A Statistical Prediction of Likely Distress in Nigeria Banking Sector Using a Neural Network Approach
One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.
[1] Adefila, J.J. (2002) “Assessing Financial Strenght to Determine
Bankruptcy Potential: A case study of Trade Bank Plc” Nigerian Journal
of Management Science Vol. 30 No. 2, pp. 24-35.
[2] Altman, E.I. (1968), “Financial Ratios, Discriminant Analysis and the
Prediction of Corporate Bankruptcy” Journal of Finance, Vol. 23 p. 589
– 609. [3] Atiya, A.F., (2001) “Bankruptcy Prediction for credit Risk using Neural
Networks: A Survey and New Results” IEEE Transactions on Neural
Networks, Vol. 12 No. 4, pp. 929 – 935.
[4] Beaver, William H. (1966) “Financial Ratios as Predictions of Failure”
Emperical Research in Accounting. Selected Studies, Supplement to
Journal of Accounting Research vol. 4, pp. 77-111.
[5] Boyacioglu, M.A., Kara Y and Baykan, O.K. (2009), “Predicting bank
financial failures using neural neworks, support vector machines and
multivariate statistical methods: A comparative anslysis in the sample of
Savings Deposit Insurance Fund (SDIF) transferred banks in Turkey”
www.citibank.co.in vol 36 issue 2 pp. 3355-3366. Acessed on May 31,
2010.
[6] CBN, (2007) “A case study of Distressed Banks in Nigeria” Publication
of Research and Statistics Department.
[7] Erdogan, B.E. (2008), “Bankruptcy Prediction of Turkish Commercial
Banks using Financial. Ratios” Applied Mathematical Sciences Vol. 2
No. 60, pp. 2973 – 2982..
[8] Jimoh, A. (1993, “The Role of Early warning Models in the
Identification of Problem Banks Evidence from Nigeria”. Nigeria
Financial Review. Vol. 6, No. 1, pp. 29-43.
[9] Konstandina, N.V. (2006) “Probability of Bank Failure: The Russian
Case” Paper presented at Economics Education and Research
Consortium: Russia and CIS.
[10] Kosoko(2007) “The Story of Banking Consolidation, Spring Bank as a
Case Study’’ Thisday Newspaper June 17, pp.14
[11] Lim, S.H. and Nam, K. (2006), “Artificial Neural Network Modeling in
Forecasting Successful Implementation of ERP Systems” International
Journal of Computational Intelligence Research, vol. 2 No. 1, pp. 115 –
119.
[12] Odife, D. (2009), “The rot started long ago” Newswatch Magazine, Vol.
50, No. 9, pp. 14-15.
[13] Ohlson, J. (1980), “Financial Ratios and the Probabilities Prediction of
Bankruptcy” Journal of Accounting Research Vol. 18 pp. 109 – 131.
[14] Olaniyi, T.A. (2007), “Predicting Potential of Failure in Nigerian
Banking Sector: A Comparative Analysis of First Bank Plc and Trade
Bank Plc”. Journal of Management and Social Sciences, Babcock
Univeristy. Vol. 6, No. 1, pp. 64-73.
[15] Sogunro, A.B. (2008), “Predicting Insurer Failures: An Artificial
Intelligence Approach”. M.Sc. Thesis, University of Lagos.
[16] Subramanian N., Yajnik, A., and Murthy, R.S.R. (2004), “Artificial
Neural Network as an Alternative to Multiple Regression Analysis in
Optimizing Formulation Parameters of Cytarabine Liposomes”
American Association of Pharmaceutical scientists.
www.aapspharm.scitech.org accessed 12/10/2010. .
[17] Vallini, C., Ciampi, F.and Gordini, N., (2009) “Using Artificial Neural
Network Analysis for Small Enterprise Default Prediction Modelling:
Statistical Evidence From Italian Firms”. Oxford Business and
Economics Conference Program.
[18] Watanabe, K. (2010), “Predicting Future Depositor’s Rate of Return
Applying Neural Network: A case study of Indonesian Islamic Bank”
International Journal of Economics and Finance Vol. 2 No. 3 pp. 1 - 9
[19] Zhang, G., Hu, M.Y., and Indro, D.C. (1999), “Artificial Neural
Networks in Bankruptcy prediction: General Framework and Cross –
Validation analysis”. European Journal of Operational Research.
www.sciencedirect.com accessed on 4/9/2010.
[1] Adefila, J.J. (2002) “Assessing Financial Strenght to Determine
Bankruptcy Potential: A case study of Trade Bank Plc” Nigerian Journal
of Management Science Vol. 30 No. 2, pp. 24-35.
[2] Altman, E.I. (1968), “Financial Ratios, Discriminant Analysis and the
Prediction of Corporate Bankruptcy” Journal of Finance, Vol. 23 p. 589
– 609. [3] Atiya, A.F., (2001) “Bankruptcy Prediction for credit Risk using Neural
Networks: A Survey and New Results” IEEE Transactions on Neural
Networks, Vol. 12 No. 4, pp. 929 – 935.
[4] Beaver, William H. (1966) “Financial Ratios as Predictions of Failure”
Emperical Research in Accounting. Selected Studies, Supplement to
Journal of Accounting Research vol. 4, pp. 77-111.
[5] Boyacioglu, M.A., Kara Y and Baykan, O.K. (2009), “Predicting bank
financial failures using neural neworks, support vector machines and
multivariate statistical methods: A comparative anslysis in the sample of
Savings Deposit Insurance Fund (SDIF) transferred banks in Turkey”
www.citibank.co.in vol 36 issue 2 pp. 3355-3366. Acessed on May 31,
2010.
[6] CBN, (2007) “A case study of Distressed Banks in Nigeria” Publication
of Research and Statistics Department.
[7] Erdogan, B.E. (2008), “Bankruptcy Prediction of Turkish Commercial
Banks using Financial. Ratios” Applied Mathematical Sciences Vol. 2
No. 60, pp. 2973 – 2982..
[8] Jimoh, A. (1993, “The Role of Early warning Models in the
Identification of Problem Banks Evidence from Nigeria”. Nigeria
Financial Review. Vol. 6, No. 1, pp. 29-43.
[9] Konstandina, N.V. (2006) “Probability of Bank Failure: The Russian
Case” Paper presented at Economics Education and Research
Consortium: Russia and CIS.
[10] Kosoko(2007) “The Story of Banking Consolidation, Spring Bank as a
Case Study’’ Thisday Newspaper June 17, pp.14
[11] Lim, S.H. and Nam, K. (2006), “Artificial Neural Network Modeling in
Forecasting Successful Implementation of ERP Systems” International
Journal of Computational Intelligence Research, vol. 2 No. 1, pp. 115 –
119.
[12] Odife, D. (2009), “The rot started long ago” Newswatch Magazine, Vol.
50, No. 9, pp. 14-15.
[13] Ohlson, J. (1980), “Financial Ratios and the Probabilities Prediction of
Bankruptcy” Journal of Accounting Research Vol. 18 pp. 109 – 131.
[14] Olaniyi, T.A. (2007), “Predicting Potential of Failure in Nigerian
Banking Sector: A Comparative Analysis of First Bank Plc and Trade
Bank Plc”. Journal of Management and Social Sciences, Babcock
Univeristy. Vol. 6, No. 1, pp. 64-73.
[15] Sogunro, A.B. (2008), “Predicting Insurer Failures: An Artificial
Intelligence Approach”. M.Sc. Thesis, University of Lagos.
[16] Subramanian N., Yajnik, A., and Murthy, R.S.R. (2004), “Artificial
Neural Network as an Alternative to Multiple Regression Analysis in
Optimizing Formulation Parameters of Cytarabine Liposomes”
American Association of Pharmaceutical scientists.
www.aapspharm.scitech.org accessed 12/10/2010. .
[17] Vallini, C., Ciampi, F.and Gordini, N., (2009) “Using Artificial Neural
Network Analysis for Small Enterprise Default Prediction Modelling:
Statistical Evidence From Italian Firms”. Oxford Business and
Economics Conference Program.
[18] Watanabe, K. (2010), “Predicting Future Depositor’s Rate of Return
Applying Neural Network: A case study of Indonesian Islamic Bank”
International Journal of Economics and Finance Vol. 2 No. 3 pp. 1 - 9
[19] Zhang, G., Hu, M.Y., and Indro, D.C. (1999), “Artificial Neural
Networks in Bankruptcy prediction: General Framework and Cross –
Validation analysis”. European Journal of Operational Research.
www.sciencedirect.com accessed on 4/9/2010.
@article{"International Journal of Business, Human and Social Sciences:65410", author = "D. A. Farinde", title = "A Statistical Prediction of Likely Distress in Nigeria Banking Sector Using a Neural Network Approach", abstract = "One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.
", keywords = "Bank, Bankruptcy, Financial Ratios, Neural Network, Multilayer Perceptron, Predictive Model", volume = "7", number = "10", pages = "2724-6", }
