What Investment Robots Need To Know – Evidence from Panel Survey Data

Automated asset management offerings, so called investment robots (or robo-advisors), assign risky portfolios to individual investors based on investor characteristics such as age, net income or self-assessments of risk aversion. Using new German household panel data, this paper investigates the key household characteristics that predict financial market participation. This information allows us to assess which set of variables is most needed to model private portfolio decisions. Using heavily cross-validated classification trees, we find that a combination of household balance sheet variables – describing the ability to take risks (e.g. net wealth) – and household personal characteristics – describing the willingness to take risks (e.g. risk aversion) – best explain the cross sectional variation in financial market participation.