论文标题
个性化资产分配的统计学习
Statistical Learning for Individualized Asset Allocation
论文作者
论文摘要
我们为个性化资产分配建立了高维统计学习框架。我们提出的方法解决了具有大量特征的连续行动决策。我们开发了一种离散化方法来模拟连续作用的效果，并允许离散频率很大，并且与观察次数分歧。使用惩罚回归估算连续行动的价值函数，并通过我们提出的广义惩罚对模型系数的线性转换施加。我们表明，我们提议的离散和回归对效应不连续性（DROVE）方法的普遍折叠处罚（DROVE）方法具有理想的理论属性，并允许对与最佳决策相关的最佳价值进行统计推断。从经验上讲，提出的框架是通过健康和退休研究数据来寻找个性化最佳资产分配的。结果表明，我们个性化的最佳战略改善了人口财务状况。
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.
