Predicting Business Distress Using Neural Network in SME-Arab Region
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
Abdou, H. and Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature, Intelligent Systems in Accounting, Finance and Management, 18 (2-3), pp. 59-88.
Al Khatib and Al Bzour (2011). Predicting Corporate Bankruptcy of Jordanian Listed Companies: Using Altman and Kida Models. International Journal of Business and Management. Vol. 6, No. 3, P. 208 - 215
Altman (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, September, p. 589-609.
Altman and Sabato (2007). Modeling Credit Risk for SME: Evidence from the US Market". ABACUS 43, p.
Altman, Sabato, andWilson (2010). The value of non-financial information in small and medium sized enterprises risk management. The Journal of credit risk, p.1-33, Vol. 6.
Beresteanu, Ariel (2003). Nonparametric Estimation of Regression Functions under Restrictions on Partial Derivatives. Working Paper, Department of Economics, Duke University. Webpage: www.econ.duke.edu/˜arie/shape.pdf.
Cameron and TRIVEDI (2009). Micro-econometrics Using Stata, Texas, USA, Stata Corp LP.
Central Bank of UAE, IRB guideline (2012). Guidance for the waiver application of Internal Rating Based Approaches for Credit Risk (IRB)". V 1.0
Emine (2012). Financial Challenges That Impede Increasing the Productivity of SME in Arab Region. Journal of Contemporary Management, Academic Research Centre of Canada, P. 17-32. Article ID: 1929-0128-2012-02-17-16.
Gulf news, 17th November 2015.
Gumparthi, Khatri and Manickavasagam (2011). Design and development of credit rating model for public sector banks in India: Special reference to small and medium enterprises. Journal of Accounting and Taxation, Vol. 3(5), P. 105-124,
Hudson (1987). The age, regional and industrial structure of company liquidations, Journal of Business Finance and Accounting, Vol. 14, P. 199–213.
Hashim, and Adbullah (2002). A proposed framework for redefining SME in Malaysia: One Industry one definition. Asian Academy of Management Journal, USM.my P.65-79.
Huei Lin (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, Vol. 72, P. 3507–3516
M. Adnan Aziz, Humayon A. Dar (2004). Predicting Corporate Financial Distress: Where do We Stand? Department of Economics, Loughborough University, UK, p. 1- 40
????, ???? ??? “????????? ? ???????? ??????? ?? ??????? ?????? ? ?????????" ?????? ? ?????? ???? ???? ????? ? ???????(Translation) Matar, Mohamed (2003), Modern Directions and Styles in Financial and Credit Analysis. Amman: Dar Wa'el. 1st Edition
Olutunla and Obamuyi (2008). An empirical analysis of factors associated with the Profitability of Small and medium - enterprises in Nigeria. African Journal of Business Management Vol.2 (x), pp. 195-200.
Ono (2006). The Role of Credit Scoring in Small Business Lending. Mizuho Research Institute, Tokyo, Japan.
Rocha, Farazai, Khouri, and Pearce (2011). The Status of Bank Lending to SME's in the Middle East and North Africa Region. The Union of Arab Banks and the World Bank
Rustichini, Aldo, John Dickhaut, Paolo Ghirardato, Kip Smith, and Jose V. Pardo (2002). A Brain Imaging Study of Procedural Choice. Working Paper, Department of Economics, University of Minnesota. Webpage: http://www.econ.umn.edu/˜arust/ProcCh3.pdf.
Rosli (2011). Determinants of small and medium enterprises performance in the Malaysian autoparts industry. African Journal of Business Management. Vol. 5, pp. 8235-8241,
Shumway (2001). Forecasting Bankruptcy more accurately: A Simple Hazard Rate Model. Journal of Business, Vol.74, pp. 101–124.
Yu Cao (2011). A survival analysis of small and medium enterprises (SMEs) in central China and their determinants. African Journal of Business Management. Vol. 6, P. 3834-3850