Predicting Business Distress Using Neural Network in SME-Arab Region
Keywords:Business distress, Arab region, Petrochemical sub-sectors, Logit Model, Neural Network, SME
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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