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Artificial Intelligence Alter Egos: Who might benefit from robo-investing?

Author

Listed:
  • D'Hondt, Catherine
  • De Winne, Rudy
  • Ghysels, Eric
  • Raymond, Steve

Abstract

We use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the potential benefits of robo-investing. We explore robo-investing strategies commonly used in the industry, including some involving advanced machine learning methods. We shadow each of our investors with a robo-advisor to shed light on possible benefits the emerging robo-advising industry may provide to certain segments of the population, such as low income and/or low education investors.
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Suggested Citation

  • D'Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," LIDAM Reprints LFIN 2020007, Université catholique de Louvain, Louvain Finance (LFIN).
    • Handle: RePEc:ajf:louvlr:2020007
    Note: In : Journal of Empirical Finance, Vol. 59, p. 278-299 (2020)
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    Cited by:

    1. Nicole Maria Namyslo & Dominik Jung & Timo Sturm, 2025. "The state of robo-advisory design: A systematic consolidation of design requirements and recommendations," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-29, December.
    2. Lambrecht, Marco & Oechssler, Jörg & Weidenholzer, Simon, 2023. "On the benefits of robo-advice in financial markets," Working Papers 0734, University of Heidelberg, Department of Economics.
    3. Kaawach, Said & Kowalewski, Oskar & Talavera, Oleksandr, 2024. "Automatic versus manual investing: Role of past performance," Journal of Financial Stability, Elsevier, vol. 74(C).
    4. Liyun Wu & Muneeb Ahmad & Salman Ali Qureshi & Kashif Raza & Yousaf Ali Khan, 2022. "An analysis of machine learning risk factors and risk parity portfolio optimization," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-19, September.
    5. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
    6. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
    7. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    8. Heming Chen & Xiaojing Cai, 2025. "Optimal vs. Naive Diversification in the Cryptocurrencies Market: The Role of Time-Varying Moments and Transaction Costs," Papers 2501.12841, arXiv.org, revised Nov 2025.
    9. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    10. Vishaal Baulkaran & Pawan Jain, 2023. "Who uses robo‐advising and how?," The Financial Review, Eastern Finance Association, vol. 58(1), pages 65-89, February.
    11. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    12. Bianchi, Milo & Brière, Marie, 2021. "Human-Robot Interactions in Investment Decisions," TSE Working Papers 21-1251, Toulouse School of Economics (TSE), revised Mar 2024.
    13. Zhu, Hui & Vigren, Olli & Söderberg, Inga-Lill, 2024. "Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda," Journal of Business Research, Elsevier, vol. 174(C).
    14. Said Kaawach & Oskar Kowalewski & Oleksandr Talavera, 2023. "Automatic vs Manual Investing: Role of Past Performance," Discussion Papers 23-04, Department of Economics, University of Birmingham.
    15. Tiberius, Victor & Gojowy, Robin & Dabić, Marina, 2022. "Forecasting the future of robo advisory: A three-stage Delphi study on economic, technological, and societal implications," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    16. Ida Ayu Agung Faradynawati & Inga-Lill Söderberg, 2022. "Sustainable Investment Preferences among Robo-Advisor Clients," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    17. Yang, Jing & Shi, Jianxun & Xu, Ling, 2025. "Effect of digital finance on household financial asset allocation: a social psychology perspective," The North American Journal of Economics and Finance, Elsevier, vol. 78(C).
    18. D’Hondt, Catherine & Merli, Maxime & Roger, Tristan, 2022. "What drives retail portfolio exposure to ESG factors?," Finance Research Letters, Elsevier, vol. 46(PB).
    19. Anthony Bellofatto & Marie-Hélène Broihanne & Catherine D’Hondt, 2025. "Financial knowledge acquisition and trading behavior: empirical evidence from an online information tool," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 39(1), pages 1-45, March.
    20. Seiler, Volker & Fanenbruck, Katharina Maria, 2021. "Acceptance of digital investment solutions: The case of robo advisory in Germany," Research in International Business and Finance, Elsevier, vol. 58(C).
    21. Reher, Michael & Sokolinski, Stanislav, 2024. "Robo advisors and access to wealth management," Journal of Financial Economics, Elsevier, vol. 155(C).

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