offer dedicated services for prop trading companies, including grey-labeled trading platforms, integrated liquidity, CRM, and even risk management tools. On the downside… If you are considering going into this form of “grey label” to direct your business, there are some costs that you may or may not run into.
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