Journal
JOURNAL OF EMPIRICAL FINANCE
Volume 49, Issue -, Pages 81-106Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jempfin.2018.09.001
Keywords
Fund performance evaluation; Mutual fund and hedge fund; Skill vs. luck; Time-varying coefficient model
Categories
Funding
- National Natural Science Foundation of China (NNSFC) [71703045, 71803091, 71801117, 71803196]
- Program for HUST Academic Frontier Youth Team, PRC
- Ministry of Education in China Project of Humanities and Social Sciences [18YJC790015]
- National Social Science Foundation of China [18CJY061]
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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.
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