This article aims to assess the current robo-advisor models. The first part analyses the existing quantitative models behind robo-advisors. It scrutinizes the current robo-advisory quantitative models and their tendency to rely on modern portfolio theory for asset allocation decisions. Looking at the academic developments of recent years this article then proposes some viable alternatives and improvements of these models that asset managers should consider.
The second part of the article will elaborate on the ability of robo-advisors to anticipate the client’s willingness and competence to take risks. Robo-advisors might be able to leverage data to better assess the ‘risk profile’ of their customers.
Using data might however propose some regulatory challenges.
The third part of this article addresses the use of behavioural finance by robo- advisors as a strategy to mitigate risk using clients’ level of risk aversion as a parameter to protect investors from behavioural biases hence reducing financial investors downturns.
The conclusion of this article proposes some suggestions to unlock the full potential of robo-advisors.