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A Robustness Test On Chinese Open-ended Fund Return:Fund Perspective And Manager Perspective

Posted on:2019-05-20Degree:MasterType:Thesis
Country:ChinaCandidate:X W HuangFull Text:PDF
GTID:2429330566493762Subject:Finance
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With the flourishing development of fund market,star funds are becoming the first choice of Chinese investors' investment portfolio.However,most of fund rating companies analyze the performance of funds instead of fund managers,despite the difference between mutual fund return and mutual fund manager skill due to the common decision making and frequent change of fund managers.This paper constructs the samples of fund and fund manager,using a comprehensive data of Chinese mutual equity fund and hybrid fund spanning 2001 to 2016,and separately examines their performance through bootstrap simulations based on Carhart four-factor model.Meanwhile we attribute their performance to either timing-picking or stock-picking and investigate their time-varying characteristics through rolling and recursive regression.The empirical result illustrates that:(1)Most of Chinese mutual funds can achieve excess return,but the fund-evalution-system will overestimate unskilled fund managers and underestimate the skilled ones.(2)Significance test indicates that fund-evalution-system can only measure funds with skill appropriately while fund-manager-evaluation-system can accurately measure both skilled and unskilled managers.(3)Although the time-picking ability of Chinese mutual fund is insignificant,the percentage of skilled managers is far beyond that of skilled funds,with increasing stability and significance of its stocking-picking ability.In conclusion,fund-manager-evaluation-system can better focus on individual variation of managers.Considering the actual situation of Chinese mutual fund market and the average tenure of fund manager,fund-manager-evaluation-system is capable to provide a more accurate measurement for fund manager skill.
Keywords/Search Tags:Fund Manager Skills, Fund Performance, Open-Ended Fund, Carhart Four Factor Model, Bootstrap Resampling Regression
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