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dc.contributor.author
Ezovalis, Stamatis
en
dc.date.accessioned
2021-09-08T09:06:51Z
dc.date.available
2021-09-08T09:06:51Z
dc.date.issued
2021-09-08
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29780
dc.rights
Default License
dc.subject
Business failure
en
dc.subject
Logit analysis
en
dc.subject
Bankruptcy prediction
en
dc.subject
Statistical analysis
en
dc.title
Why Business Fail - A prediction model for SME’s failure in Greece
en
heal.type
masterThesis
en_US
heal.dateAvailable
2021-05-19
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Economics, Business Administration and Legal Studies, Executive MBA
en_US
heal.publicationDate
2021-02-28
heal.abstract
Business failure is one of the most common terms heard today when the current economic crisis in Greece is brought up. It can be characterised as a company's inability to cope with its obligations to its creditors, or in other words, its inability to pay its debts. Internal and external factors such as management efficiency, competitors, and funding ability may all lead to the risk of bankruptcy. Indications of a possible corporate bankruptcy are apparent well before the real bankruptcy takes place. As a result, designing models that forecast imminent financial collapse has become an important aspect of corporate finance literature in order to assist management in refocusing their resources, re-evaluating their corporate strategies, and eliminating losses. This work explores the literature on predicting financial distress and decision making as well as assessing the probability of bankruptcy based on solvent Greek SMEs during the period 2014 to 2019 via logit analysis. To do so, we'll look at a sample of bankrupt and non-bankrupt companies, as well as a collection of economic and financial ratios. These ratios are determined using the data from the firms' balance sheets and income statements. This financial analysis, which is calculated using the ratios, is necessary to evaluate how healthy the company is financially, therefore assisting investors, creditors and managers when predicting favourable situations or economic difficulties. In this analysis, a four-variable Logit model developed via a forward-stepwise selection protocol correctly predicted 83% of 92 matched-samples one year before default.
en
heal.advisorName
Leventis, Stergios
en
heal.committeeMemberName
Laspita, Stavroula
en
heal.committeeMemberName
Archontakis, Fragiskos
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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