The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
The Promises and Pitfalls of Robo-Advising
Abstract
We study the introduction of a wealth-management robo-adviser that constructs portfolios tailored to investors’ holdings and preferences. Adopters are similar to non-adopters in terms of demographics and prior interactions with human advisers but tend to be more active and have greater assets under management. Investors adopting robo-advising experience diversification benefits. Ex ante undiversified investors increase stock holdings and hold portfolios with less volatility and better returns. Already well-diversified investors hold fewer stocks, yet see some reduction in volatility, and trade more after adoption. All investors increase attention based on online account logins. We find that adopters exhibit declines in prominent behavioral biases, including the disposition, trend chasing, and rank effect. Our results emphasize the promises and pitfalls of robo-advising tools, which are becoming ubiquitous all over the world.Received May 31, 2017; editorial decision August 21, 2018 by Editor Wei Jiang.
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Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors
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FinTech and banking: What do we know?
Thakor, A. V. (2020) · Journal of Financial Intermediation · doi:10.1016/j.jfi.2019.100833 · Cited by 1,378
Keding, C. (2021) · Management Review Quarterly · doi:10.1007/s11301-020-00181-x · Cited by 231
Reducing algorithm aversion through experience
Filiz, I., Judek, J. R., Lorenz, M., & Spiwoks, M. (2021) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2021.100524 · Cited by 88
How Costly are Cultural Biases? Evidence from FinTech
D'Acunto, F., Ghosh, P., & Rossi, A. G. (2022) · doi:10.2139/ssrn.4147353 · Cited by 11
Self-citation
Metadata
- Authors
- D’Acunto, F., Prabhala, N., & Rossi, A. G.
- Venue
- The Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-04-04
- Volume
- 32
- Issue
- 5
- Pages
- 1983-2020
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W3123379013
- Cited by
- 496
- References
- 8
- FWCI
- 48.43
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
