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Deep Reinforcement Learning for Robust Goal-Based Wealth Management

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  • Tessa Bauman
  • Bruno Gav{s}perov
  • Stjepan Beguv{s}i'c
  • Zvonko Kostanjv{c}ar

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.

Suggested Citation

  • Tessa Bauman & Bruno Gav{s}perov & Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar, 2023. "Deep Reinforcement Learning for Robust Goal-Based Wealth Management," Papers 2307.13501, arXiv.org.
    • Handle: RePEc:arx:papers:2307.13501
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    References listed on IDEAS

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    1. Sanjiv R. Das & Daniel Ostrov & Anand Radhakrishnan & Deep Srivastav, 2020. "Dynamic portfolio allocation in goals-based wealth management," Computational Management Science, Springer, vol. 17(4), pages 613-640, December.
    2. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    3. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
    4. Francisco Gomes, 2020. "Portfolio Choice Over the Life Cycle: A Survey," Annual Review of Financial Economics, Annual Reviews, vol. 12(1), pages 277-304, December.
    5. Matthew Dixon & Igor Halperin, 2020. "G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning," Papers 2002.10990, arXiv.org.
    6. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 2020.
    7. Peter A. Forsyth & Yuying Li & Kenneth R. Vetzal, 2017. "Are target date funds dinosaurs? Failure to adapt can lead to extinction," Papers 1705.00543, arXiv.org.
    8. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
    9. Arjun K. Iyer & Seth A. Hoelscher & Cédric L. Mbanga, 2022. "Target Date Funds, Drawdown Risk, and Central Bank Intervention: Evidence during the COVID-19 Pandemic," JRFM, MDPI, vol. 15(9), pages 1-18, September.
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