Paper proposes a new method for finding sparse mean reverting portfolios efficiently.
problem Finding sparse mean reverting portfolios from a large number of assets.
method Leverages H-SGDLM data to formulate a quasi-convex minimization problem with a normalisation constraint, solving it with a cyclical coordinate descent algorithm.
result Efficiently computes exact sparse solutions for large asset universes, demonstrating flexibility, speed, and scalability.