{"title":"收缩调整夏普比率:一种改进的共同基金选择方法","authors":"Moshe Levy, Richard Roll","doi":"10.3905/joi.2022.1.252","DOIUrl":null,"url":null,"abstract":"Mutual fund selection is a notoriously difficult task, because past performance is a poor predictor of future performance. We propose a fund performance measure that incorporates a simple idea: shrinkage, in the sense of Bayes-James-Stein, should be applied to gross return parameters, but not to fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves the prediction of out-of-sample performance relative to existing methods. The best prediction is obtained when fees are weighed five times heavier than sample returns.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"7 - 23"},"PeriodicalIF":0.6000,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Shrinkage Adjusted Sharpe Ratio: An Improved Method for Mutual Fund Selection\",\"authors\":\"Moshe Levy, Richard Roll\",\"doi\":\"10.3905/joi.2022.1.252\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mutual fund selection is a notoriously difficult task, because past performance is a poor predictor of future performance. We propose a fund performance measure that incorporates a simple idea: shrinkage, in the sense of Bayes-James-Stein, should be applied to gross return parameters, but not to fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves the prediction of out-of-sample performance relative to existing methods. The best prediction is obtained when fees are weighed five times heavier than sample returns.\",\"PeriodicalId\":45504,\"journal\":{\"name\":\"Journal of Investing\",\"volume\":\"32 1\",\"pages\":\"7 - 23\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Investing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3905/joi.2022.1.252\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Investing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3905/joi.2022.1.252","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
摘要
众所周知,选择共同基金是一项困难的任务,因为过去的业绩对未来的业绩预测很差。我们提出了一个基金业绩指标,其中包含了一个简单的想法:Bayes James Stein意义上的收缩应该应用于总回报参数,而不是已知的费用。相对于现有方法,所提出的收缩调整夏普比(SAS)显著改进了对样本外性能的预测。当费用比样本回报重五倍时,可以获得最佳预测。
The Shrinkage Adjusted Sharpe Ratio: An Improved Method for Mutual Fund Selection
Mutual fund selection is a notoriously difficult task, because past performance is a poor predictor of future performance. We propose a fund performance measure that incorporates a simple idea: shrinkage, in the sense of Bayes-James-Stein, should be applied to gross return parameters, but not to fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves the prediction of out-of-sample performance relative to existing methods. The best prediction is obtained when fees are weighed five times heavier than sample returns.