1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Άρθρα/Articles
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/2036
DC FieldValueLanguage
dc.contributor.authorKahya, Emel-
dc.contributor.authorTheodossiou, Panayiotis-
dc.date.accessioned2013-01-28T11:13:34Zen
dc.date.accessioned2013-05-16T08:22:23Z-
dc.date.accessioned2015-12-02T09:34:10Z-
dc.date.available2013-01-28T11:13:34Zen
dc.date.available2013-05-16T08:22:23Z-
dc.date.available2015-12-02T09:34:10Z-
dc.date.issued1999-
dc.identifier.citationReview of quantitative finance and accounting, 1999, vol. 13, iss. 4, pp. 323-345en_US
dc.identifier.issn15737179-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/2036-
dc.description.abstractThe ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logiten_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofReview of Quantitative Finance and Accountingen_US
dc.rights© Kluwer Academic Publishersen_US
dc.subjectFinanceen_US
dc.subjectAccountingen_US
dc.subjectStatistical methodsen_US
dc.subjectBusinessen_US
dc.titlePredicting corporate financial distress: a time-series CUSUM methodologyen_US
dc.typeArticleen_US
dc.collaborationRutgers Universityen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countryUnited Statesen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1023/A:1008326706404en_US
dc.dept.handle123456789/54en
dc.relation.issue4en_US
dc.relation.volume13en_US
cut.common.academicyear1998-1999en_US
dc.identifier.spage323en_US
dc.identifier.epage345en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
crisitem.journal.journalissn1573-7179-
crisitem.journal.publisherSpringer Nature-
crisitem.author.deptDepartment of Finance, Accounting and Management Science-
crisitem.author.facultyFaculty of Management and Economics-
crisitem.author.orcid0000-0001-5556-2594-
crisitem.author.parentorgFaculty of Management and Economics-
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