
Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination
- Author
- Andrey Volkov (UGent) , Dries Benoit (UGent) and Dirk Van den Poel (UGent)
- Organization
- Abstract
- In this paper we make a contribution to the body literature that incorporates a dynamic view on bankruptcy into bankruptcy prediction modelling In addition to using financial ratios measured over multiple time periods, we introduce variables based on the Markov for discrimination (MFD) model. MFD variables are able to extract the sequential information from time-series of financial ratios and concentrate it in one score. Our results obtained from multiple samples of Belgian bankruptcy data show that using data collected from multiple time periods outperforms snap-shot data that contains financial ratios measured at one point in time. In addition, we demonstrate that inclusion of MFD variables in non-ensemble bankruptcy prediction models considered in the study can lead to better classification performance. The latter type of models, despite not achieving the top performance based on metric considered in our study, can still be used by practitioners who prefer simpler, more interpretable models. (C) 2017 Elsevier B.V. All rights reserved.
- Keywords
- FAILURE PREDICTION, SURVIVAL ANALYSIS, COMPANY FAILURE, CREDIT, MODELS, PERFORMANCE, SEQUENCES, FIRMS, RISK, Bankruptcy prediction, Markov chains, Markov for discrimination, Time, series classification
Downloads
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 364.06 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8541213
- MLA
- Volkov, Andrey, et al. “Incorporating Sequential Information in Bankruptcy Prediction with Predictors Based on Markov for Discrimination.” DECISION SUPPORT SYSTEMS, vol. 98, Elsevier Science Bv, 2017, pp. 59–68, doi:10.1016/j.dss.2017.04.008.
- APA
- Volkov, A., Benoit, D., & Van den Poel, D. (2017). Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination. DECISION SUPPORT SYSTEMS, 98, 59–68. https://doi.org/10.1016/j.dss.2017.04.008
- Chicago author-date
- Volkov, Andrey, Dries Benoit, and Dirk Van den Poel. 2017. “Incorporating Sequential Information in Bankruptcy Prediction with Predictors Based on Markov for Discrimination.” DECISION SUPPORT SYSTEMS 98: 59–68. https://doi.org/10.1016/j.dss.2017.04.008.
- Chicago author-date (all authors)
- Volkov, Andrey, Dries Benoit, and Dirk Van den Poel. 2017. “Incorporating Sequential Information in Bankruptcy Prediction with Predictors Based on Markov for Discrimination.” DECISION SUPPORT SYSTEMS 98: 59–68. doi:10.1016/j.dss.2017.04.008.
- Vancouver
- 1.Volkov A, Benoit D, Van den Poel D. Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination. DECISION SUPPORT SYSTEMS. 2017;98:59–68.
- IEEE
- [1]A. Volkov, D. Benoit, and D. Van den Poel, “Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination,” DECISION SUPPORT SYSTEMS, vol. 98, pp. 59–68, 2017.
@article{8541213, abstract = {{In this paper we make a contribution to the body literature that incorporates a dynamic view on bankruptcy into bankruptcy prediction modelling In addition to using financial ratios measured over multiple time periods, we introduce variables based on the Markov for discrimination (MFD) model. MFD variables are able to extract the sequential information from time-series of financial ratios and concentrate it in one score. Our results obtained from multiple samples of Belgian bankruptcy data show that using data collected from multiple time periods outperforms snap-shot data that contains financial ratios measured at one point in time. In addition, we demonstrate that inclusion of MFD variables in non-ensemble bankruptcy prediction models considered in the study can lead to better classification performance. The latter type of models, despite not achieving the top performance based on metric considered in our study, can still be used by practitioners who prefer simpler, more interpretable models. (C) 2017 Elsevier B.V. All rights reserved.}}, author = {{Volkov, Andrey and Benoit, Dries and Van den Poel, Dirk}}, issn = {{0167-9236}}, journal = {{DECISION SUPPORT SYSTEMS}}, keywords = {{FAILURE PREDICTION,SURVIVAL ANALYSIS,COMPANY FAILURE,CREDIT,MODELS,PERFORMANCE,SEQUENCES,FIRMS,RISK,Bankruptcy prediction,Markov chains,Markov for discrimination,Time,series classification}}, language = {{eng}}, pages = {{59--68}}, publisher = {{Elsevier Science Bv}}, title = {{Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination}}, url = {{http://dx.doi.org/10.1016/j.dss.2017.04.008}}, volume = {{98}}, year = {{2017}}, }
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: