Statistical Learning for Individualized Asset Allocation

TL;DR

This paper introduces a high-dimensional statistical learning framework for personalized asset allocation that models continuous decisions using discretization and penalized regression, improving individual financial strategies.

Contribution

It develops a novel discretization and penalized regression approach with theoretical guarantees for continuous-action decision-making in high dimensions.

Findings

The framework effectively models continuous actions with high accuracy.

Empirical results show improved individual asset allocation strategies.

The method provides valid statistical inference for optimal decision-making.

Abstract

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.