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Robo-advising: a dynamic mean-variance approach

Author

Listed:
  • Min Dai

    (National University of Singapore)

  • Hanqing Jin

    (The University of Oxford)

  • Steven Kou

    (Boston University)

  • Yuhong Xu

    (Soochow University)

Abstract

In contrast to traditional financial advising, robo-advising needs to elicit investors’ risk profile via several simple online questions and provide advice consistent with conventional investment wisdom, e.g., rich and young people should invest more in risky assets. To meet the two challenges, we propose to do the asset allocation part of robo-advising using a dynamic mean-variance criterion over the portfolio’s log returns. We obtain analytical and time-consistent optimal portfolio policies under jump-diffusion models and regime-switching models.

Suggested Citation

  • Min Dai & Hanqing Jin & Steven Kou & Yuhong Xu, 2021. "Robo-advising: a dynamic mean-variance approach," Digital Finance, Springer, vol. 3(2), pages 81-97, June.
    • Handle: RePEc:spr:digfin:v:3:y:2021:i:2:d:10.1007_s42521-021-00028-4
    DOI: 10.1007/s42521-021-00028-4
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    References listed on IDEAS

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    Cited by:

    1. 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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    More about this item

    Keywords

    FinTech; Dynamic mean-variance; Time-consistence; Time-varying mean returns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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