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CART analysis of qualitative variables to improve credit rating processes

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  • Giampaolo Gabbi

    ()
    (Banking and Finance Dpt Sda Bocconi Milan - Siena University)

  • Massimo Matthias

    (University of Siena, Italy)

  • Marco De Lerma

    (University of Siena, Italy)

Abstract

Qualitative 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; banks’ 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 Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 179.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:179

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Keywords: credit risk; qualitative models; rating; regression tree;

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