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On the benefits of robo-advice in financial markets

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  • 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.

Suggested Citation

  • 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.
    • Handle: RePEc:awi:wpaper:0734
    Note: This paper is part of http://archiv.ub.uni-heidelberg.de/volltextserver/view/schriftenreihen/sr-3.html
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    References listed on IDEAS

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    1. Back, Kerry, 2010. "Asset Pricing and Portfolio Choice Theory," OUP Catalogue, Oxford University Press, number 9780195380613, Decembrie.
    2. 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.
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    4. 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.
    5. 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).
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    Keywords

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    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

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