KellyBoost：基于梯度提升树的增长最优投资组合构建
KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees
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中文总结 AI 辅助
该研究提出名为KellyBoost的多输出XGBoost模型，以精确负对数增长率为损失函数，推导并验证相关海森矩阵，实现基于特征的增长最优投资组合配置。
中文摘要 AI 辅助
KellyBoost是一个单一的多输出XGBoost模型，其softmax输出即为投资组合：设y为各资产持有期收益向量，训练损失为 - log(1 + w y)，即负对数增长率，因此拟合模型是基于特征条件的增长最优（Kelly）配置。该目标是精确的而非替代的：我们推导了梯度、解析对角海森矩阵和完整海森矩阵的闭式表达式，通过有限差分验证，并提供了无依赖的参考引擎。
英文摘要
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.
