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Fed/GFLEC Financial Literacy Seminar Series

November 12, 2020

3:30 PM - 4:30 PM ET

Seminar III: Who Benefits from Robo-advising? Evidence from Machine Learning



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Alberto Rossi

Provost’s Distinguished Associate Professor of Finance

Associate Director, Center for Financial Markets & Policy, McDonough School of Business, Georgetown University



Bio: Alberto Rossi

Alberto Rossi is the Provost’s Distinguished Associate Professor of Finance at the McDonough School of Business, Georgetown University. He is also the Associate Director of the Center for Financial Markets and Policy at Georgetown and an Academic Fellow of the Luohan Academy. His research interests include FinTech, Household Finance, Machine Learning and Asset Pricing. His recent work studies how robo-advisors can help individuals make better financial decisions and how to predict stock market returns using machine learning algorithms. He has worked extensively in analyzing big data, has collaborated with major brokerage houses, FinTech firms and asset managers around the world.

Professor Rossi’s work has been published in leading academic journals such as the Journal of Finance, the Review of Financial Studies, the Journal of Financial Economics and Management Science.

Before McDonough, he was an Associate Professor with tenure at the R.H. Smith School of Business, University of Maryland. He also worked as an economist at the Board of Governors of the Federal Reserve System in Washington DC. He received his PhD in Economics from the University of California, San Diego.


We study the effects of a large U.S. hybrid robo-adviser on the portfolios of previously self-directed investors. Across all investors, robo-advising reduces idiosyncratic risk by lowering the holdings of individual stocks and active mutual funds and raising exposure to low-cost indexed mutual funds. It further eliminates investors’ home bias and increases investors’ overall risk-adjusted performance, mainly by lowering investors’ portfolio risk. We use a machine learning algorithm, known as Boosted Regression Trees (BRT), to explain the cross-sectional variation in the effects of advice on portfolio allocations and performance. Finally, we study the determinants of investors’ sign-up and attrition.