We document five effects of providing individuals with crowdsourced spending information about their peers (individuals with similar characteristics) through a FinTech app. First, users who spend more than their peers reduce their spending significantly, whereas users who spend less keep...

We study a robo-advising portfolio optimizer that constructs tailored strategies based on in- vestors’ 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 document five effects of providing individuals with crowdsourced spending information about their peers (individuals with similar characteristics) through a FinTech app. First, users who spend more than their peers reduce their spending significantly, whereas users who spend less keep...

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,...