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

TL;DR

This paper presents a reinforcement learning framework for robo-advisors that adaptively learns investors' risk preferences over time through observed portfolio choices, improving decision-making in varying market conditions.

Contribution

It introduces an exploration-exploitation algorithm that learns investor risk preferences without prior knowledge, with proven convergence to optimal advice over time.

Findings

Algorithm's value function converges polynomially to the optimal.

Robo-advisor can outperform individual investors by correcting mistakes.

Framework effectively balances information gathering and autonomous trading.

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.