Non-Parametric Statistic for Testing Cumulative Abnormal Stock Returns

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annif.suggestionsrisk management|yield|events|security market|statistical methods|shares|tests|econometrics|prices|financial markets|enen
annif.suggestions.linkshttp://www.yso.fi/onto/yso/p3134|http://www.yso.fi/onto/yso/p4629|http://www.yso.fi/onto/yso/p2108|http://www.yso.fi/onto/yso/p12456|http://www.yso.fi/onto/yso/p3127|http://www.yso.fi/onto/yso/p11398|http://www.yso.fi/onto/yso/p3971|http://www.yso.fi/onto/yso/p13480|http://www.yso.fi/onto/yso/p750|http://www.yso.fi/onto/yso/p7536en
dc.contributor.authorPynnönen, Seppo
dc.contributor.departmentfi=Ei tutkimusalustaa|en=No platform|-
dc.contributor.facultyfi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations|-
dc.contributor.organizationfi=Vaasan yliopisto|en=University of Vaasa|
dc.date.accessioned2022-05-25T11:04:26Z
dc.date.accessioned2025-06-25T13:45:02Z
dc.date.available2022-05-25T11:04:26Z
dc.date.issued2022-03-23
dc.description.abstractDue to the non-normality of stock returns, nonparametric rank tests are gaining accceptance relative to parametric tests in financial economics event studies. In rank tests, financial assets’ multiple day cumulative abnormal returns (CARs) are replaced by cumulated ranks. This paper proposes modifications to the existing approaches to improve robustness to cross-sectional correlation of returns arising from calendar time overlapping event windows. Simulations show that the proposed rank test is well specified in testing CARs and is robust towards both complete and partial overlapping event windows.-
dc.description.notification© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).-
dc.description.reviewstatusfi=vertaisarvioitu|en=peerReviewed|-
dc.format.bitstreamtrue
dc.format.contentfi=kokoteksti|en=fulltext|-
dc.format.extent14-
dc.identifier.olddbid16455
dc.identifier.oldhandle10024/14137
dc.identifier.urihttps://osuva.uwasa.fi/handle/11111/2670
dc.identifier.urnURN:NBN:fi-fe2022052538776-
dc.language.isoeng-
dc.publisherMDPI-
dc.relation.doi10.3390/jrfm15040149-
dc.relation.ispartofjournalJournal of Risk and Financial Management-
dc.relation.issn1911-8074-
dc.relation.issn1911-8066-
dc.relation.issue4-
dc.relation.urlhttps://doi.org/10.3390/jrfm15040149-
dc.relation.volume15-
dc.rightsCC BY 4.0-
dc.source.identifierWOS:000785033600001-
dc.source.identifierhttps://osuva.uwasa.fi/handle/10024/14137
dc.subjectclustered event days-
dc.subjectcross-sectional correlation-
dc.subjectcumulated ranks-
dc.subjecteconomics-
dc.subjectevent study-
dc.subjectFinance-
dc.subjectrank test-
dc.subjectstandardized abnormal returns-
dc.subject.disciplinefi=Matematiikka|en=Mathematics|
dc.titleNon-Parametric Statistic for Testing Cumulative Abnormal Stock Returns-
dc.type.okmfi=A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä|en=A1 Peer-reviewed original journal article|sv=A1 Originalartikel i en vetenskaplig tidskrift|-
dc.type.publicationarticle-
dc.type.versionpublishedVersion-

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