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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Miguel, A. F. & Chen, Y. (2021). Do machines beat humans? Evidence from mutual fund performance persistence. International Review of Financial Analysis. 78
Exportar Referência (IEEE)
A. M. Miguel and Y. Chen,  "Do machines beat humans? Evidence from mutual fund performance persistence", in Int. Review of Financial Analysis, vol. 78, 2021
Exportar BibTeX
@article{miguel2021_1732357058607,
	author = "Miguel, A. F. and Chen, Y.",
	title = "Do machines beat humans? Evidence from mutual fund performance persistence",
	journal = "International Review of Financial Analysis",
	year = "2021",
	volume = "78",
	number = "",
	doi = "10.1016/j.irfa.2021.101913",
	url = "https://www.sciencedirect.com/journal/international-review-of-financial-analysis"
}
Exportar RIS
TY  - JOUR
TI  - Do machines beat humans? Evidence from mutual fund performance persistence
T2  - International Review of Financial Analysis
VL  - 78
AU  - Miguel, A. F.
AU  - Chen, Y.
PY  - 2021
SN  - 1057-5219
DO  - 10.1016/j.irfa.2021.101913
UR  - https://www.sciencedirect.com/journal/international-review-of-financial-analysis
AB  - We study the performance persistence of quantitative actively managed US equity funds. We show that the persistence of quantitative funds originates from poor performers and that there are reversals at the top of the performance scale, which is no different from the widely accepted evidence in the mutual fund literature. When testing for differences in performance persistence between quantitative and non–quantitative funds, we find no differences for poorly performing funds, but we observe significantly more reversals for quantitative funds at the top of the performance distribution. We also find that the differences in performance persistence are not explained by differences in flow–induced incentives to generate alpha, as there is no heterogeneity in investors preferences when allocating capital to these funds. Overall our results are consistent with machines having less skill than their human counterparts.
ER  -