Abstract
It is important to be able to predict the performance of a company in terms of bankruptcy, however, to date, there is no robust automated model capable of predicting corporate bankruptcy regardless of the sector to which a company belongs. This study has estimated the expected probability of bankruptcy of companies in 3 different sectors (the manufacturing sector for which Z-score had been initially developed as well as wholesale and finance; two of the most important sectors in terms of contribution to the Spain’s GDP). The proposed model has a success rate of over 90% in predicting bankruptcy and analyses fewer company’s attributes than the Altman Z-score model.
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Notes
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SABI database (https://sabi.bvdinfo.com/): Sabi contains detailed information about companies in Spain and Portugal. It is public and can be used to carry out research on companies.
- 2.
CNAE codes: https://www.cnae.com.es/.
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Acknowledgements
This research has been supported by the project “INTELFIN: Artificial Intelligence for investment and value creation in SMEs through competitive analysis and business environment”, Reference: RTC-2017-6536-7, funded by the Ministry of Science, Innovation and Universities (Challenges-Collaboration 2017), the State Agency for Research (AEI) and the European Regional Development Fund (ERDF).
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Pérez-Pons, M.E., Parra, J., Hernández, G., González, J., Corchado, J.M. (2021). Machine Learning and Financial Ratios as an Alternative to Altman’s Z-Score Bankruptcy Model in Spanish Companies. In: Bucciarelli, E., Chen, SH., Corchado, J.M., Parra D., J. (eds) Decision Economics: Minds, Machines, and their Society. DECON 2020. Studies in Computational Intelligence, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-030-75583-6_13
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