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. The value function of continuous-action is estimated 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 population financial well-being.
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。我们制定了一种分解方法,以模拟连续行动的效果,并使分解频率与观察次数大相径庭。持续行动的价值作用是使用惩罚性回归来估计的,而我们提议的对模型系数的线性转换实行的普遍惩罚是惩罚性的。我们表明,我们提议的分解和递解,对效果不连续(DROVE)法实行普遍分解和递解,这具有理想的理论特性,并允许从统计上推断与最佳决策相关的最佳价值。在寻找个性化最佳资产分配方面,与健康和退休研究数据一起运用拟议的框架。结果显示,我们个别化的最佳战略改善了人口的财务福利。