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

Author

Listed:
  • Marco Corazza

    (Dept. of Economics, Università Ca' Foscari Venice)

  • Giovanni Fasano

    (Dept. of Management, Università Ca' Foscari Venice)

  • Stefania Funari

    (Dept. of Management, Università Ca' Foscari Venice)

  • Riccardo Gusso

    (Dept. of Management, Università Ca' Foscari Venice)

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.

Suggested Citation

  • Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2017. "PSO-based tuning of MURAME parameters for creditworthiness evaluation of Italian SMEs," Working Papers 04, Department of Management, Università Ca' Foscari Venezia.
    • Handle: RePEc:vnm:wpdman:137
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    References listed on IDEAS

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    More about this item

    Keywords

    MultiCriteria Decision Analysis; Small and Medium-sized Enterprises; Credit Risk; Particle Swarm Optimization.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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