Personalized Robo-Advising: Enhancing Investment through Client Interactions

60 Pages Posted: 6 Nov 2019

See all articles by Agostino Capponi

Agostino Capponi

Columbia University

Sveinn Olafsson

Columbia University

Thaleia Zariphopoulou

University of Texas at Austin - Red McCombs School of Business

Date Written: October 29, 2019

Abstract

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

Capponi, Agostino and Olafsson, Sveinn and Zariphopoulou, Thaleia, Personalized Robo-Advising: Enhancing Investment through Client Interactions (October 29, 2019). Available at SSRN: https://ssrn.com/abstract=3453975 or http://dx.doi.org/10.2139/ssrn.3453975

Agostino Capponi (Contact Author)

Columbia University ( email )

S. W. Mudd Building
New York, NY 10027
United States

Sveinn Olafsson

Columbia University ( email )

331 S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States
7655887631 (Phone)

Thaleia Zariphopoulou

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

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