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2023 | OriginalPaper | Buchkapitel

Deep Reinforcement Learning for Robust Goal-Based Wealth Management

verfasst von : Tessa Bauman, Bruno Gašperov, Stjepan Begušić, Zvonko Kostanjčar

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: 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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Vorheriges Kapitel Deep Learning Based Employee Attrition Prediction

Nächstes Kapitel DeNISE: Deep Networks for Improved Segmentation Edges

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.

An example is given by the asset allocation of Fidelity Freedom Funds (https://www.fidelity.com/mutual-funds/fidelity-fund-portfolios/freedom-funds).

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.

http://www.econ.yale.edu/~shiller/data.htm.

Since the original policy found by PPO is stochastic, its determinism is enforced by returning the mode of the distribution over the action space instead of sampling from it.

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Titel: Deep Reinforcement Learning for Robust Goal-Based Wealth Management
verfasst von: Tessa Bauman
Bruno Gašperov
Stjepan Begušić
Zvonko Kostanjčar
Verlag: Springer Nature Switzerland
Buch: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-031-34110-6

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

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

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