CART analysis of qualitative variables to improve credit rating processes
AbstractQualitative behaviours of small firms are explored to forecast criticalities in the Italian credit market. We build up an evaluation process to integrate quantitative rating practices. Research method: Relevant qualitative factors to estimate the credit risk are empirically investigated by choosing variables related to seven perspectives of analysis (sector, governance, internal processes, learning and growth, customers, economic-financial analysis, quality of balance sheet). Outcomes have been elaborated through the Classification And Regression Tree. Rating evaluation is based upon qualitative factors. Findings: The capability to forecast bad firms is 92.4\%, while 84.5\% is the percentage to forecast good firms. The Cumulative Accuracy Curve shows the 84\% ability to explain the variance of phenomenon. Main results: ratings of firms are primary explained by aptitude to reach short and long term purposes; banksâ€™ analysts should integrate their quantitative models with qualitative data; methodologies employed offer a quantitative solution to estimate the weight of each variable. Conclusions: When balance sheets are characterized by small consistency, qualitative variables should be taken into consideration to elaborate or integrate rating procedures.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 179.
Date of creation: 04 Jul 2006
Date of revision:
credit risk; qualitative models; rating; regression tree;
Find related papers by JEL classification:
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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