Personalized Robo-Advising: Enhancing Investment through Client Interactions
60 Pages Posted: 6 Nov 2019
Date Written: October 29, 2019
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. Their viability crucially depends on timely communication of information from the clients they serve. We introduce and develop a novel human-machine interaction framework, in which the robo-advisor solves an adaptive mean-variance control problem, with the risk-return tradeoff dynamically updated based on the risk profile communicated by the client. Our model predicts that clients who value a personalized portfolio are more suitable for robo-advising. Clients who place higher emphasis on delegation and clients with a risk profile that changes frequently benefit less from robo-advising.
Keywords: robo-advising, Fintech, portfolio choice, adaptive control, regret, human-advisor
JEL Classification: G11, O33, C61
Suggested Citation: Suggested Citation