Event-Driven Trading

Positioning around scheduled information releases in prediction markets

What is event-driven trading?

Event-driven trading involves taking positions in anticipation of, or in reaction to, specific scheduled events that will resolve uncertainty in a market. In traditional finance, this includes earnings announcements, central bank decisions, and regulatory rulings. In prediction markets, the concept maps directly to Kalshi's event-based contract structure.

The core premise: markets reprice around information releases. If a trader has a well-informed view of the likely outcome before the information is public, they can position ahead of the repricing.

Types of events on Kalshi

Kalshi lists markets tied to many different types of scheduled events:

Economic data releases

Monthly jobs reports (BLS), CPI inflation data, GDP estimates, and other economic indicators are released on known schedules. Kalshi lists markets like "Will the unemployment rate be above X%?" or "Will CPI exceed Y%?" These contracts see significant price movement at the exact moment the data is released.

Central bank decisions

Federal Reserve (FOMC) meetings occur on a published schedule. Markets on interest rate decisions, rate cut/hike probabilities, and forward guidance are among the most actively traded on Kalshi. The CME FedWatch tool, which derives probabilities from futures markets, provides a benchmark for comparison.

Election results

Election night provides a sequence of scheduled information releases (state-by-state results) that progressively resolve uncertainty. Prediction markets for elections tend to be among the most liquid on Kalshi.

Weather events

While weather is continuous, specific observation times (daily high, daily low, daily precipitation totals) create discrete resolution points. Forecast updates from the NWS throughout the day also serve as information events.

Pre-event vs. post-event strategies

Pre-event positioning

A trader takes a position before the event, based on their view of the likely outcome. This requires forming an independent estimate that differs from the market price. Sources of edge include:

• Forecasting models — Economic nowcasting models (e.g., Atlanta Fed GDPNow) may provide more timely estimates than what the market has priced in.
• Leading indicators — For employment data, indicators like initial jobless claims, ADP private payrolls, and ISM employment sub-indices are released before the official BLS report.
• Consensus analysis — The market price may lag behind the latest economist consensus if it was established during a low-volume period.

Post-event reaction

Some traders focus on how markets react immediately after an event. If the initial price reaction overshoots (e.g., a slightly-worse-than-expected jobs report causes a 15-cent drop in a related market), there may be an opportunity to fade the overreaction. This overlaps with the mean reversion strategy.

The information cascade

Prediction market prices near events can exhibit cascade behavior. Bikhchandani, Hirshleifer, and Welch (1992) describe information cascades as situations where participants ignore their private information and follow the crowd. In liquid prediction markets, this is generally self-correcting — but in thin markets near settlement, a few large orders can temporarily push prices away from fair value.

Timing and liquidity

Event-driven markets on Kalshi follow a predictable liquidity pattern:

• Days before the event — Moderate liquidity, wide spreads, prices reflect general expectations
• Hours before — Liquidity increases as active traders position themselves, spreads tighten
• Minutes before — Peak liquidity and tightest spreads, prices reflect the latest consensus
• At the event — Rapid repricing as the outcome becomes known; liquidity may temporarily evaporate
• After settlement — Contracts resolve and capital is freed for the next event

Key considerations

• Known unknowns vs. unknown unknowns — Event-driven trading works best when the possible outcomes are well-defined and the question is which one occurs. Surprise events (geopolitical shocks, unscheduled announcements) are harder to position for.
• Binary resolution removes nuance — A jobs report that comes in at 180K vs. the 175K consensus is a meaningful miss in equities but may resolve the same YES/NO bracket on Kalshi. The binary structure collapses continuous outcomes into discrete buckets.
• Timing risk — Some events occur at exact known times (FOMC announcements at 2pm ET); others have wider windows (BLS reports at 8:30am ET but may leak or be delayed). Positioning too early ties up capital; positioning too late means the price already reflects the information.
• Calendar concentration — Major economic releases cluster around certain days of the month. A purely event-driven strategy may have periods of high activity followed by quiet stretches.