POSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING

Pēteris Grabusts

Abstract


Prediction of corporate bankruptcy is a study topic of great interest.
Under the conditions of the modern free market, early diagnostics of unfavourable
development trends of company’s activity or bankruptcy becomes a matter of
great importance. There is no general method which would allow one to forecast
unfavourable consequence with a high confidence degree. This paper focuses on
the analysis of the approaches that can be used to perform an early bankruptcy
diagnostics- in previous research multivariate discriminant analysis (MDA), neural
network based approach and rule extraction method have been examined. Lately,
time series clustering approach has become popular and its feasibility for
bankruptcy data analysis is being investigated. Experiments carried out validate
the use of such methods in the given class of tasks. As a novelty, an attempt to
apply time series clustering method to the analysis of bankruptcy data is made.


Keywords


bankruptcy prediction; financial ratio; time series; clustering

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References


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http://godefroy.sdf-eu.org/apl95/ratios95.zip - skatīts 26.02.2010.




DOI: http://dx.doi.org/10.17770/lner2010vol1.2.1779

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