We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect from continuous actions and allow the discretization level to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with generalized penalties that are imposed on linear transformations of the model coefficients. We show that our estimators using generalized folded concave penalties enjoy desirable theoretical properties and allow for statistical inference of the optimal value associated with optimal decision-making. Empirically, the proposed framework is exercised with the Health and Retirement Study data in finding individualized optimal asset allocation. The results show that our individualized optimal strategy improves individual financial well-being and surpasses benchmark strategies.

翻译：我们为个人化资产分配建立了一个高层次的统计学习框架。我们建议的方法处理具有大量特点的连续行动决策。我们制定了一种分化方法,以模拟持续行动的效果,并使分化水平与观察数量大相径庭。持续行动的价值观作用是使用惩罚性回归来估计,对模型系数的线性转换施加普遍惩罚。我们表明,使用普遍折叠的同级罚款的估测者享有理想的理论属性,并允许统计推断最佳决策的最佳价值。我们很生动地利用健康和退休研究数据来寻找个性化的最佳资产分配。结果显示,我们个人化的最佳战略改善了个人的财务福利,超过了基准战略。