Robo-Advising

Francesco D’Acunto () and Alberto G. Rossi ()
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Francesco D’Acunto: Carroll School of Management, Boston College
Alberto G. Rossi: Georgetown University

A chapter in The Palgrave Handbook of Technological Finance, 2021, pp 725-749 from Springer

Abstract: Abstract 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 of 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 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-65117-6_26

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DOI: 10.1007/978-3-030-65117-6_26

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