|Title||Statistical Learning for Individualized Asset Allocation|
|Publication Type||Journal Article|
|Year of Publication||Forthcoming|
|Authors||Ding, Y, Li, Y, Song, R|
|Journal||Journal of the American Statistical Association|
|Keywords||Continuous-action decision-making, High-dimensional statistical learning, Individualization, Penalized regression|
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. © 2022 American Statistical Association.