28 Pages Posted: 24 Mar 2020

Multiple version iconThere are 2 versions of this paper

Date Written: January 15, 2020


In this chapter, we first discuss the limitations of traditional financial advice, which led to the emergence of robo-advising. We then describe the main features of robo-advising and propose a taxonomy of robo-advisors based on four defining dimensions: personalization, discretion, involvement, and human interaction. Building on these premises, we delve into the theoretical and empirical evidence on the design and effects of robo-advisors on two major sets of financial decisions, that is, investment choices (for both short- or long-term horizons) and the allocation if financial resources between spending and saving. We conclude by elaborating on five broadly open issues in robo-advising, which beget theoretical and empirical research by scholars in economics, finance, psychology, law, philosophy, as well as regulators and industry practitioners.

Keywords: Robo-advisors, algorithmic aversion, machine learning, artificial intelligence, hybrid robo-advice, holistic advice, conflicts of interest, fiduciaries, preferences, beliefs

JEL Classification: D14, G21

Suggested Citation

D'Acunto, Francesco and Rossi, Alberto G., Robo-Advising (January 15, 2020). Available at SSRN: or

Francesco D'Acunto (Contact Author)

Georgetown University ( email )

Washington, DC 20057
United States

Alberto G. Rossi

Georgetown University ( email )

McDonough School of Business
Georgetown University
Washington, DC 20057
United States

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