Statistical Learning for Individualized Asset Allocation

• 成果类型: Article
• 署名作者: Ding, Yi; Li, Yingying; Song, Rui
• 署名单位: University of Macau; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; North Carolina State University; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2139265
• 发表日期: 2024
• 页码: 639-649
• 关键词: nonconcave penalized likelihood; variable selection; model selection; regression; consumption; sparsity

摘要

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.