Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/206358 
Year of Publication: 
2017
Citation: 
[Journal:] BRQ Business Research Quarterly [ISSN:] 2340-9436 [Volume:] 20 [Issue:] 1 [Publisher:] Elsevier España [Place:] Barcelona [Year:] 2017 [Pages:] 45-62
Publisher: 
Elsevier España, Barcelona
Abstract: 
This study investigates uncertainty levels of various industries and tries to determine financial ratios having the greatest information content in determining the set of industry characteristics. It then uses these ratios to develop industry specific financial distress models. First, we employ factor analysis to determine the set of ratios that are most informative in specified industries. Second, we use a method based on the concept of entropy to measure the level of uncertainty in industries and also to single out the ratios that best reflect the uncertainty levels in specific industries. Finally, we conduct a logistic regression analysis and derive industry specific financial distress models which can be used to judge the predictive ability of selected financial ratios for each industry. The results show that financial ratios do indeed echo industry characteristics and that information content of specific ratios varies among different industries. Our findings show diverging impact of industry characteristics on companies; and thus the necessity of constructing industry specific financial distress models."
Subjects: 
Uncertainty
Information theory
Financial ratios
Financial distress modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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