Recent literature shows an increasing interest in considering alternative sources of information for predicting Small and Medium Enterprises (SMEs) default. Generally, papers in the field use accounting indicators as regressors of bankruptcy prediction models, although it has been demonstrated they do not allow to completely overcome the information opacity that is one of the main barriers preventing SMEs from accessing to credit. Plus, companies balance sheet data are published at least one year late with respect to the reference period, so that they prevent a real time prediction as requested by financial operators. In this paper we propose websites as an additional source of information for forecasting SMEs default. We borrowed this idea from contributions the have used corporate websites to retrieve online proxies of firms’ economic characteristics, such as corporate culture or firm performance. Our work explores the joint use of online and offline data for enhancing correct prediction of default through kernel discriminant analysis. We also study in detail the firms where the combination is successful in order to highlight possible patterns for future applications.

Crosato, L., Domènech, J., Liberati, C. (2022). Websites’ Data: a New Asset for Enhancing Credit Risk Modeling. Intervento presentato a: Statistics and Data Science in Business and Industry (ISBIS 2022), Napoli.

Websites’ Data: a New Asset for Enhancing Credit Risk Modeling

Caterina Liberati
Ultimo
Membro del Collaboration Group
2022

Abstract

Recent literature shows an increasing interest in considering alternative sources of information for predicting Small and Medium Enterprises (SMEs) default. Generally, papers in the field use accounting indicators as regressors of bankruptcy prediction models, although it has been demonstrated they do not allow to completely overcome the information opacity that is one of the main barriers preventing SMEs from accessing to credit. Plus, companies balance sheet data are published at least one year late with respect to the reference period, so that they prevent a real time prediction as requested by financial operators. In this paper we propose websites as an additional source of information for forecasting SMEs default. We borrowed this idea from contributions the have used corporate websites to retrieve online proxies of firms’ economic characteristics, such as corporate culture or firm performance. Our work explores the joint use of online and offline data for enhancing correct prediction of default through kernel discriminant analysis. We also study in detail the firms where the combination is successful in order to highlight possible patterns for future applications.
Si
relazione (orale)
SMEs, Websites Data, Kernel Discriminant, Default Prediction
English
Statistics and Data Science in Business and Industry (ISBIS 2022)
Crosato, L., Domènech, J., Liberati, C. (2022). Websites’ Data: a New Asset for Enhancing Credit Risk Modeling. Intervento presentato a: Statistics and Data Science in Business and Industry (ISBIS 2022), Napoli.
Crosato, L; Domènech, J; Liberati, C
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/386984
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact