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 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 select mutual funds in real time by combining individual fund holdings and a large number (94) of stock characteristics to compute fund-level characteristics on the basis of the stocks they hold. We show that, first, the majority of funds are largely exposed---both positively and...

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...