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

Abstract

Advances in financial technology have led to the development of easy-to-use online platforms referred to as robo-advisors or digital-advisors, offering automated investment and portfolio management services to retail investors. By leveraging algorithms embodying well-established investment principles and the availability of exchange traded funds (ETFs) and liquid securities in different asset classes, robo-advisors automatically manage client portfolios that deliver similar or better investment performance at a lower cost as compared to traditional financial retail services.

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. Since robo-advisors serve individual retail investors, tax management is a focal point on most platforms. We devote substantial attention to automated implementations of a number of tax optimization strategies, including tax-loss harvesting and asset location. Finally, we explore future developments in the robo-advisory space related to goal-based investing, portfolio personalization, and cash management.

Keywords: Asset location, Autonomous finance, Cash management, Goals-based investing, Investor interaction, Portfolio management, Robo-advisory, Responsible investing, Retail investing, Retirement planning, Risk management, Tax-loss harvesting, Tax management

JEL Classification: D10, D31, D91, G11, O16, O33,