Systematic review of bankruptcy prediction models: towards a framework for tool selection

Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O. and Bilal, M. (2018) Systematic review of bankruptcy prediction models: towards a framework for tool selection. Expert Systems with Applications. 94, pp. 164-184. 0957-4174.

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Item Type:	Article
Abstract:	The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.
Uncontrolled Keywords:	Bankruptcy prediction tools, financial ratios, error types, systematic review, tool selection framework, artificial intelligence tools, statistical tools
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Creators:	Alaka, Hafiz A, Oyedele, Lukumon O, Owolabi, Hakeem A, Kumar, Vikas, Ajayi, Saheed O, Akinade, Olugbenga O and Bilal, Muhammad
Publisher:	Elsevier
Faculties, Divisions and Institutes:	University Faculties, Divisions and Research Centres - OLD > Faculty of Business & Law > Business Faculties > Faculty of Business & Law > International Strategy & Business
Date:	15 March 2018
Date Type:	Publication
Page Range:	pp. 164-184
Journal or Publication Title:	Expert Systems with Applications
Volume:	94
Language:	English
DOI:	https://doi.org/10.1016/j.eswa.2017.10.040
ISSN:	0957-4174
Status:	Published / Disseminated
Refereed:	Yes
URI:	http://nectar.northampton.ac.uk/id/eprint/9991

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