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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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    More about this item

    Keywords

    robo-advising; reinforcement learning; portfolio selection; probably approximately correct-Markov decision processes (PAC-MDP);
    All these keywords.

    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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