An analysis of the key-variables of default risk using complex systems
Abstract
This paper presents a method for the definition of the variables that determine the default. The choice of key-variables in default risk analysis is very important for banks and firms. Indeed, when a firm goes bankrupt, serious problems arise for all the stakeholders of the firm, as well as, for all subjects having relations with the failed subject. A feed-forward neural network with back propagation is used in this paper to classify firms and to divide them according to two groups: failed and not failed firms. The analysis of the model results shows that 13 variables, out of the 41 variables introduced in the network, are those that determine the default. This result is validated by using different techniques, such as the cluster analysis. Moreover, this study also finds that, by reducing the data introduced in the model, neural network models provide a very good performance. The conclusion is that Artificial Neural Network (ANN) models are able to identify key-variables for default risk.
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Default risk is more often accurately predicted by artificial neural networks.
“The findings of the research indicate that default risk is more often accurately predicted by ANNs.”
From A Neural Network Approach to Estimating the Allowance for Bad Debt (2011) · cites this paper as Falavigna (2008)
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A Neural Network Approach to Estimating the Allowance for Bad Debt
Joyner, D. (2011) · VCU Libraries · doi:10.25772/h720-xy24 · Cited by 2 · see citing works
- Authors
- Falavigna, G.
- Corresponding author
- Falavigna, Greta
- ORCID
- Falavigna, Greta
- Venue
- International Journal of Business Performance Management
- Publisher
- Inderscience Publishers
- Published
- 2008
- Volume
- 10
- Issue
- 2/3
- Pages
- 202
- ISSN
- 1368-4892 (ISSN-L)
- 1741-5039
- OpenAlex
- W2166719975
- Cited by
- 4
- References
- 0
- FWCI
- 4.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Business Performance Management (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Business, Management and Accounting › Accounting › Corporate Insolvency and Governance
- Keywords
- default risk, bankruptcy prediction, key variables, feedforward neural networks, backpropagation, complex systems, business failure, credit risk, clustering, data reduction
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