Statistical Learning for Individualized Asset Allocation

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 of continuous actions and allow the discretization frequency to be large and diverge with the number of observations. We estimate the value function of continuous-action using penalized regression with our proposed generalized penalties that are imposed on linear transformations of the model coefficients. We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows 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 the financial well-being of the population. Supplementary materials for this article are available online.

本研究构建了面向个性化资产配置的高维统计学习框架。所提出的方法可处理含大量特征的连续行动决策问题。本文提出一种离散化方法对连续行动的效应进行建模，并允许离散化频率随观测样本量增大而发散。本文针对模型系数的线性变换提出广义惩罚项，并采用带该惩罚项的惩罚回归方法估计连续行动下的价值函数。研究表明，所提出的针对效应间断性的广义折叠凹惩罚离散化与回归（Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity, DROVE）方法具备优良的理论性质，且可对最优决策对应的最优价值开展统计推断。实证层面，本研究借助健康与退休研究（Health and Retirement Study, HRS）数据集开展个性化最优资产配置的相关实验。实验结果表明，所提出的个性化最优策略可提升群体的金融福祉。本文配套补充材料可在线获取。