Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: The asset portfolios of retirees’ serve many purposes including current income, liquidity, purchasing power, estate, tax minimization, and so on. Multiple objectives with different levels of importance lead to a complex asset allocation problem for retirees. Existing asset allocation methods normally focus on a subset of the multiple objectives of retirees in an approximate way. The simulation-based multiple objective asset allocation method objectively assesses each allocation plan against multiple objectives in a consistent way and provides a more holistic picture of possible outcomes. This information is critical for finding the optimal allocation plan. The optimization is based on the weighted performance relative to multiple objectives. At the same time, it requires more inputs and advanced modeling.