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Algorithmic portfolio choice: lessons from panel survey data

Author

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  • Bernd Scherer

    (Deutsche Asset Management
    EDHEC Risk
    WU Wien)

Abstract

Automated asset management offerings algorithmically assign risky portfolios to individual investors based on investor characteristics such as age, net income, or self-assessment of risk aversion. Using new German household panel data, we investigate the key household characteristics that drive private asset allocation decisions. This information allows us to assess which set of variables should be included in algorithmic portfolio advice. Using heavily cross-validated classification trees, we find that a combination of household balance sheet variables—describing the ability to take risks (e.g., net wealth)—and household personal characteristics—describing the willingness to take risks (e.g., risk aversion)—best explain the cross-sectional variation in household portfolio choice. Our empirical evidence is in line with models of portfolio choice under decreasing relative risk aversion and fixed investment costs. The results suggest the utility of a more holistic modeling of household characteristics. Including background risks in the form of household leverage not only makes investment sense, but is also the new regulatory reality under MIFID II rules. Robo-advisors are strongly advised to act accordingly.

Suggested Citation

  • Bernd Scherer, 2017. "Algorithmic portfolio choice: lessons from panel survey data," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(1), pages 49-67, February.
    • Handle: RePEc:kap:fmktpm:v:31:y:2017:i:1:d:10.1007_s11408-016-0282-8
    DOI: 10.1007/s11408-016-0282-8
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    References listed on IDEAS

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    1. Mervyn A. King & Jonathan I. Leape, 1987. "Asset Accumulation, Information, and the Life Cycle," NBER Working Papers 2392, National Bureau of Economic Research, Inc.
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    3. Mark Grinblatt & Matti Keloharju & Juhani Linnainmaa, 2011. "IQ and Stock Market Participation," Journal of Finance, American Finance Association, vol. 66(6), pages 2121-2164, December.
    4. Hsu, Jason C., 2012. "What drives equity market non-participation?," The North American Journal of Economics and Finance, Elsevier, vol. 23(1), pages 86-114.
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    Cited by:

    1. Xiaonan Chen & Jianfeng Song, 2022. "Influence Path Analysis of Rural Household Portfolio Selection: A Empirical Study Using Structural Equation Modelling Method," The Journal of Real Estate Finance and Economics, Springer, vol. 64(2), pages 298-322, February.

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    More about this item

    Keywords

    Robo-advice; Household portfolio choice; Panel data; Regression trees;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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