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2023 | OriginalPaper | Chapter

Deep Reinforcement Learning for Robust Goal-Based Wealth Management

Authors : Tessa Bauman, Bruno Gašperov, Stjepan Begušić, Zvonko Kostanjčar

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer Nature Switzerland

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Abstract

Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as it requires choosing the appropriate investment until a goal is achieved. Consequently, reinforcement learning, a machine learning technique appropriate for sequential decision-making, offers a promising path for optimizing these investment strategies. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed. The experimental results indicate its superiority over several goal-based wealth management benchmarks on both simulated and historical market data.

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Unlike in GBWM, in market making, increasing levels of risk are typically incurred as the terminal time is approached [8], resulting in weaker inventory penalization.

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The CRRA utility \(\mathcal {U}\) is given by: \( \mathcal {U}(x) = {x^{\gamma }}/{\gamma },\text { }\gamma < 1, \) where \(1-\gamma \) is the coefficient of relative risk aversion.

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Title: Deep Reinforcement Learning for Robust Goal-Based Wealth Management
Authors: Tessa Bauman
Bruno Gašperov
Stjepan Begušić
Zvonko Kostanjčar
Publisher: Springer Nature Switzerland
Book: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-031-34110-6

Electronic ISBN: 978-3-031-34111-3

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-34111-3_7

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