Robo-Advisory: From Investing Principles and Algorithms to Future Developments

Adam Grealish
Betterment, LLC

Petter N. Kolm
New York University (NYU) - Courant Institute of Mathematical Sciences

In this chapter we explore how robo-advisors translate core investing principles and best practices into algorithms. We discuss client onboarding and algorithmic approaches to client risk assessment and financial planning. We review portfolio strategies available on robo-advisor platforms and algorithmic implementations of ongoing portfolio management and risk monitoring. We devote substantial attention to automated implementations of a number of tax optimization strategies, including tax-loss harvesting and asset location. Finally, we examine future developments in the robo-advisory space related to goal-based investing, portfolio personalization, and cash management.

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