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Original Articles

Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises

Pages 23-45 | Published online: 19 Nov 2019

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (11)

Edward I. Altman, Marco Balzano, Alessandro Giannozzi & Stjepan Srhoj. (2023) Revisiting SME default predictors: The Omega Score. Journal of Small Business Management 61:6, pages 2383-2417.
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Constantin Siggelkow & Roberto Marquez Fernandez. (2023) SME default prediction using random forest including nonfinancial features: An empiricial analysis of German enterprises. Journal of the International Council for Small Business 0:0, pages 1-19.
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Christophe Schalck & Meryem Yankol-Schalck. (2021) Predicting French SME failures: new evidence from machine learning techniques. Applied Economics 53:51, pages 5948-5963.
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Zhehao Jia, Yukun Shi, Cheng Yan & Meryem Duygun. (2020) Bankruptcy prediction with financial systemic risk. The European Journal of Finance 26:7-8, pages 666-690.
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Nana Chai, Bi Wu, Weiwei Yang & Baofeng Shi. (2019) A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China. Emerging Markets Finance and Trade 55:11, pages 2523-2543.
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Salim Lahmiri & Stelios Bekiros. (2019) Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design. Quantitative Finance 19:9, pages 1569-1577.
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Simon Cornée. (2019) The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank. Journal of Small Business Management 57:3, pages 699-719.
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Manuel D. N. T. Oliveira, Fernando A. F. Ferreira, Guillermo O. Pérez-Bustamante Ilander & Marjan S. Jalali. (2017) Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises. Journal of the Operational Research Society 68:9, pages 985-997.
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Raffaella Calabrese, Giampiero Marra & Silvia Angela Osmetti. (2016) Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model. Journal of the Operational Research Society 67:4, pages 604-615.
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M. Modina & F. Pietrovito. (2014) A default prediction model for Italian SMEs: the relevance of the capital structure. Applied Financial Economics 24:23, pages 1537-1554.
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Carmen Gallucci, Rosalia Santullli, Michele Modina & Vincenzo Formisano. (2022) Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default. Journal of Management and Governance 27:3, pages 873-892.
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Mohammad Zoynul Abedin, Chi Guotai, Petr Hajek & Tong Zhang. (2022) Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex & Intelligent Systems 9:4, pages 3559-3579.
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He-Boong Kwon, Jooh Lee & Laee Choi. (2021) Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach. Annals of Operations Research 324:1-2, pages 687-719.
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Khaled Halteh & Milind Tiwari. (2023) Preempting fraud: a financial distress prediction perspective on combating financial crime. Journal of Money Laundering Control.
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Jooh Lee & He-Boong Kwon. (2021) Synergistic effect of R&D and exports on performance in US manufacturing industries: high-tech vs low-tech. Journal of Modelling in Management 18:2, pages 343-371.
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Milagros Vivel-Búa & Rubén Lado-Sestayo. (2021) Contagion Effect on Business Failure: A Spatial Analysis of the Hotel Sector. Journal of Hospitality & Tourism Research 47:3, pages 482-502.
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Baofeng Shi. 2023. Novel Financial Applications of Machine Learning and Deep Learning. Novel Financial Applications of Machine Learning and Deep Learning 125 149 .
Ratangar Djimnadjingar. (2022) The failure of family SMEs in Chad. Recherches en Sciences de Gestion N° 152:5, pages 31-54.
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Frank Ranganai Matenda & Mabutho Sibanda. (2022) Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe. Economies 10:11, pages 274.
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Mohamed El-Sayed Mousa & Mahmoud Abdelrahman Kamel. (2021) An integrated framework for predicting the best financial performance of banks: evidence from Egypt. Journal of Modelling in Management 17:3, pages 964-986.
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Desheng Wu, Xiyuan Ma & David L. Olson. (2022) Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decision Support Systems 159, pages 113814.
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David Veganzones. (2022) Corporate failure prediction using threshold‐based models. Journal of Forecasting 41:5, pages 956-979.
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Svatopluk Kapounek, Jan Hanousek & František Bílý. (2022) Predictive Ability of Altman Z-score of European Private Companies. Politická ekonomie 70:3, pages 265-287.
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Marco Repetto. (2022) Multicriteria interpretability driven deep learning. Annals of Operations Research.
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Anwer Mustafa Hilal, Hadeel Alsolai, Fahd N. Al-Wesabi, Mohammed Abdullah Al-Hagery, Manar Ahmed Hamza & Mesfer Al Duhayyim. (2022) Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science. Computers, Materials & Continua 70:3, pages 6289-6304.
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Francesco Corea, Giorgio Bertinetti & Enrico Maria Cervellati. (2021) Hacking the venture industry: An Early-stage Startups Investment framework for data-driven investors. Machine Learning with Applications 5, pages 100062.
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Mustafa Yıldırım, Feyza Yıldırım Okay & Suat Özdemir. (2021) Big data analytics for default prediction using graph theory. Expert Systems with Applications 176, pages 114840.
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Lisa Crosato, Josep Domenech & Caterina Liberati. (2021) Predicting SME’s default: Are their websites informative?. Economics Letters 204, pages 109888.
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GALINA ANDREEVA & EDWARD I. ALTMAN. (2021) THE VALUE OF PERSONAL CREDIT HISTORY IN RISK SCREENING OF ENTREPRENEURS: EVIDENCE FROM MARKETPLACE LENDING. Journal of Financial Management, Markets and Institutions 09:01, pages 2150004.
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Mehran Amini, Sara Salimi, Farid Yousefinejad, Mohammad J. Tarokh & Sayyed M. Haybatollahi. (2021) The implication of business intelligence in risk management: a case study in agricultural insurance. Journal of Data, Information and Management 3:2, pages 155-166.
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Dennis Bams, Magdalena Pisa & Christian C. P. Wolff. (2020) Spillovers to small business credit risk. Small Business Economics 57:1, pages 323-352.
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Francesco Ciampi, Alessandro Giannozzi, Giacomo Marzi & Edward I. Altman. (2021) Rethinking SME default prediction: a systematic literature review and future perspectives. Scientometrics 126:3, pages 2141-2188.
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Yuan Sun, Weifeng Jian, Yufeng Fu, Huiping Sun, Yuesheng Zhu & Zhiqiang Bai. (2021) A new perspective of credit scoring for small and medium-sized enterprises based on invoice data. A new perspective of credit scoring for small and medium-sized enterprises based on invoice data.
Gang Kou, Yong Xu, Yi Peng, Feng Shen, Yang Chen, Kun Chang & Shaomin Kou. (2021) Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems 140, pages 113429.
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Andro Merćep, Lovre Mrčela, Matija Birov & Zvonko Kostanjčar. (2020) Deep Neural Networks for Behavioral Credit Rating. Entropy 23:1, pages 27.
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Francesco Ciampi, Valentina Cillo & Fabio Fiano. (2018) Combining Kohonen maps and prior payment behavior for small enterprise default prediction. Small Business Economics 54:4, pages 1007-1039.
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Ao Yu, Zhuoqiang Jia, Weike Zhang, Ke Deng & Francisco Herrera. (2020) A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods. Sustainability 12:5, pages 1715.
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Jooh Lee, He-Boong Kwon & Niranjan Pati. (2019) Exploring the relative impact of R&D and operational efficiency on performance: A sequential regression-neural network approach. Expert Systems with Applications 137, pages 420-431.
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Rasa Kanapickiene & Renatas Spicas. (2019) Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania. Risks 7:2, pages 67.
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Timothy Wang. (2018) Predicting Private Company Failures in Italy Using Financial and Non‐financial Information. Australian Accounting Review 29:1, pages 143-157.
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Valentina Cillo, Riccardo Rialti, Bernardo Bertoldi & Francesco Ciampi. (2019) Knowledge management and open innovation in agri-food crowdfunding. British Food Journal 121:2, pages 242-258.
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Raffaella Calabrese, Galina Andreeva & Jake Ansell. (2019) “Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults. Risk Analysis 39:1, pages 71-84.
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Mohammad Zoynul Abedin, Chi Guotai, Fahmida-E- Moula, A.S.M. Sohel Azad & Mohammed Shamim Uddin Khan. (2019) Topological applications of multilayer perceptrons and support vector machines in financial decision support systems. International Journal of Finance & Economics 24:1, pages 474-507.
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Linda Gabbianelli. (2018) A territorial perspective of SME’s default prediction models. Studies in Economics and Finance 35:4, pages 542-563.
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Khaled Halteh, Kuldeep Kumar & Adrian Gepp. (2018) Financial distress prediction of Islamic banks using tree-based stochastic techniques. Managerial Finance 44:6, pages 759-773.
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Samir D. Baidoun, Robert N. Lussier, Maisa Burbar & Sawsan Awashra. (2018) Prediction model of business success or failure for Palestinian small enterprises in the West Bank. Journal of Entrepreneurship in Emerging Economies 10:1, pages 60-80.
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Anna Chiara Invernizzi, Anna Menozzi, Diana Anna Passarani, Dean Patton & Giampaolo Viglia. (2016) Entrepreneurial overconfidence and its impact upon performance. International Small Business Journal: Researching Entrepreneurship 35:6, pages 709-728.
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Jooh Lee & He-Boong Kwon. (2017) Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs. International Journal of Production Economics 183, pages 91-102.
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Fabio Sartori, Alice Mazzucchelli & Angelo Di Gregorio. (2016) Bankruptcy forecasting using case-based reasoning: The CRePERIE approach. Expert Systems with Applications 64, pages 400-411.
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Galina Andreeva, Raffaella Calabrese & Silvia Angela Osmetti. (2016) A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models. European Journal of Operational Research 249:2, pages 506-516.
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