  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.
