Regulation across countries
Regulation remains a major obstacle to wider use of prediction markets. Many jurisdictions have treated them as gambling. Regulators are also considering their use as hedging tools and economic indicators.
In the United States, the Commodity Futures Trading Commission (CFTC) regulates exchanges such as Kalshi. In the EU, the Markets in Crypto-Assets (MiCA) regulation covers certain crypto-assets and services, but excludes assets that qualify as financial instruments. MiCA is not a blanket authorisation for prediction markets.
AI and automated forecasting
Large language models (LLMs) and specialized AI agents could help process news and compare forecasts. Their usefulness depends on data quality, evaluation, and checks for errors.
- Trading bots can keep buying and selling in niche markets that would otherwise see little activity.
- Automated systems can scan multiple sources, but speed alone does not establish forecasting accuracy.
- AI could help review evidence for settlement, but subjective criteria still need clear rules and a way to resolve disputes.
Decentralized trading
Blockchain-based prediction markets can use smart contracts to hold funds and settle trades. This changes custody arrangements but does not remove smart-contract, oracle, governance, or access risks.
Liquidity pools can connect participants across markets. Actual access and costs depend on the platform, network, and applicable rules.
How institutions use market data
Market prices can be one input to scenario planning. A hypothetical 90% implied chance of a regulation passing might prompt an analyst to examine affected businesses, but the price does not establish whether the event will happen.
Research has examined internal prediction markets at Google and Ford. Such markets can collect employee forecasts, including views that differ from the official plan, but participation and incentives affect their usefulness.
Manipulation and public opinion
Someone with enough money could try to move a market's price to influence public opinion. Other traders have a financial reason to trade against a price they think is wrong. That incentive can help correct distorted prices, though manipulation remains a risk.
Following the odds
Prediction market probabilities can be compared with other forecasts. To assess their usefulness, track calibration over time and check liquidity, settlement rules, and the evidence behind a price.
Sources and further reading
- Research: corporate prediction markets
- CFTC: Kalshi exchange registration
- ESMA: MiCA Article 2, scope and exclusions
- Polymarket: custody and platform rules
Read next: How prediction markets work · Compare platforms · Trading and risk management