The objective of a Credit Risk model is to develop an accurate rule that can distinguish between good and bad instances. In this work instances are firms, specif- ically we focus on Small and Medium Enterprises (SMEs), as most of the papers in the financial literature, thanks to their contribution to the European Union economy for both value added and the creation of jobs. The aim of the paper is twofold: to explore the usage of the web-scraped indicators to improve firms’ default predic- tions and to compare the models’ performances fed with the web-based indicators with the ones built with the standard financial information. The results, obtained on a sample of Spanish companies, will also include a focus about the ability of the web features in recognizing uncommon defaulted firms.

L’obiettivo di un modello di rischio di credito e` quello di sviluppare una regola accurata in grado di distinguere tra osservazioni buone e cattive. In questo lavoro le aziende sono le nostre osservazioni, in particolare ci concentriamo sulle Piccole e Medie Imprese (PMI), come la maggior parte dei lavori nella letteratura finanziaria, dato il loro contributo all’economia dell’Unione Europea sia per il val- ore aggiunto sia per la creazione di posti di lavoro. Lo scopo dell’articolo e` duplice: esplorare l’uso degli indicatori ottenuti dal web per migliorare le previsioni di in- solvenza delle imprese e confrontare le prestazioni dei modelli alimentati con tali indicatori rispetto a quelli costruiti con le informazioni finanziarie standard. I risul- tati, ottenuti su un campione di aziende spagnole, includeranno anche un focus sulla capacita` degli indicatori web di riconoscere le aziende insolventi non comuni.

Crosato, L., Domenech, J., Liberati, C. (2023). Enhancing SMEs default prediction with web-scraped data.. In IES 2023 - Statistical Methods for Evaluation and Quality: Techniques, Technologies and Trends (T3) BOOK OF SHORT PAPERS (pp.133-136). IlViandante [10.60984/978-88-94593-36-5-IES2023].

Enhancing SMEs default prediction with web-scraped data.

Liberati, C
2023

Abstract

The objective of a Credit Risk model is to develop an accurate rule that can distinguish between good and bad instances. In this work instances are firms, specif- ically we focus on Small and Medium Enterprises (SMEs), as most of the papers in the financial literature, thanks to their contribution to the European Union economy for both value added and the creation of jobs. The aim of the paper is twofold: to explore the usage of the web-scraped indicators to improve firms’ default predic- tions and to compare the models’ performances fed with the web-based indicators with the ones built with the standard financial information. The results, obtained on a sample of Spanish companies, will also include a focus about the ability of the web features in recognizing uncommon defaulted firms.
paper
Credit Risk Modeling, Default Prediction, SMEs, Web-scraped Data, Balance sheets
English
11th International Conference IES 2023 Statistical Methods for Evaluation and Quality: Techniques, Technologies and Trends (T3)
2023
Bucci, A; Cartone, A; Evangelista, A; Marletta, A
IES 2023 - Statistical Methods for Evaluation and Quality: Techniques, Technologies and Trends (T3) BOOK OF SHORT PAPERS
9791280333698
2023
133
136
none
Crosato, L., Domenech, J., Liberati, C. (2023). Enhancing SMEs default prediction with web-scraped data.. In IES 2023 - Statistical Methods for Evaluation and Quality: Techniques, Technologies and Trends (T3) BOOK OF SHORT PAPERS (pp.133-136). IlViandante [10.60984/978-88-94593-36-5-IES2023].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/436538
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