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Robo-advising: a dynamic mean-variance approach

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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s42521-021-00028-4

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
St Peter's College
Role:
Author
ORCID:
0000-0001-5299-5730


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Funder identifier:
https://ror.org/01h0zpd94


Publisher:
Springer
Journal:
Digital Finance More from this journal
Volume:
3
Issue:
2
Pages:
81-97
Publication date:
2021-06-16
Acceptance date:
2021-02-07
DOI:
EISSN:
2524-6186
ISSN:
2524-6984


Language:
English
Keywords:
Pubs id:
1781990
Local pid:
pubs:1781990
Deposit date:
2026-01-21
ARK identifier:

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