Cross-Event Correlation Data
In prediction markets, events are often not independent. Cross-event correlation data measures how probability changes in one market relate to changes in another. These correlations can appear when events share common drivers, such as economic conditions, political outcomes, or regulatory decisions. A shift in one market may signal information relevant to another.
Correlation data helps reveal hidden structure in prediction markets data. It shows whether markets reinforce each other, move in opposite directions, or behave independently.
This analysis is especially useful when tracking portfolios of markets rather than single events. It helps explain synchronized movements that might otherwise look like coincidence.
For analysts, cross-event correlation data supports deeper interpretation. It adds context to probability changes and helps identify shared risk, information spillovers, and systemic patterns across prediction markets.
Why It Matters
Markets do not move in isolation. Cross-event correlation data helps users understand how information and risk propagate across prediction markets.
What is cross-event correlation data in prediction markets?
In prediction markets, cross-event correlation data measures how outcomes or probabilities across different events are related. It shows whether markets tend to move together or separately. This helps identify shared influences. It adds structure to multi-market analysis.
How is cross-event correlation data used in prediction markets data analysis?
Analysts use cross-event correlation data to study co-movement between markets. This supports risk analysis, diversification, and detection of information spillovers. It also helps explain simultaneous probability shifts. Correlation analysis improves understanding beyond single-event views.
Why is cross-event correlation data important for prediction markets APIs?
Prediction markets APIs provide standardized probability data across many events. This makes it possible to compute correlations programmatically. Analysts can track relationships over time and at scale. APIs enable automated cross-event analysis using consistent prediction markets data.
Real-World Example
On Polymarket, markets related to different election outcomes may move together after major polling updates. Cross-event correlation data highlights these linked probability shifts.
Cross-Event Correlation Data and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data needed to compute cross-event correlation data. Analysts can combine probability streams from multiple events and analyze their relationships. This supports portfolio analysis, systemic risk modeling, and multi-market monitoring. The API enables consistent correlation analysis across prediction markets.