A case study for unlocking the potential of deep learning in asset-liability-management

Abstract

The extensive application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset-Liability-Management ("Deep ALM") for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimization of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset-Liability-Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylized case.

Keywords: asset-liability-management; deep hedging; machine learning in finance; portfolio management; reinforcement learning.