Does active management add value? New evidence from a quantile regression approach

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 funds’ and managers’ characteristics in an attempt to understand their influence on mutual fund efficiency. We explore these issues in a two-stage 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.

Keywords:

• Mutual fund
• partial frontier
• performance
• quantile regression

Acknowledgements

*We are grateful to Giacomo Nocera, Nicolas Nalpas, Lucía Morales, Manuel J. Rocha Armada, José Luis Sarto and two anonymous referees whose comments have contributed to the improvement of the article’s overall quality.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Actually, in some fields such as the determinants of local government performance, some surveys have specifically revised the literature on its determinants (see, for instance Aiello and Bonanno, 2019; Narbón-Perpiñá and De Witte, 2018). More recently, and for the interested reader, Daraio et al. (2019) have reviewed all empirical surveys that, in the field of efficiency and productivity analysis using frontier techniques, are available so far.

2 For instance, the efficiency scores obtained using linear programming techniques are dependent by construction.

3 In the case of non-normal distributions, Glawischnig and Sommersguter-Reichmann (2010) consider taking non-central measures by using information about skewness and kurtosis. See also Briec et al. (2007) and Brandouy et al. (2013). We dealt with the negative values found both for both skewness and kurtosis by rescaling both variables.

Additional information

Funding

We would also like to acknowledge the financial support from the Ministerio de Economía y Competitividad (ECO2014-55221-P and ECO2017-85746-P), Universitat Jaume I (UJI-B2017-14 and UJI-B2017-33), and Generalitat Valenciana (PROMETEO/2018/102).