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Modeling hard and soft facts for SMEs: Some international evidence
Author(s) -
Matthias Massimo,
Giammarino Michele,
Gabbi Giampaolo
Publication year - 2019
Publication title -
journal of international financial management and accounting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.818
H-Index - 37
eISSN - 1467-646X
pISSN - 0954-1314
DOI - 10.1111/jifm.12108
Subject(s) - balanced scorecard , order (exchange) , sample (material) , estimation , variable (mathematics) , business , financial crisis , qualitative property , economics , finance , marketing , computer science , macroeconomics , management , chromatography , machine learning , mathematical analysis , chemistry , mathematics
This paper asks how well the use of quantitative and qualitative variables can improve the assessment of companies' creditworthiness and how this result can be influenced by the economic and financial peculiarities of countries. We harden qualitative variable measures to model soft information aimed at scoring microfirms, small, and medium‐sized firms. The structural survey covers Germany, Italy, and the UK in a sample of about 17 thousand companies observed during the financial crisis. Soft facts are determined within the balanced scorecard framework in order to find out the impact of customers, business processes, learning and growth, and financial perspectives. Our findings show that credit models integrating soft variables optimize the risk estimation, but estimates are country‐specific and should be tailored to the characteristics of each economic system.