We study a robo-advising portfolio optimizer that constructs tailored strategies based on investors' holdings and preferences. Adopters are similar to non-adopters in terms of demographics, but have more assets under management, trade more, and have higher risk-adjusted performance. The...

We study the spending response of first-time borrowers to an overdraft facility and elicit their preferences, beliefs, and motives through a FinTech application. Users increase their spending permanently, lower their savings rate, and reallocate spending from non-discretionary to discretionary...

In this chapter, we first discuss the limitations of traditional financial advice, which led to the emergence of robo-advising. We then describe the main features of robo-advising and propose a taxonomy of robo-advisors based on four defining dimensions - personalization, discretion,...