Prediction of SMEs Bankruptcy at the Industry Level with Balance Sheets and Website Indicators
[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 bal...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/208407 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/208407 |
| Access Level: | acceso abierto |
| Palabra clave: | Website data Html code SMEs Supervised learning |
| Sumario: | [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. |
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