Sequential bootstrapping is effective for sampling in financial ML, addressing concurrency at its source. Unlike traditional methods that allow redundancy, this method dynamically adjusts probabilities based on temporal overlaps, producing samples with maximally independent observations.

This method demonstrates inefficiencies of standard bootstrap in finance due to label concurrency. Standard bootstrap's reliance on IID observations does not translate well to financial data where observations overlap temporally. This results in a smaller effective sample size and misleading variance estimations.

Sequential bootstrapping adjusts the probability of drawing observations based on their uniqueness to the current sample, minimizing redundancy and enhancing model robustness. Implementing this requires efficient calculation of indicator matrices and uniq...

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