Deep Reinforcement Learning for Robust Goal-Based Wealth Management

Goal-based wealth management (GBWM), also known as goal-based investing [1], is a relatively new class of approaches to wealth management that focus on attaining specific financial objectives (goals). As opposed to more traditional approaches to wealth management, in which the notion of expected profit and loss (PnL) plays a central role, GBWM revolves around maximizing the probability of goal attainment. Common investment goals include saving for college tuition, retirement, or purchasing a home. Recent years have seen an uptick in the popularity of GBWM [2], particularly through the use of target date funds (TDFs). TDFs, also known as life-cycle funds [3] or target-retirement funds, are mutual funds or exchange-traded funds that provide investors with an asset allocation aimed at fulfilling a target (goal) by a specified target date (e.g. a retirement date).