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Predicting Insolvency: A comparison between discriminant analysis and logistic regression using principal components
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of risk. The results shows that the logistic regression outperforms the discriminant analysis in classifying all of the groups except for the middle one. We conclude that this is in line with previous studies.

Place, publisher, year, edition, pages
2014. , 33 p.
Keyword [en]
Risk modelling, Discriminant analysis, Logisistic regression, Principal components, Solvency
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-243289OAI: oai:DiVA.org:uu-243289DiVA: diva2:786886
Subject / course
Statistics
Supervisors
Available from: 2015-02-09 Created: 2015-02-06 Last updated: 2015-02-09Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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