How Risk Profiles of Investors Affect Robo-Advised Portfolios - Boreiko, Dmitri; Massarotti, Francesca (Frontiers in Artificial Intelligence, 2020)
Automated financial advising (robo-advising) has become an established practice in wealth management, yet very few studies have looked at the cross-section of the robo-advisors and the factors explaining persistent variability in their portfolio allocation recommendations. Using a sample of 53 advising platforms from US and Germany, we show that the underlying algorithms manage to identify different risk profiles although substantial variability is evident even within the same investor types’ groups. The robo-advisor expertise in a particular asset class seems to play a significant role as well as the geographical location, unlike the breadth of the offered investment choice (number of portfolios) across the robo-advisors under study.