New technologies are revolutionizing the market for advice. For example, algorithms help customers correct their eating habits (e.g. Weight Watchers App), choose a suitable dating mate (e.g. through partnership websites) and adjust their sports training strategies (e.g. Nike+ Running App). This is the case also for financial advice, where robo-advisors for automated and standardized portfolio management have rapidly entered the field.
Robo-advisors attractiveness stems mainly from their capability to reduce irrationality, biases and errors, as well as from their low cost and user-friendly interfaces. Moreover, robo-advisors could eliminate agency and moral hazard problems, since they can be designed to unambiguously serve the investors without having financial interests of their own.
Thanks to the financial support of the Thinking Forward Initiative, we set up a series of field experiments to investigate the peculiarities of human/algorithm interactions in the domain of household financial decision-making. Specifically, we study how robo-advisors could act as commitment devices and hence improve individual financial choices.
The main objective of this study is to identify the optimal design of robo-advisors to improve individual financial choices. As previous research suggest, less wealthy and impatient investors, as well as those with little financial knowledge, would be the ones benefiting more from robo-advisors. Importantly, previous research suggest that the possibility of having financial advice is a key determinant of households' willingness to invest in risky assets. In particular, in our experiment we aim to identify which features of robo-advisors i) increase take-up rate, ii) reduces behavioural biases.