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

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  • 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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  • D’Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 278-299.
    • Handle: RePEc:eee:empfin:v:59:y:2020:i:c:p:278-299
    DOI: 10.1016/j.jempfin.2020.10.002
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    Cited by:

    1. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2023. "Panel Data Nowcasting: The Case of Price-Earnings Ratios," Papers 2307.02673, arXiv.org.
    2. 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.
    3. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    4. Bianchi, Milo & Brière, Marie, 2021. "Human-Robot Interactions in Investment Decisions," TSE Working Papers 21-1251, Toulouse School of Economics (TSE), revised Mar 2024.
    5. Ida Ayu Agung Faradynawati & Inga-Lill Söderberg, 2022. "Sustainable Investment Preferences among Robo-Advisor Clients," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    6. D’Hondt, Catherine & Merli, Maxime & Roger, Tristan, 2022. "What drives retail portfolio exposure to ESG factors?," Finance Research Letters, Elsevier, vol. 46(PB).
    7. 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).
    8. 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.
    9. Vishaal Baulkaran & Pawan Jain, 2023. "Who uses robo‐advising and how?," The Financial Review, Eastern Finance Association, vol. 58(1), pages 65-89, February.
    10. 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.
    11. 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).

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