Predicting Business Distress Using Neural Network in SME-Arab Region

  • Malik AL KHATIB Euler Hermes, A company of Allianz - Dubai - United Arab Emirates
  • Worku B GENANEW Economics and Statistics, College of Business Administration, University of Dubai
  • Ananth Rao, Prof University of Dubai - Dubai Business School https://orcid.org/0000-0002-8110-5423
Keywords: Business distress, Arab region, Petrochemical sub-sectors, Logit Model, Neural Network, SME

Abstract

The paper analyzes the financial and operational measures for Small and medium-sized enterprises (SME) business distress for predicting credit worthiness by using panel data of 110 observations from 22 SME companies for a period of 5 years (2009 – 2013). Panel logistic and Neural Network (NN) models are developed as alternative techniques for predicting the business distress.  The result suggests that cash cycle, net fixed assets, and leverage ratio are key factors in making credit decisions by lenders. The logistic model overall correctly classified 70 percent while NN framework outperformed the logistic model with 93 percent overall correct classification in training phase, and 83 percent in testing phase. The study opens up potential opportunities for the lending firms to adopt advanced analytical frameworks for predicting distress behavior of business firms.

Keywords: SME, Business distress, Arab region, Petrochemical sub-sectors, Logit Model, Neural Network.  

JEL codes: G29, G32

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Author Biographies

Malik AL KHATIB, Euler Hermes, A company of Allianz - Dubai - United Arab Emirates

Credit Risk Manager of GCC Countries, Risk Department, Euler Hermes, A company of Allianz, P.O. Box : 183957, Dubai, United Arab Emirates, Phone: +971 4 211 6017, Fax: + 971 4 211 6060, Mobile: +971 50 455 355 8, E-mail: alkhateeb.mlk@gmail.com

Worku B GENANEW, Economics and Statistics, College of Business Administration, University of Dubai

Economics and Statistics, College of Business Administration, University of Dubai; P.O.Box 14143, Dubai, United Arab Emirates;

Phone: +971-4-2072623, Fax: +97142242670.

E-mail: gbekele@ud.ac.ae

Ananth Rao, Prof, University of Dubai - Dubai Business School

Corresponding Author
Director (MBA and PhD), Academic Advisor to President, University of Dubai; P.O.Box 14143, Dubai, United Arab Emirates;

Phone: +971-4-2072623, Fax+971 4 2242670.

Email: arao@ud.ac.ae

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Published
2018-04-15
How to Cite
AL KHATIB, M., GENANEW, W., & Rao, A. (2018). Predicting Business Distress Using Neural Network in SME-Arab Region. International Review of Advances in Business, Management and Law, 1(1), 68-84. https://doi.org/10.30585/irabml.v1i1.68