THESIS
2020
x, 106 pages : illustrations ; 30 cm
Abstract
We establish a statistical learning framework for individualized asset allocation. A high-dimensional
Q-learning methodology is developed for continuous-action decision making. We show that our
proposed model parameter estimator enjoys desirable theoretical properties, and our approach
allows for valid statistical inference for the optimal value associated with the optimal decision
making. Empirically, the proposed statistical learning framework is exercised with Health and
Retirement Study data. The results show that our optimal individualized strategy improves individual
financial well-being and surpasses benchmark strategies under a consumption-based
utility framework. We discuss further the multiple asset allocation without resorting to individualized
information. When indiv...[
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We establish a statistical learning framework for individualized asset allocation. A high-dimensional
Q-learning methodology is developed for continuous-action decision making. We show that our
proposed model parameter estimator enjoys desirable theoretical properties, and our approach
allows for valid statistical inference for the optimal value associated with the optimal decision
making. Empirically, the proposed statistical learning framework is exercised with Health and
Retirement Study data. The results show that our optimal individualized strategy improves individual
financial well-being and surpasses benchmark strategies under a consumption-based
utility framework. We discuss further the multiple asset allocation without resorting to individualized
information. When individualized information is absent, we focus on high-dimensional
minimum variance portfolio (MVP) and study the estimation of MVP under statistical factor
models. A unified MVP estimator is developed and shown to enjoy sharp risk consistency theoretically.
Our proposed MVP estimator performs favorably over various benchmark portfolios
in numerical studies.
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