Robo-advisors are automated investment platforms that are rapidly emerging as an alternative to traditional financial advisors. They currently manage over USD440 billion in the United States, and are expected to reach an estimated USD1.5 trillion by 2023. Their performance strongly depends on the mechanism used to elicit the risk preferences of investors. We propose a control problem formulation, where the robo-advisor dynamically adapts its model for the investor's risk aversion based on information solicited throughout the investment horizon. We derive a closed-form solution for the resulting control problem, and introduce novel regret measures to quantify the benefits of a more tailored investment advice. Our model explains empirical findings according to which clients who place higher emphasis on delegation and clients with a more unstable risk profile benefit less from robo-advising.

Agostino Capponi is an Associate Professor of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute, and the FDT Center for Intelligent Asset Management . He serves as an External Consultant at the U.S. Commodity Futures Trading Commission, Office of the Chief Economist, on topics related to clearinghouse collateral requirements.

Agostino's main research interests are on systemic risk and financial stability, market microstructure, and over-the-counter markets. Topics of recent interest include robo-advising, and the analysis of cryptocurrencies.

Agostino's research has been presented at premier finance conferences, including AFA, EFA, FIRS, the SFS Cavalcade North America Conference, and the Utah Winter Finance Conference.

Agostino's research has been funded by major public institutions and private corporations, including the NSF, DARPA, J.P. Morgan, the Institute for New Economic Thinking, the Global Risk Institute, the Clearpool Group, and the

OCP Group. Agostino serves on the editorial board of Finance and Stochastics, SIAM Journal on Financial Mathematics, Mathematics and Financial Economics, Management Science, Operations Research, Stochastic Systems, and many others. He serves as the chair of the SIAM Activity group in Financial Mathematics and Engineering, and as the president of the INFORMS Finance Section.

Agostino is a recipient of the NSF CAREER award, the J.P. Morgan AI Research Faculty award, and a prize from the MIT Center for Finance and Policy and the Harvard Crowd Innovation Laboratory.

Agostino's research on clearinghouses, joint with the Department of Treasury's Office of Financial Research, has received attention by various media outlets, including Thomson Reuters, Bloomberg, and the American Banker.

Agostino holds a world patent for a target tracking methodology in military networks.

Agostino received his Master and Ph.D. Degree in Computer Science and Applied and Computational Mathematics from the California Institute of Technology, respectively in 2006 and 2009.