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 employ a novel brokerage account dataset to investigate which individual investors are the most attentive, how investors allocate their attention, and the relation between investor attention and performance. Attention is positively related to investment performance, both at the portfolio...

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,...

We exploit a unique dataset to study how the information sets of retail and institutional investors evolve over time. In line with theoretical models of rational inattention, investors’ information set is related to their sophistication, with more sophisticated investors tracking more stocks....