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MINI REVIEW article

Front. Behav. Econ.
Sec. Behavioral Microfoundations
Volume 3 - 2024 | doi: 10.3389/frbhe.2024.1489159

Evaluating Robo-Advisors Through Behavioral Finance: A Critical Review of Technology Potential, Rationality, and Investor Expectations

Provisionally accepted
  • The Chicago School of Professional Psychology, Chicago, United States

The final, formatted version of the article will be published soon.

    The mini review assesses the value propositions of robo-advisors through the lens of behavioral finance. Despite their promise of data-driven, rational investment strategies, robo-advisors may not fully replicate the personalized service of human financial advisors or eliminate human biases in decision-making. A content analysis of 80 peer-reviewed articles and publications was conducted, focusing on the intersection of financial technology and behavioral finance. Literature was retrieved using The Chicago School University Library's OneSearch and the EBSCO host database, with key terms including "robo-advisor," "investment behavior," "risk tolerance," "financial literacy," and "affective trust." The review identifies four key limitations of robo-advisors: (1) their inability to replicate the service-relationship of human advisors; (2) the presence of human bias in supposedly rational algorithms; (3) the inability to minimize market risk; and (4) their limited impact on improving users' financial literacy. Instead, robo-advisors temporarily compensate for a lack of financial knowledge through passive investment strategies. The findings suggest that integrating behavioral finance principles could enhance the predictive power of robo-advisors, though this would introduce additional complexities. The review calls for further research and regulatory measures to ensure that these technologies prioritize investor protection and financial literacy as they continue to evolve.

    Keywords: behavioral finance, Robo-advisor, Investment behavior, risk, affective trust, financial literacy, artificial intelligence

    Received: 31 Aug 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Eichler and Schwab. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Kim Sandy Eichler, The Chicago School of Professional Psychology, Chicago, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.