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Financial distress model prediction using SVM+
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| datacite.subject.sdg | 07:Energias Renováveis e Acessíveis | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| dc.contributor.author | Ribeiro, Bernardete | |
| dc.contributor.author | Silva, Catarina | |
| dc.contributor.author | Vieira, Armando | |
| dc.contributor.author | Gaspar-Cunha, A. | |
| dc.contributor.author | Neves, João C. das | |
| dc.date.accessioned | 2025-11-20T10:53:33Z | |
| dc.date.available | 2025-11-20T10:53:33Z | |
| dc.date.issued | 2010-07 | |
| dc.description | Conference name - 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010; Conference date - 18 July 2010 - 23 July 2010; Conference code - 85188 | |
| dc.description.abstract | Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost. | eng |
| dc.description.sponsorship | This work was partially supported by “Fundação da Ciência e Tecnologia’ under grant no. PTDC/GES/70168/2006. | |
| dc.identifier.citation | B. Ribeiro, C. Silva, A. Vieira, A. Gaspar-Cunha and J. C. das Neves, "Financial distress model prediction using SVM+," The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 2010, pp. 1-7, doi: https://doi.org/10.1109/IJCNN.2010.5596729. | |
| dc.identifier.doi | 10.1109/ijcnn.2010.5596729 | |
| dc.identifier.isbn | 978-142446917-8 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/14685 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE Canada | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/document/5596729 | |
| dc.relation.ispartof | The 2010 International Joint Conference on Neural Networks (IJCNN) | |
| dc.rights.uri | N/A | |
| dc.subject | Support vector machines | |
| dc.subject | Companies | |
| dc.subject | Kernel | |
| dc.subject | Predictive models | |
| dc.subject | Accuracy | |
| dc.subject | Data models | |
| dc.subject | Measurement | |
| dc.title | Financial distress model prediction using SVM+ | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2010-07 | |
| oaire.citation.conferencePlace | Barcelona, Spain | |
| oaire.citation.title | Proceedings of the International Joint Conference on Neural Networks | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Silva | |
| person.givenName | Catarina | |
| person.identifier.ciencia-id | 1B19-3DDC-BE75 | |
| person.identifier.orcid | 0000-0002-5656-0061 | |
| relation.isAuthorOfPublication | ee28e079-5ca7-4842-9094-372c40f75c38 | |
| relation.isAuthorOfPublication.latestForDiscovery | ee28e079-5ca7-4842-9094-372c40f75c38 |
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- Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost.
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