Personalized Robo-Advising: Enhancing Investment Through Client Interaction

Abstract

Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework in which a robo-advisor interacts with a client to solve an adaptive mean-variance portfolio optimization problem. The risk-return tradeoff adapts to the client’s risk profile, which depends on idiosyncratic characteristics, market returns, and economic conditions. We show that the optimal investment strategy includes both myopic and intertemporal hedging terms that reflect the dynamic risk profile of the client. We characterize the optimal portfolio personalization via a tradeoff faced by the robo-advisor between receiving information from the client in a timely manner and mitigating behavioral biases in the communicated risk profile. We argue that the optimal portfolio’s Sharpe ratio and return distribution improve if the robo-advisor counters the client’s tendency to reduce market exposure during economic contractions when the market risk-return tradeoff is more favorable. This paper was accepted by David Simchi-Levi, stochastic models and simulation.