MURAME parameter setting for creditworthiness evaluation: data-driven optimization

Marco Corazza (), Giovanni Fasano (), Stefania Funari and Riccardo Gusso

Decisions in Economics and Finance, 2021, vol. 44, issue 1, No 17, 295-339

Abstract: Abstract In this paper, we amend a multi-criteria methodology known as MURAME, to evaluate the creditworthiness of a large sample of Italian Small and Medium-sized Enterprises, using as input their balance sheet data. This methodology produces results in terms of scoring and of classification into homogeneous rating classes. A distinctive goal of this paper is to consider a preference disaggregation method to endogenously determine some parameters of MURAME, by solving a nonsmooth constrained optimization problem. Because of the complexity of the involved mathematical programming problem, for its solution we use an evolutionary metaheuristic, coupled with a specific efficient initialization. This is combined with an unconstrained reformulation of the problem, which provides a reasonable compromise between the quality of the solution and the computational burden. An extensive numerical experience is reported, comparing an exogenous choice of MURAME parameters with our approach.

Keywords: Multi-Criteria Decision Analysis (MCDA); MUlticriteria RAnking MEthod (MURAME); Small and Medium-sized Enterprises (SMEs); Creditworthiness evaluation; Preference disaggregation; Particle Swarm Optimization (PSO) (search for similar items in EconPapers)
JEL-codes: C61 C63 G29 G33 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10203-021-00322-1

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