Lead–Lag Detection in Financial Markets: A Structured Framework Integrating Bootstrap, FDR, Permutation Tests, and Future AI Extensions

This whitepaper introduces a structured framework for detecting lead–lag relationships in financial markets, combining bootstrap resampling, false discovery rate correction, and permutation testing. A case study on the US Dollar Index (DXY) and Dow Jones Industrial Average (US30) shows methodology-sensitive signals, with binomial and bootstrap tests suggesting significance while permutation and out-of-sample validation weaken results. The study highlights the importance of multi-layer validation and proposes future extensions with Monte Carlo simulations, genetic algorithms, and machine learning.