Time-to-event
Time-to-event is a simple idea: how far away are we from the moment that matters. That moment could be an earnings announcement, an election result, a data release, or the resolution time of a prediction market. When people talk about signals “getting stronger” as an event approaches, they are usually talking about changes that relate to time-to-event.
In many event-based datasets, you have a start time, an expected end time, and sometimes multiple milestones in between. Time-to-event is calculated by taking the current timestamp and subtracting it from the event’s target time. It can be expressed in minutes, hours, or days, depending on how fast the market or system moves.
Time-to-event is useful because behavior often changes as the clock runs down. Liquidity can increase, volatility can shift, and attention can rise as more people focus on the same deadline. Even when the topic stays the same, the market’s sensitivity to new information may change depending on how close it is to the final moment.
For analysis, time-to-event creates a common axis for comparison. Instead of aligning markets by calendar date, you align them by “days before resolution” or “hours before the announcement.” That makes patterns easier to see across many different events. It also helps when building features for models, because the same signal may have different meaning when the event is far away versus when it is imminent.
Why It Matters
Time-to-event helps you interpret signals in the right context, especially when markets behave differently as deadlines approach. It also makes it easier to compare different events on a consistent timeline.
How do you calculate time-to-event in an event dataset?
You need a well-defined event timestamp, such as a scheduled start time or resolution time. Then you subtract the current time (or the observation time) from that event time. It’s important to use consistent time zones and clear definitions, because “event time” can mean different milestones. When datasets include multiple milestones, analysts often choose the one tied to outcome confirmation.
How does time-to-event affect probabilities or prices in event markets?
As resolution approaches, markets often incorporate information faster because the uncertainty window is shrinking. Late-breaking news can cause sharper moves because there is less time for gradual adjustment. Some events show slow drift early and more volatile updates near the end. Tracking time-to-event helps separate “normal tightening” from unusual shocks.
Why is time-to-event useful for backtesting event-focused strategies?
Backtests are easier to compare when you align observations relative to the event, not by calendar date. This lets you test questions like “What happens 24 hours before resolution across many markets?” It also helps you avoid mixing different phases of the event lifecycle. Strategies can look profitable if phases are mixed incorrectly, so time alignment improves validity.
Real-World Example
An analyst compares 200 prediction markets by looking at implied probabilities at 7 days, 2 days, and 6 hours before resolution. They discover that many markets become much more volatile in the final 12 hours.
Time-to-event and Prediction Market API
FinFeedAPI’s Prediction Market API is relevant because prediction markets are naturally organized around time-bound events that resolve on a schedule. Time-to-event helps developers align probability histories, build alerts as deadlines approach, and analyze how markets behave in the run-up to resolution. It’s a practical feature for research and monitoring tools.