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A reexamination of stock return predictability

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dc.contributor.authorChoi, Yongok-
dc.contributor.authorJacewitz, Stefan-
dc.contributor.authorPark, Joon Y.-
dc.date.available2020-07-09T08:21:00Z-
dc.date.issued2016-05-
dc.identifier.issn0304-4076-
dc.identifier.issn1872-6895-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/41619-
dc.description.abstractWe provide a simple and innovative approach to test for predictability in stock returns. Our approach consists of two methodologies, time change and instrumental variable estimation, which are employed respectively to deal effectively with persistent stochastic volatility in stock returns and endogenous nonstationarity in their predictors. These are prominent characteristics of the data used in predictive regressions, which are known to have a substantial impact on the test of predictability, if not properly taken care of. Our test finds no evidence supporting stock return predictability, at least if we use the common predictive ratios such as dividend-price and earnings-price ratios. (C) 2016 Elsevier B.V. All rights reserved.-
dc.format.extent22-
dc.language영어-
dc.language.isoENG-
dc.publisherELSEVIER SCIENCE SA-
dc.titleA reexamination of stock return predictability-
dc.typeArticle-
dc.identifier.doi10.1016/j.jeconom.2015.02.048-
dc.identifier.bibliographicCitationJOURNAL OF ECONOMETRICS, v.192, no.1, pp 168 - 189-
dc.description.isOpenAccessN-
dc.identifier.wosid000372384900011-
dc.identifier.scopusid2-s2.0-84960194299-
dc.citation.endPage189-
dc.citation.number1-
dc.citation.startPage168-
dc.citation.titleJOURNAL OF ECONOMETRICS-
dc.citation.volume192-
dc.type.docTypeArticle-
dc.publisher.location스위스-
dc.subject.keywordAuthorPredictive regression-
dc.subject.keywordAuthorTime change-
dc.subject.keywordAuthorCauchy estimator-
dc.subject.keywordAuthorNonstationarity-
dc.subject.keywordAuthorStochastic volatility-
dc.subject.keywordPlusUNIT-ROOT TESTS-
dc.subject.keywordPlusVOLATILITY MODELS-
dc.subject.keywordPlusEQUITY PREMIUM-
dc.subject.keywordPlusRISK PREMIA-
dc.subject.keywordPlusSAMPLE-
dc.subject.keywordPlusREGRESSIONS-
dc.subject.keywordPlusCONSUMPTION-
dc.subject.keywordPlusHYPOTHESIS-
dc.subject.keywordPlusINFERENCE-
dc.subject.keywordPlusEARNINGS-
dc.relation.journalResearchAreaBusiness & Economics-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalResearchAreaMathematical Methods In Social Sciences-
dc.relation.journalWebOfScienceCategoryEconomics-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategorySocial Sciences, Mathematical Methods-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
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