Prediction markets have gone from niche curiosities to regulated financial venues in the US. They let participants trade contracts that pay out based on the outcome of future events—think: “will inflation exceed 4% in Q2?”—and they do it within a legal framework that treats those contracts like exchange-traded instruments. This piece lays out how these platforms operate, what to watch for as a trader or product designer, and how regulation shapes market design and risk.

At a high level, these markets pair a simple idea with market mechanics: turn uncertainty into price. A contract that pays $1 if X happens and $0 otherwise trades at a price that reflects the market-implied probability of X. But there’s nuance—settlement rules, eligibility criteria, dispute processes, and compliance all bend the simple model into a workable product for real-world users and regulators.

Why regulation matters (and what it looks like)

Regulation isn’t just red tape. In the US, regulated platforms must satisfy federal rules that focus on investor protection, market integrity, surveillance, and clear settlement mechanisms. That means formal registration with the Commodity Futures Trading Commission (CFTC) or operating under specified exemptions, written rulebooks, trade reporting, and often real-money safeguards like margin or position limits. Platforms that secure approval are able to offer broader categories of event contracts to retail and institutional participants, and that opens up real utility—hedging, price discovery, and risk transfer.

For example, some platform providers have pursued and received formal regulatory status that lets them list event-based contracts publicly and accept deposits; others run under models with more restrictions. One such regulated example is kalshi, which sought clear regulatory standing to offer event contracts that settle to $0 or $1 based on objective outcomes. The point is: if you want scale and institutional participation, you need a compliance-first approach.

Core components of an event market

Designing a functioning event market requires getting multiple moving parts right. Here are the essentials.

Market definition: Events must be unambiguous. Who decides? The platform’s rulebook must specify the data source and tie-breakers. Ambiguity invites disputes, and disputes are expensive.

Contract structure: Most regulated venues use binary (yes/no) contracts, though categorical or scalar contracts exist. Tick size, contract denominations, and payout mechanics shape liquidity and user behavior.

Trading mechanics: Order book vs. dealer models—each has tradeoffs. Order books give transparent price formation but need enough participants. Dealer or automated market maker (AMM) designs can provide continuous liquidity but require capital and proper incentive design.

Settlement and verification: A trusted, objective settlement source is mandatory. Platforms typically pre-specify trusted official data providers (e.g., government releases, exchange indices) and timelines for finality. Clear settlement windows reduce counterparty risk.

Liquidity, incentives, and market quality

Liquidity is always the story. Thin markets mean wide spreads, slippage, and easier manipulation. To build depth, platforms use market makers (human or algorithmic), fee rebates, or bounty programs. They may also tailor tick sizes to encourage small trades or introduce minimum granularity to discourage quote flickering.

Incentives matter: overnight funding, maker-taker fees, and promotional credits can jumpstart activity, but long-term health hinges on repeat users and diverse participant types—speculators, hedgers, data consumers. Institutional users bring size and horizon, while retail users often bring nimbleness and volume. A balanced ecosystem tends to produce better price discovery.

Common trading strategies and use cases

Event markets support several coherent strategies:

• Directional betting — standard speculation on outcomes.
• Hedging — corporations or funds use contracts to offset exposure to macro or industry-specific risks.
• Arbitrage — exploiting mispricing across related markets (e.g., economic releases vs. derivatives).
• Information-based trading — participants with superior data or modeling trade on that edge.

Use cases range from corporate risk management (hedging weather, shipping delays, policy changes) to research (aggregating expectations about macro indicators) and entertainment/speculation. Each use case demands different market parameters and user protections.

Risks and mitigation

These markets aren’t magic. Manipulation risk rises when outcomes are narrow or settled on sources that can be influenced. Platforms fight this through position limits, surveillance, identity verification, and dispute resolution. Counterparty risk is reduced if the exchange is well-capitalized or uses a clearinghouse-style model. Regulatory risk is non-trivial too: rules can change, and certain event types (e.g., sports or election betting) draw special attention.

Tax and reporting considerations also matter. Depending on structure, gains may be taxed as ordinary income or capital gains; platforms and users must track and report appropriately. If you build products for institutions, assume they will demand robust reporting and audit trails.

Product design tips for founders and PMs

Start with the event definition. Make it airtight. Next, think about liquidity bootstrapping: seed market makers, offer layered incentives, and consider phased rollouts of event types. Keep the interface simple for newcomers but expose advanced order types for professional traders. Finally, bake compliance into product decisions—allowing a feature is one thing; operating it sustainably under regulatory scrutiny is another.