Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*

Mean-variance versus Full-scale Optimisation: In and out of Sample

Humoud Alsabah, Agostino Capponi, Octavio Ruiz Lacedelli and Matt Stern

Journal of Financial Econometrics, 2021, vol. 19, issue 2, 369-392

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.

Keywords: robo-advising; reinforcement learning; portfolio selection; probably approximately correct-Markov decision processes (PAC-MDP) (search for similar items in EconPapers)
JEL-codes: D14 G02 G11 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (6)

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