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Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices

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  • 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 which 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 algorithm's value function converges to the optimal 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, 2019. "Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices," Papers 1911.02067, arXiv.org, revised Nov 2019.
    • Handle: RePEc:arx:papers:1911.02067
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    References listed on IDEAS

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    1. Alessandro Bucciol & Raffaele Miniaci, 2011. "Household Portfolios and Implicit Risk Preference," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1235-1250, November.
    2. Constantinides, George M., 1984. "Optimal stock trading with personal taxes : Implications for prices and the abnormal January returns," Journal of Financial Economics, Elsevier, vol. 13(1), pages 65-89, March.
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    4. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    5. Alessandro Bucciol & Raffaele Miniaci, 2018. "Financial Risk Propensity, Business Cycles and Perceived Risk Exposure," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(1), pages 160-183, February.
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