IDEAS home Printed from https://ideas.repec.org/p/zbw/vfsc24/302354.html
   My bibliography  Save this paper

On the benefits of robo-advice in financial markets

Author

Listed:
  • Lambrecht, Marco
  • Oechssler, Jörg
  • Weidenholzer, Simon

Abstract

Robo-advisors are novel tools in financial markets that provide investors with low-cost financial advice, usually based on individual characteristics like risk attitudes. In a portfolio choice experiment running over 10 weeks, we study how much investors benefit from robo advice. We also study whether robos increase financial market participation. The treatments are whether investors just receive advice, have a robo making all decisions for them, or have to trade on their own. We find no effect on initial market participation. But robos help investors to avoid mistakes, make rebalancing more frequent, and overall yield portfolios much closer to the utility maximizing ones. Robo-advisors that implement the recommendations by default do significantly better than those that just give advice.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Lambrecht, Marco & Oechssler, Jörg & Weidenholzer, Simon, 2024. "On the benefits of robo-advice in financial markets," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302354, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc24:302354
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/302354/1/vfs-2024-pid-106218.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Back, Kerry, 2010. "Asset Pricing and Portfolio Choice Theory," OUP Catalogue, Oxford University Press, number 9780195380613.
    2. Hackethal, Andreas & Haliassos, Michael & Jappelli, Tullio, 2012. "Financial advisors: A case of babysitters?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 509-524.
    3. Hans P. Binswanger, 1980. "Attitudes Toward Risk: Experimental Measurement in Rural India," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 62(3), pages 395-407.
    4. Eckel, Catherine C. & Grossman, Philip J., 2008. "Men, Women and Risk Aversion: Experimental Evidence," Handbook of Experimental Economics Results, in: Charles R. Plott & Vernon L. Smith (ed.), Handbook of Experimental Economics Results, edition 1, volume 1, chapter 113, pages 1061-1073, Elsevier.
    5. 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.
    6. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    7. Bhatia, Ankita & Chandani, Arti & Chhateja, Jagriti, 2020. "Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    8. repec:cup:judgdm:v:7:y:2012:i:1:p:25-47 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Nourallah, Mustafa, 2023. "One size does not fit all: Young retail investors’ initial trust in financial robo-advisors," Journal of Business Research, Elsevier, vol. 156(C).
    3. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    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. Goldzahl, Léontine, 2017. "Contributions of risk preference, time orientation and perceptions to breast cancer screening regularity," Social Science & Medicine, Elsevier, vol. 185(C), pages 147-157.
    6. Lex Borghans & Angela Lee Duckworth & James J. Heckman & Bas ter Weel, 2008. "The Economics and Psychology of Personality Traits," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
    7. Ndoye Niane, Aifa Fatimata & Burger, Kees, 2012. "Gender and Experimental Measurement of Producers Risk Attitude Towards Output Market Price and its Effects on Economic Performance," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126928, International Association of Agricultural Economists.
    8. Brenner, Lukas & Meyll, Tobias, 2020. "Robo-advisors: A substitute for human financial advice?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    9. Julius Pahlke & Sebastian Strasser & Ferdinand Vieider, 2015. "Responsibility effects in decision making under risk," Journal of Risk and Uncertainty, Springer, vol. 51(2), pages 125-146, October.
    10. Ferdinand M. Vieider & Mathieu Lefebvre & Ranoua Bouchouicha & Thorsten Chmura & Rustamdjan Hakimov & Michal Krawczyk & Peter Martinsson, 2015. "Common Components Of Risk And Uncertainty Attitudes Across Contexts And Domains: Evidence From 30 Countries," Journal of the European Economic Association, European Economic Association, vol. 13(3), pages 421-452, June.
    11. Sheremenko, Ganna & Magnan, Nicholas, 2015. "Gender-specific Risk Preferences and Fertilizer Use in Kenyan Farming Households," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205766, Agricultural and Applied Economics Association.
    12. Felix Holzmeister & Martin Holmén & Michael Kirchler & Matthias Stefan & Erik Wengström, 2023. "Delegation Decisions in Finance," Management Science, INFORMS, vol. 69(8), pages 4828-4844, August.
    13. Lata Gangadharan & Tarun Jain & Pushkar Maitra & Joe Vecci, 2022. "Lab-in-the-field experiments: perspectives from research on gender," The Japanese Economic Review, Springer, vol. 73(1), pages 31-59, January.
    14. Brunen, Ann-Christine & Laubach, Oliver, 2022. "Do sustainable consumers prefer socially responsible investments? A study among the users of robo advisors," Journal of Banking & Finance, Elsevier, vol. 136(C).
    15. Gloede, Oliver & Menkhoff, Lukas & Waibel, Hermann, 2015. "Shocks, Individual Risk Attitude, and Vulnerability to Poverty among Rural Households in Thailand and Vietnam," World Development, Elsevier, vol. 71(C), pages 54-78.
    16. Friedman, Dan & Sunder, Shyam, 2011. "Risky Curves: From Unobservable Utility to Observable Opportunity Sets," Santa Cruz Department of Economics, Working Paper Series qt36q158jt, Department of Economics, UC Santa Cruz.
    17. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    18. Alexis H. Villacis & Jeffrey R. Alwang & Victor Barrera, 2021. "Linking risk preferences and risk perceptions of climate change: A prospect theory approach," Agricultural Economics, International Association of Agricultural Economists, vol. 52(5), pages 863-877, September.
    19. James Ang & Rebel Cole & Daniel Lawson, 2010. "The Role of Owner in Capital Structure Decisions: An Analysis of Single-Owner Corporations," Journal of Entrepreneurial Finance, Pepperdine University, Graziadio School of Business and Management, vol. 14(3), pages 1-36, Fall.
    20. Filiz, Ibrahim & Judek, Jan René & Lorenz, Marco & Spiwoks, Markus, 2021. "Reducing algorithm aversion through experience," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).

    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:vfsc24:302354. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vfsocea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.