{"title":"共同基金表现不佳的分解","authors":"Jin-Li Hu, Tzu-Pu Chang","doi":"10.1080/17446540701720675","DOIUrl":null,"url":null,"abstract":"This article follows a three-stage data envelopment analysis (DEA) approach proposed by Fried et al. (2002) to decompose mutual fund underperformance, in order to obtain pure managerial performance. In the first stage, DEA is used to compute each fund's performance. In the second stage, a stochastic frontier regression decomposes fund underperformance into characteristics (including fund and management attributes), managerial inefficiency, and statistical noise. In the third stage, DEA with slack-adjusted data is used to find out the pure performance. It is found that a fund's performance significantly increases with its size, previous performance, manager's tenure and education, while it decreases with the age of the fund and number of managed funds.","PeriodicalId":345744,"journal":{"name":"Applied Financial Economics Letters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Decomposition of mutual fund underperformance\",\"authors\":\"Jin-Li Hu, Tzu-Pu Chang\",\"doi\":\"10.1080/17446540701720675\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article follows a three-stage data envelopment analysis (DEA) approach proposed by Fried et al. (2002) to decompose mutual fund underperformance, in order to obtain pure managerial performance. In the first stage, DEA is used to compute each fund's performance. In the second stage, a stochastic frontier regression decomposes fund underperformance into characteristics (including fund and management attributes), managerial inefficiency, and statistical noise. In the third stage, DEA with slack-adjusted data is used to find out the pure performance. It is found that a fund's performance significantly increases with its size, previous performance, manager's tenure and education, while it decreases with the age of the fund and number of managed funds.\",\"PeriodicalId\":345744,\"journal\":{\"name\":\"Applied Financial Economics Letters\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Financial Economics Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/17446540701720675\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Financial Economics Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17446540701720675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
摘要
本文采用Fried et al.(2002)提出的三阶段数据包络分析(DEA)方法对共同基金表现不佳进行分解,以获得纯粹的管理绩效。在第一阶段,使用DEA计算每个基金的绩效。在第二阶段,随机前沿回归将基金表现不佳分解为特征(包括基金和管理属性)、管理效率低下和统计噪声。第三阶段,采用经松弛调整数据的DEA计算纯绩效。研究发现,基金的绩效随基金规模、过往业绩、基金管理人任期和学历显著增加,随基金年龄和管理基金数量减少。
This article follows a three-stage data envelopment analysis (DEA) approach proposed by Fried et al. (2002) to decompose mutual fund underperformance, in order to obtain pure managerial performance. In the first stage, DEA is used to compute each fund's performance. In the second stage, a stochastic frontier regression decomposes fund underperformance into characteristics (including fund and management attributes), managerial inefficiency, and statistical noise. In the third stage, DEA with slack-adjusted data is used to find out the pure performance. It is found that a fund's performance significantly increases with its size, previous performance, manager's tenure and education, while it decreases with the age of the fund and number of managed funds.