KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees

Abstract:KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the training loss is - log(1 + w y), the negative log growth rate, so the fitted model is the growth-optimal (Kelly) allocation conditioned on the features. The objective is exact rather than a surrogate: we derive the gradient, the analytic diagonal Hessian and the full Hessian in closed form, verify them by finite differences, and ship a dependency-free reference engine.

Portfolio Management (q-fin.PM); Machine Learning (cs.LG)

arXiv:2608.23393 [q-fin.PM]

(or arXiv:2608.23393v1 [q-fin.PM] for this version)

https://doi.org/10.48550/arXiv.2608.23393 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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