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The financing of innovative SMEs: A multicriteria credit rating model

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  • Angilella, Silvia
  • Mazzù, Sebastiano

Abstract

Small and Medium-sized Enterprises (SMEs) face many obstacles when they try to access the credit market. These obstacles increase if the SMEs are innovative. In this case, financial data are insufficient or even unreliable. Thus, building a judgmental rating model, mainly based on qualitative criteria (soft information), is very important to finance SMEs’ activities. Till now, there has not been a multicriteria credit risk model based on soft information for innovative SMEs. In this paper, we try to fill this gap by presenting a multicriteria credit risk model named ELECTRE-TRI. A SMAA-TRI analysis is also implemented to obtain robust SMEs’ assignments to the risk classes. SMAA-TRI incorporates ELECTRE-TRI by considering different sets of preference parameters and uncertainty in the data via Monte Carlo simulations. Finally, we carry out a real case study with the aim of illustrating the multicriteria credit risk model.

Suggested Citation

  • Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
  • Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:540-554
    DOI: 10.1016/j.ejor.2015.01.033
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    1. A. Seetharaman & Vikas Kumar Sahu & A. S. Saravanan & John Rudolph Raj & Indu Niranjan, 2017. "The Impact of Risk Management in Credit Rating Agencies," Risks, MDPI, Open Access Journal, vol. 5(4), pages 1-16, September.
    2. Liu, Zhengchi & Shang, Jennifer & Wu, Shin-yi & Chen, Pei-yu, 2020. "Social collateral, soft information and online peer-to-peer lending: A theoretical model," European Journal of Operational Research, Elsevier, vol. 281(2), pages 428-438.
    3. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    4. Corazza, Marco & Funari, Stefania & Gusso, Riccardo, 2016. "Creditworthiness evaluation of Italian SMEs at the beginning of the 2007–2008 crisis: An MCDA approach," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 1-26.
    5. Liu, Jiapeng & Liao, Xiuwu & Huang, Wei & Yang, Jian-bo, 2018. "A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples," European Journal of Operational Research, Elsevier, vol. 265(2), pages 598-620.
    6. 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.
    7. Magni, Carlo Alberto & Malagoli, Stefano & Marchioni, Andrea & Mastroleo, Giovanni, 2019. "Rating firms and sensitivity analysis," MPRA Paper 95265, University Library of Munich, Germany.
    8. Marcello Pagnini & Paola Rossi & Valerio Vacca & Lucia dalla Pellegrina & Serena Frazzoni & Zeno Rotondi & Andrea Vezzulli, 2017. "Access to Credit for Small Innovative Businesses," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 46(3), pages 411-458, November.
    9. Faiza Sajjad & Muhammad Zakaria, 2018. "Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 6(1), pages 1-14, January.
    10. Lagravinese, Raffaele & Liberati, Paolo & Resce, Giuliano, 2019. "Exploring health outcomes by stochastic multicriteria acceptability analysis: An application to Italian regions," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1168-1179.
    11. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.

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