Events

We establish a statistical learning framework for personalized wealth management. A high-dimensional Q-learning methodology is proposed for continuous decision making. The proposed method is shown to enjoy desirable oracle properties and facilitate valid statitical inference for optimal values. Empirically, the proposed statistical learning methodology is exercised with Health and Retirement Study data. The results show that the proposed personalized optimal strategy can improve individual’s financial well-being and surpasses benchmark strategies under a consumption based utility framework.

This is joint work with Yi Ding and Rui Song.