What is it about?
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).
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Why is it important?
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.
Perspectives
The study opens up potential opportunities for the lending firms to adopt advanced analytical frameworks for predicting distress behavior of business firms.
Dr Ananth Rao
University of Dubai
Read the Original
This page is a summary of: Predicting Business Distress Using Neural Network in SME-Arab Region, International Review of Advances in Business Management and Law, April 2018, University of Dubai,
DOI: 10.30585/irabml.v1i1.68.
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