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dc.contributor.author | Crosato, Lisa | es_ES |
dc.contributor.author | Domenech, Josep | es_ES |
dc.contributor.author | Liberati, Caterina | es_ES |
dc.date.accessioned | 2022-11-10T08:03:03Z | |
dc.date.available | 2022-11-10T08:03:03Z | |
dc.date.issued | 2022-09-20 | |
dc.identifier.isbn | 9788413960180 | |
dc.identifier.uri | http://hdl.handle.net/10251/189543 | |
dc.description.abstract | [EN] Small and Medium Enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. That is why the prediction of their survival is considered one of the economic pillars UE keeps under observation. Default prediction models, accounting for SMEs idiosyncratic traits, are based on several types of data, mainly accounting indicators. Balance sheet data, indeed, are considered the standard predictors for classification models in this field, although they do not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing credit. In our work, we explore the possibility of complementing accounting information with data scraped from the firms’ websites. We modeled the data using a nonlinear discriminant analysis and we benchmarked the results with the Logistic Regression. The evidence of our study is promising although the combination of online and offline data shows better results in case of survival firms than for defaulted companies. | es_ES |
dc.description.sponsorship | This work was partially supported by grants PID2019-107765RB-I00 and funded by MCIN/AEI/10.13039/501100011033. | es_ES |
dc.format.extent | 6 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Editorial Universitat Politècnica de València | es_ES |
dc.relation.ispartof | 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022) | |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Website Data | es_ES |
dc.subject | SMEs | es_ES |
dc.subject | Default Prediction | es_ES |
dc.subject | Kernel Discriminant | es_ES |
dc.title | Non-conventional data and default prediction: the challenge of companies’ websites | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.4995/CARMA2022.2022.15103 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107765RB-I00/ES/HUELLA DIGITAL, COMPETITIVIDAD Y DEMOGRAFIA EMPRESARIAL/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Facultad de Administración y Dirección de Empresas - Facultat d'Administració i Direcció d'Empreses | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials | es_ES |
dc.description.bibliographicCitation | Crosato, L.; Domenech, J.; Liberati, C. (2022). Non-conventional data and default prediction: the challenge of companies’ websites. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 253-258. https://doi.org/10.4995/CARMA2022.2022.15103 | es_ES |
dc.description.accrualMethod | OCS | es_ES |
dc.relation.conferencename | CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics | es_ES |
dc.relation.conferencedate | Junio 29-Julio 01, 2022 | es_ES |
dc.relation.conferenceplace | Valencia, España | |
dc.relation.publisherversion | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2022/paper/view/15103 | es_ES |
dc.description.upvformatpinicio | 253 | es_ES |
dc.description.upvformatpfin | 258 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | OCS\15103 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |