Economics at your fingertips  

Time-varying skills (versus luck) in U.S. active mutual funds and hedge funds

Biqing Cai, Tingting Cheng and Cheng Yan

Journal of Empirical Finance, 2018, vol. 49, issue C, 81-106

Abstract: In this paper, we develop a nonparametric methodology for estimating and testing time-varying fund alphas and betas as well as their long-run counterparts (i.e., their time-series averages). Traditional linear factor model arises as a special case without time variation in coefficients. Monte Carlo simulation evidence suggests that our methodology performs well in finite samples. Applying our methodology to U.S. mutual funds and hedge funds, we find most fund alphas decrease with time. Combining our methodology with the bootstrap method which controls for ‘luck’, positive long-run alphas of mutual funds but hedge funds disappear, while negative long-run alphas of both mutual and hedge funds remain. We further check the robustness of our results by altering benchmarks, fund skill indicators and samples.

Keywords: Fund performance evaluation; Mutual fund and hedge fund; Skill vs. luck; Time-varying coefficient model (search for similar items in EconPapers)
JEL-codes: C1 G1 G2 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-05-11
Handle: RePEc:eee:empfin:v:49:y:2018:i:c:p:81-106