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PSO-based tuning of MURAME parameters for creditworthiness evaluation of Italian SMEs

Marco Corazza (), Giovanni Fasano (), Stefania Funari () and Riccardo Gusso ()
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Marco Corazza: Dept. of Economics, Università Ca' Foscari Venice
Stefania Funari: Dept. of Management, Università Ca' Foscari Venice

No 4, Working Papers from Department of Management, Università Ca' Foscari Venezia

Abstract: In this work we use a MultiCriteria Decision Analysis (MCDA) model to evalu- ate the creditworthiness of a sample of Italian Small and Medium-sized Enterprises (SMEs), on the basis of their balance sheet data provided by the AIDA database. Our methodology is able to consider simultaneously different factors affecting the firmsÕ solvency level, and can produce results in terms of scoring, classification into homogeneous rating classes and migration probabilities. In this contribution we compare the results obtained considering two scenarios. On one hand, we experience an exogenous specification of the parameters that describe the preference structure implicit in the used MCDA model. On the other hand, we consider the results obtained using a preference disaggregation method to endogenously determine some of the model parameters. Because of the complexity of the obtained math- ematical programming problem, we use an heuristic methodology, namely Particle Swarm Optimization (PSO), which provides a reasonable compromise between the quality of the solution and the computational burden.

Keywords: MultiCriteria Decision Analysis; Small and Medium-sized Enterprises; Credit Risk; Particle Swarm Optimization. (search for similar items in EconPapers)
JEL-codes: C38 C61 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-ore
Date: 2017-04
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