We normally think of robo-advisors as applying advanced technology with the goal of providing state-of-the-art investment solutions to investors at low cost. But it can be argued that from an investment management point of view, technology for most robo-advisors is relatively simple. It builds portfolios of prefabricated indexes based on a preconceived academic notion of investment optimization and aligns these portfolios with client profiles based on relatively elementary risk/return profiling. We suspect that the second or perhaps third wave of true robo-advisors may have as their goal something more ambitious, i.e., the application of big data analytics and artificial intelligence in order to actually improve the investment process itself.
This could be the development of smart-beta 2.0, followed by quantitative active management on steroids. The new robo-advisors could more fully leverage the growing quantity and availability of relevant data as well as the computing power and software engineering sophistication now more widely employed in non-financial industries. Robo-engines could task intelligent bots to review millions of data points from across an increasing number of sources, identify relationships and patterns, and apply the findings to guide investment decisions. Client profiling too could be enhanced by the incorporation of a much enriched personal profile data set.
The building of the next generation of robo-advisors is just beginning as startups are drawing technologists from companies at the forefront of big data (Google, Facebook, etc.) and applying their skills to financial services and investment management. Their success may lead to a much altered landscape for advisors, investment managers and investors.