{"title":"在横断面收益可预测性中,大多数声称的统计结果可能是正确的","authors":"Andrew Y. Chen","doi":"10.2139/ssrn.3912915","DOIUrl":null,"url":null,"abstract":"Harvey, Liu, and Zhu (2016) “argue that most claimed research findings in financial economics are likely false.” Surprisingly, their false discovery rate (FDR) estimates suggest most are true. I revisit their results by developing non- and semi-parametric FDR estimators that account for publication bias and empirical correlations. These estimators provide simple closed-form expressions and reliably produce an upper bound on the FDR in simulations that cluster-bootstrap from empirical predictor returns. Applying these estimators to the Chen-Zimmermann dataset of 205 predictors, I find that most claimed statistical findings in the cross-sectional predictability literature are likely true.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Most Claimed Statistical Findings in Cross-Sectional Return Predictability Are Likely True\",\"authors\":\"Andrew Y. Chen\",\"doi\":\"10.2139/ssrn.3912915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Harvey, Liu, and Zhu (2016) “argue that most claimed research findings in financial economics are likely false.” Surprisingly, their false discovery rate (FDR) estimates suggest most are true. I revisit their results by developing non- and semi-parametric FDR estimators that account for publication bias and empirical correlations. These estimators provide simple closed-form expressions and reliably produce an upper bound on the FDR in simulations that cluster-bootstrap from empirical predictor returns. Applying these estimators to the Chen-Zimmermann dataset of 205 predictors, I find that most claimed statistical findings in the cross-sectional predictability literature are likely true.\",\"PeriodicalId\":139983,\"journal\":{\"name\":\"Econometrics: Econometric & Statistical Methods - Special Topics eJournal\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometrics: Econometric & Statistical Methods - Special Topics eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3912915\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3912915","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Most Claimed Statistical Findings in Cross-Sectional Return Predictability Are Likely True
Harvey, Liu, and Zhu (2016) “argue that most claimed research findings in financial economics are likely false.” Surprisingly, their false discovery rate (FDR) estimates suggest most are true. I revisit their results by developing non- and semi-parametric FDR estimators that account for publication bias and empirical correlations. These estimators provide simple closed-form expressions and reliably produce an upper bound on the FDR in simulations that cluster-bootstrap from empirical predictor returns. Applying these estimators to the Chen-Zimmermann dataset of 205 predictors, I find that most claimed statistical findings in the cross-sectional predictability literature are likely true.