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dc.contributor.author | Bottai, Carlo | es_ES |
dc.contributor.author | Crosato, Lisa | es_ES |
dc.contributor.author | Liberati, Caterina | es_ES |
dc.date.accessioned | 2024-09-20T10:38:23Z | |
dc.date.available | 2024-09-20T10:38:23Z | |
dc.date.issued | 2024-07-16 | |
dc.identifier.isbn | 9788413962016 | |
dc.identifier.uri | http://hdl.handle.net/10251/208407 | |
dc.description.abstract | [EN] This paper addresses the importance of industry-specific models for SMEs bankruptcy prediction, building on earlier research finding larger predictive accuracy and enhanced temporal stability. Using Italian data, we propose separate bankruptcy prediction models for a few industries based on balance sheet data and explore the predictive power of SMEs' website html code structure. Our findings suggest that website data can serve as a valid complementary source for bankruptcy prediction, with different performances across sectors. We observe a certain degree of sectoral heterogeneity in the importance of financial ratios, firm-specific characteristics, and website structure, calling for an industry-tailored approach in bankruptcy prediction models. | es_ES |
dc.format.extent | 7 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Editorial Universitat Politècnica de València | es_ES |
dc.relation.ispartof | 6th International Conference on Advanced Research Methods and Analytics (CARMA 2024) | |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Website data | es_ES |
dc.subject | Html code | es_ES |
dc.subject | SMEs | es_ES |
dc.subject | Supervised learning | es_ES |
dc.title | Prediction of SMEs Bankruptcy at the Industry Level with Balance Sheets and Website Indicators | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.4995/CARMA2024.2024.17761 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Bottai, C.; Crosato, L.; Liberati, C. (2024). Prediction of SMEs Bankruptcy at the Industry Level with Balance Sheets and Website Indicators. Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2024.2024.17761 | es_ES |
dc.description.accrualMethod | OCS | es_ES |
dc.relation.conferencename | CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics | es_ES |
dc.relation.conferencedate | Junio 26-28, 2024 | es_ES |
dc.relation.publisherversion | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2024/paper/view/17761 | es_ES |
dc.description.upvformatpinicio | 235 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | OCS\17761 | es_ES |