Topic: Set it and Forget it: Engineering Investment Habits with FinTech

Speaker: Alberto Rossi, Hachigian Family Professor of Finance (with Tenure), McDonough School of Business, Georgetown University

Time: 10:00am-11:30am, Wednesday, December 10

Location: 4-101

Abstract:

We study how automated investment rules affect saving behavior and investment outcomes using detailed data from a FinTech app designed to help retail investors access mutual funds. Users choose how to design these rules, which vary along dimensions such as frequency, amount, and triggering conditions. Using a randomized encouragement design, we show that automated rules causally increase average savings without crowding out manual contributions. We also show that automated rules reduce trend-chasing behavior: while manual deposits respond strongly to recent returns, automated ones do not, narrowing the gap between fund returns and realized investor returns. However, rule timing remains performance-sensitive—users tend to activate rules after periods of strong returns and suspend them during downturns, especially for equity funds. A survey deployed on the app user population reveals that adopters of automated investment rules are primarily motivated by a desire to avoid procrastination, reduce cognitive load, and simplify decision-making, while non-adopters cite preferences for flexibility and concerns about income volatility. Our findings highlight both the promises and the limitations of automation in improving individual financial outcomes.

Speaker Biography:

Alberto Rossi is the Hachigian Family Professor of Finance at the McDonough School of Business, Georgetown University. He is also the Director of the AI, Analytics, and Future of Work Initiative at Georgetown, a Visiting Fellow at Brookings, and a member of the Economic Advisory Committee (EAC) at FINRA. 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 Ph.D. in Economics from the University of California, San Diego.