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The Promises and Pitfalls of Robo-advising

Author

Listed:
  • Francesco D'Acunto
  • Nagpurnanand Prabhala
  • Alberto G. Rossi

Abstract

We study a robo-advising portfolio optimizer that constructs tailored strategies based on in- vestors’ holdings and preferences. Adopters are similar to non-adopters in terms of demographics, but have more assets under management, trade more, and have higher risk-adjusted performance. The robo-advising tool has opposite effects across investors with different levels of diversification before adoption. It increases portfolio diversification and decreases volatility for those that held less than 5 stocks before adoption. These investors’ portfolios perform better after using the tool. At the same time, robo-advising barely affects diversification for investors that held more than 10 stocks before adoption. These investors trade more after adoption with no effect on average performance. For all investors, robo-advising reduces - but does not fully eliminate - pervasive behavioral biases such as the disposition effect, trend chasing, and the rank effect, and increases attention based on online account logins. Our results emphasize the promises and pitfalls of robo-advising tools, which are becoming ubiquitous all over the world.

Suggested Citation

  • Francesco D'Acunto & Nagpurnanand Prabhala & Alberto G. Rossi, 2018. "The Promises and Pitfalls of Robo-advising," CESifo Working Paper Series 6907, CESifo.
    • Handle: RePEc:ces:ceswps:_6907
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    Cited by:

    1. Ryan Clements, 2019. "Regulating Fintech In Canada And The United States: Comparison, Challenges And Opportunities," SPP Research Papers, The School of Public Policy, University of Calgary, vol. 12(23), January.
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    3. Gregor Dorfleitner & Lars Hornuf & Martina Weber, 2018. "Paralyzed by Shock: The Portfolio Formation Behavior of Peer-to-Business Lending Investors," CESifo Working Paper Series 7092, CESifo.
    4. Rudolf Kerschbamer & Daniel Neururer & Matthias Sutter, 2019. "Credence goods markets and the informational value of new media: A natural field experiment," Working Papers 2019-02, Faculty of Economics and Statistics, Universität Innsbruck.

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    More about this item

    Keywords

    FinTech; portfolio choice; behavioral finance; individual investors; financial literacy; technology adoption;
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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