How Laytus reads the way markets move together
A five-layer pipeline that turns prediction market questions into a correlation coefficient the pricing engine can actually use.
Each cell is the correlation between two events. Darker teal means the pair tends to move together.
Independence is the default, not the truth
Most platforms price multi-event bets by multiplying individual probabilities, which assumes outcomes are unrelated. They rarely are. When Bitcoin runs, Ethereum usually follows. When the Fed eases, growth stocks rally.
The correlation engine pairs natural language processing with statistical modeling to score how prediction markets actually relate. The output is a coefficient (ρ) the Gaussian copula uses to price the joint outcome fairly.
Five layers, NLP into statistics
Entity Extraction
Parses each prediction market question for the actors, institutions, and assets it references. Overlap between questions becomes a starting signal.
Logical Implication
Reads whether one event happening would shift the odds of another, and in which direction. Connected events start with a non-zero link.
Category Classification
Questions are clustered by theme. Same-theme pairs share a baseline; clearly unrelated pairs are pinned to zero.
Correlation Matrix
Estimates the correlation (ρ) between every pair of active questions from historical market data. The matrix feeds the pricing engine.
Model Validation
Compares realized outcomes to the model in real time. Fees widen automatically when the model is less confident.
The signals the rest of the protocol consumes
Correlation between any two events
P(A ∩ B) via Gaussian copula
How much data backs a given pair
Illiquid markets, ambiguous resolution
Easing, risk-on, recession, and others
Model accuracy tracked over time