Does active management add value? New evidence from a quantile regression approach
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Other documents of the author: Matallín Sáez, Juan Carlos; Soler-Dominguez, Amparo; Tortosa-Ausina, Emili
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comunitat-uji-handle2:10234/8648
comunitat-uji-handle3:10234/8649
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INVESTIGACIONMetadata
Title
Does active management add value? New evidence from a quantile regression approachDate
2019-06Publisher
Taylor & FrancisBibliographic citation
MATALLÍN-SÁEZ, Juan Carlos; SOLER-DOMÍNGUEZ, Amparo; TORTOSA-AUSINA, Emili. Does active management add value? New evidence from a quantile regression approach. Journal of the Operational Research Society, 2019, 1-18.Type
info:eu-repo/semantics/articlePublisher version
https://www.tandfonline.com/doi/pdf/10.1080/01605682.2019.1612549Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
While it has long been recognised that active management is an important issue in the
area of mutual fund performance, little consensus has been reached about the value managers’
abilities can add. This study examines ... [+]
While it has long been recognised that active management is an important issue in the
area of mutual fund performance, little consensus has been reached about the value managers’
abilities can add. This study examines funds’ and managers’ characteristics in an attempt
to understand their influence on mutual fund efficiency. We explore these issues in a twostage approach, considering partial frontier estimators (order-m, order-α) to assess performance
in the first stage, and quantile regression in the second stage to isolate the determinants of
efficiency. This combination of methodologies has barely been considered to date in the field of
operations research. Our findings are of interest to both academics and practitioners as they
shed light on the differences among funds as well as among managers. Our analysis provides
some arguments to guide fund selection and points to some managerial features investors might
consider taking into account. In addition, some of the differences in performance among funds
are rather intricate because both the magnitude of the estimated regression coefficients and
their significance varies depending on the quantile of the distribution of fund performance,
suggesting that some relevant trends might be concealed by conditional-mean models such as
Tobit or OLS. [-]
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