This paper introduces a model for rating a firm’s default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes. The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm’s default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm’s rating.

Rating firms and sensitivity analysis / Magni, C. A.; Malagoli, S.; Marchioni, A.; Mastroleo, G.. - In: JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY. - ISSN 0160-5682. - 71:12(2020), pp. 1940-1958. [10.1080/01605682.2019.1650626]

Rating firms and sensitivity analysis

Magni C. A.;Marchioni A.;Mastroleo G.
2020

Abstract

This paper introduces a model for rating a firm’s default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes. The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm’s default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm’s rating.
71
12
1940
1958
Rating firms and sensitivity analysis / Magni, C. A.; Malagoli, S.; Marchioni, A.; Mastroleo, G.. - In: JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY. - ISSN 0160-5682. - 71:12(2020), pp. 1940-1958. [10.1080/01605682.2019.1650626]
Magni, C. A.; Malagoli, S.; Marchioni, A.; Mastroleo, G.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1264498
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