A Robust Neural Network Approach to Optimal Decumulation and Factor Investing in Defined Contribution Pension Plans

Abstract

In this thesis, we propose a novel data-driven neural network (NN) optimization framework for solving an optimal stochastic control problem under stochastic constraints. The NN utilizes customized output layer activation functions, which permits training via standard unconstrained optimization. The optimal solution of the two-asset problem yields a multi-period asset allocation and decumulation strategy for a holder of a defined contribution (DC) pension plan. The objective function of the optimal control problem is based on expected wealth withdrawn (EW) and expected shortfall (ES) that directly targets left-tail risk. The stochastic bound constraints enforce a guaranteed minimum withdrawal each year. We demonstrate that the data-driven NN approach is capable of learning a near-optimal solution by benchmarking it against the numerical results from a Hamilton-Jacobi-Bellman (HJB) Partial Differential Equation (PDE) computational framework. The NN framework has the advantage of being able to scale to high dimensional multi-asset problems, which we take advantage of in this work to investigate the effectiveness of various factor investing strategies in improving investment outcomes for the investor.