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Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices
[Mean-variance versus Full-scale Optimisation: In and out of Sample]

Author

Listed:
  • Humoud Alsabah
  • Agostino Capponi
  • Octavio Ruiz Lacedelli
  • Matt Stern

Abstract

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.

Suggested Citation

  • Humoud Alsabah & Agostino Capponi & Octavio Ruiz Lacedelli & Matt Stern, 2021. "Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices [Mean-variance versus Full-scale Optimisation: In and out of Sample]," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 369-392.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:2:p:369-392.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbz040
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    Citations

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    Cited by:

    1. Fabian Wagner, 2024. "Determinants of conventional and digital investment advisory decisions: a systematic literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
    2. Chujun He & Zhonghao Huang & Xiangguo Li & Ye Luo & Kewei Ma & Yuxuan Xiong & Xiaowei Zhang & Mingyang Zhao, 2025. "Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market," Papers 2510.21147, arXiv.org.
    3. Jiaxin Wei & Jia Liu, 2025. "Personalized fund recommendation with dynamic utility learning," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-27, December.
    4. Hu, Xiao & Kang, Siqin & Ren, Long & Zhu, Shaokeng, 2024. "Interactive preference analysis: A reinforcement learning framework," European Journal of Operational Research, Elsevier, vol. 319(3), pages 983-998.
    5. Hanyong Cho & Geumil Bae & Jang Ho Kim, 2026. "Investor risk profiles of large language models," Papers 2603.09303, arXiv.org, revised May 2026.
    6. Timothy C. Y. Chan & Rafid Mahmood & Ian Yihang Zhu, 2025. "Inverse Optimization: Theory and Applications," Operations Research, INFORMS, vol. 73(2), pages 1046-1074, March.
    7. repec:rbs:ijbess:v:7:y:2025:i:5:p:362-374 is not listed on IDEAS
    8. Nicole Maria Namyslo & Dominik Jung & Timo Sturm, 2025. "The state of robo-advisory design: A systematic consolidation of design requirements and recommendations," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-29, December.
    9. Zhanhui Chen & Yang Lu & Jinggong Zhang & Wenjun Zhu, 2024. "Managing Weather Risk with a Neural Network-Based Index Insurance," Management Science, INFORMS, vol. 70(7), pages 4306-4327, July.
    10. Wang, Ziwei & Yang, Haijun & Li, Zhen, 2025. "Will technological advancement affect Bitcoin trading and pricing? Evidence from BRC-20 tokens," Global Finance Journal, Elsevier, vol. 65(C).
    11. Narayanan, Pranadharthiharan & Somasundaram, Jeeva & Seifert, Matthias, 2025. "Risk-averse algorithmic support and inventory management," European Journal of Operational Research, Elsevier, vol. 322(3), pages 993-1004.
    12. Keffert, Henk, 2024. "Robo-advising: Optimal investment with mismeasured and unstable risk preferences," European Journal of Operational Research, Elsevier, vol. 315(1), pages 378-392.
    13. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    14. Luo, Haohan & Liu, Xin & Lv, Xingyang & Hu, Yubei & Ahmad, Ali J., 2024. "Investors’ willingness to use robo-advisors: Extrapolating influencing factors based on the fiduciary duty of investment advisors," International Review of Economics & Finance, Elsevier, vol. 94(C).
    15. Haoyang Cao & Zhengqi Wu & Renyuan Xu, 2024. "Inference of Utilities and Time Preference in Sequential Decision-Making," Papers 2405.15975, arXiv.org, revised Jun 2024.

    More about this item

    Keywords

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    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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