Time Series Analysis of Performance Efficiency of MCB Bank Limited
DOI:
https://doi.org/10.24297/ijmit.v12i1.6062Abstract
This research investigates the Time Series Analysis of Performance efficiency of MCB Bank Limited. Data were collected from Primary as well as secondary sources from management of commercial banks and from SBP officials comprising middle and top management, a closed ended questionnaire. It was revealed that As stated by the findings, five a considerable length of time Normal proportion about MCB is 81. 20%, which will be higher after that the business Normal. This indicates the execution from claiming MCB will be similarly finer as contrasted with those Normal of UBL, which might have been attempting openly division At as of late privatized. Same time those execution from claiming UBL may be superior At that point ABL which might have been handy in the begin However fair for administration issue Previously, 1999. This demonstrates that UBL need Additionally carried out great its possessions to fill in Be that not finer after that MCB.
Downloads
Download data is not yet available.
References
1. Smith, K.A. and Gupta, J.N.D. (2002) Neural Networks in Business: Techniques and Applications. Idea Group Publishing, Hershey. SPSS, Inc. (2010) PASW Modeler 14 Algorithms Guide, SPSS, Inc., Chicago. Standard and Poor's Ratings Services (2012a) 'Credit ratings definitions & FAQs', [online].Available at : http://www.standardandpoors.com/ratings/definitions-andfaqs/en/us (Accessed at: 23 June 2012) Standard and Poor's Ratings Services (2012b) 'Standard and Poor's ratings definitions', [online]. Available at: https://www.globalcreditportal.com/ratingsdirect/renderArticle.do?articleId=101944 2&SctArtId=147045&from=CM&nsl_code=LIME (Accessed at: 29 June 2012) Standard and Poor's Ratings Services (2013) 'Guide to credit rating essentials: What are credit ratings and how do they work?', [online].Available at :http://img.en25.com/Web/StandardandPoors/SP_CreditRatingsGuide.pdf (Accessed:25 March 2013). Stein, J.C. (1998) ' An adverse-selection model of bank asset and liability management with implications for transmission of monetary policy', RAND Journal of Economics, Vol. 29, No. 3, pp. 466-486. 238
2. Stiglitz, J.E. and Weiss, A. (1981) ' Credit rationing in markets with imperfect information', The American Economic Review, Vol. 71, No. 3, pp. 393-410.
3. Studenmund, A.H. (2000) Using Econometrics: A Practical Guide, 4 th ed., Addison Wesley Longman, New York. Sy, A.N.R. (2009) 'The systemic regulation of credit rating agencies and rated markets', IMF Working Paper No. 09/129.
4. Taffler, R.J. (1978) 'The assessment of financial viability using published accounting information and a multivariate approach', British Accounting Review, Vol. 10, No. 1, pp.53-68.
5. Taffler, R.J. (1982) 'Forecasting company failure in the UK using discriminant analysis and financial ratio data', Journal of the Royal Statistical Society, Vol. 145, No. 3, pp.342- 358.
6. Taffler, R.J. (1983) 'The assessment of company solvency and performance using a statistical model', Accounting and Business Research, Vol. 13, No. 52, pp.295-308.
7. Taffler, R.J. (1984) 'Empirical models for the monitoring of UK corporations', Journal of Banking and Finance, Vol. 8, No. 2, pp.199-227.
8. Talmor, E. (1980) 'A normative approach to bank capital adequacy', Journal of Financial and Quantitative Analysis, Vol. 15, No. 4, pp.785-811.Tam, K.Y. (1991) 'Neural network models and the prediction of bank bankruptcy', OMEGA, Vol. 19, No. 5, pp.429-445.
9. Tam, K.Y. and Kiang, M. (1990) 'Predicting bank failures: a neural network approach', Applied Artificial Intelligence, Vol. 4, No. 4, pp.265-282.
10. Tam, K.Y. and Kiang, M.Y. (1992) 'Managerial applications of neural networks: the case of bank failure predictions', Management Science, Vol. 38, No. 7, pp.926-947.
11. Thomas, L.C., Edelman, D.B. and Crook, J.N. (2002) Credit Scoring and its Applications, Society for Industrial and Applied Mathematics, Philadelphia. Tsai, C.-F. and Chen, M.-L. (2010) 'Credit rating by hybrid machine learning techniques', Applied Soft Computing, Vol. 10, No. 2, pp.374-380.
12. Tsukuda, J. and Baba, S.-I. (1994) 'Predicting Japanese corporate bankruptcy in terms of financial data using neural networks', Computers and Industrial Engineering, Vol. 27, No. 1-4, pp.445-448.
13. U.S. Securities and Exchange Commission (2003) 'Report on the role and function of credit rating agencies in the operation of the securities markets', [online]. Available on: http://www.sec.gov/news/studies/credratingreport0103.pdf (Accessed : 9 April 2011) Udo, G. (1992) 'The implications of neural networks for improving business effectiveness: an analysis', International Journal of Management, Vol. 9, No. 4, pp.389-398. 239
14. Udo, G. (1993) 'Neural network performance on the bankruptcy classification problem', Computers and Industrial Engineering, Vol. 25, No. 1-4, pp.377-380.
15. Van-Roy, P. (2006) 'Is there a difference between solicited and unsolicited bank ratings and if so, why?', National Bank of Belguim Working Paper No. 79. Verbeek, M. (2012) A Guide to Modern Econometrics, 4 th ed., John Wiley & Sons, Inc., West Sussex, United Kingdom. Vives, X. (2006) 'Banking and regulation in emerging markets: the role of external discipline', The World Bank Research Observer, Vol. 21, No. 2, pp.179-206.
16. Wagner, W. (2007) 'The liquidity of bank assets and banking stability', Journal of Banking and Finance, Vol. 31, No. 1, pp.121-139.Wagster, J. (1999) 'The Basel Accord of 1988 and the international credit crunch of 1989- 1992', Journal of Financial Services Research, Vol. 15, No. 2, pp.123-143.
17. Wall, L.D. and Peterson, D.R. (1987) 'The effect of capital adequacy guidelines on large bank holding companies', Journal of Banking and Finance, Vol. 11, No.4, pp.581-600
18. . Weber, R.H. and Darbellay, A. (2008) 'The regulatory use of credit ratings in bank capital requirement regulations', Journal of Banking Regulation, Vol. 10, No. 1, pp.1-16.
19. West, D. (2000) 'Neural network credit scoring models', Computers and Operations Research, Vol. 27, No. 11-12, pp.1131-1152.
20. Westgaard, S. and Wijst, N.V.D. (2001) 'Default probabilities in a corporate bank portfolio: a logistic model approach', European Journal of Operational Research, Vol. 135, No. 2, pp.338-349.
21. Wheelock, D.C. and Wilson, P.W. (2000) 'Why do banks disappear: the determinants of U.S. Bank failures and acquisitions', The Review of Economics and Statistics, Vol. 82, No. 1, pp.127-138.
22. White, L.J. (2010) 'Markets: The credit rating agencies', Journal of Economic Perspectives, Vol. 24, No. 2, pp.211-226. Widrow, B., Rumelhart, D.E. and Lehr, M.A. (1994) 'Neural networks: applications in industry, business and science', Communications of the ACM, Vol. 37, No. 3, pp.93- 105.
23. Wiginton, J.C. (1980) 'A note on the comparison of logit and discriminant models of consumer credit behavior', Journal of Financial and Quantitative Analysis, Vol. 15, No. 3, pp.757-770.
24. Wilcox, J.W. (1971) 'A simple theory of financial ratios as predictors of failure', Journal of Accounting Research, Vol. 9, No. 2, pp.389-395.
25. Wilson, R.L. and Sharda, R. (1994) 'Bankruptcy prediction using neural networks', Decision Support System, Vol. 11, No. 5, pp.545-557. 240
26. Wong, B.K., Bodnovich, T.A. and Selvi, Y. (1997) 'Neural network applications in business: a review and analysis of the literature (1988-95)', Decision Support Systems, Vol. 19, No. 4, pp.301-320.
27. Yang, Z., Wang, Y., Bai, Y. and Zhang, X. (2004) 'Measuring scorecard performance', Computational Science-ICCS 2004, Vol. 3039, pp.900-906.
28. Yap, B.W., Ong, S.H. and Husain, N.H.M. (2011) 'Using data mining to improve assessment of credit worthiness via credit scoring models', Expert Systems with Applications, Vol. 38, No. 10, pp.13274-13283.
29. Zhang, G., Hu, M.Y., Patuwo, B.E. and Indro, D.C. (1999) 'Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis', European Journal of Operational Research, Vol. 116, No. 1, pp.16-32.
30. Zhao, H., Sinha, A.P. and Ge, W. (2009) 'Effects of feature construction on classification performance: an empirical study in bank failure prediction', Expert Systems with Applications, Vol. 36, No. 2, pp.2633-2644.
2. Stiglitz, J.E. and Weiss, A. (1981) ' Credit rationing in markets with imperfect information', The American Economic Review, Vol. 71, No. 3, pp. 393-410.
3. Studenmund, A.H. (2000) Using Econometrics: A Practical Guide, 4 th ed., Addison Wesley Longman, New York. Sy, A.N.R. (2009) 'The systemic regulation of credit rating agencies and rated markets', IMF Working Paper No. 09/129.
4. Taffler, R.J. (1978) 'The assessment of financial viability using published accounting information and a multivariate approach', British Accounting Review, Vol. 10, No. 1, pp.53-68.
5. Taffler, R.J. (1982) 'Forecasting company failure in the UK using discriminant analysis and financial ratio data', Journal of the Royal Statistical Society, Vol. 145, No. 3, pp.342- 358.
6. Taffler, R.J. (1983) 'The assessment of company solvency and performance using a statistical model', Accounting and Business Research, Vol. 13, No. 52, pp.295-308.
7. Taffler, R.J. (1984) 'Empirical models for the monitoring of UK corporations', Journal of Banking and Finance, Vol. 8, No. 2, pp.199-227.
8. Talmor, E. (1980) 'A normative approach to bank capital adequacy', Journal of Financial and Quantitative Analysis, Vol. 15, No. 4, pp.785-811.Tam, K.Y. (1991) 'Neural network models and the prediction of bank bankruptcy', OMEGA, Vol. 19, No. 5, pp.429-445.
9. Tam, K.Y. and Kiang, M. (1990) 'Predicting bank failures: a neural network approach', Applied Artificial Intelligence, Vol. 4, No. 4, pp.265-282.
10. Tam, K.Y. and Kiang, M.Y. (1992) 'Managerial applications of neural networks: the case of bank failure predictions', Management Science, Vol. 38, No. 7, pp.926-947.
11. Thomas, L.C., Edelman, D.B. and Crook, J.N. (2002) Credit Scoring and its Applications, Society for Industrial and Applied Mathematics, Philadelphia. Tsai, C.-F. and Chen, M.-L. (2010) 'Credit rating by hybrid machine learning techniques', Applied Soft Computing, Vol. 10, No. 2, pp.374-380.
12. Tsukuda, J. and Baba, S.-I. (1994) 'Predicting Japanese corporate bankruptcy in terms of financial data using neural networks', Computers and Industrial Engineering, Vol. 27, No. 1-4, pp.445-448.
13. U.S. Securities and Exchange Commission (2003) 'Report on the role and function of credit rating agencies in the operation of the securities markets', [online]. Available on: http://www.sec.gov/news/studies/credratingreport0103.pdf (Accessed : 9 April 2011) Udo, G. (1992) 'The implications of neural networks for improving business effectiveness: an analysis', International Journal of Management, Vol. 9, No. 4, pp.389-398. 239
14. Udo, G. (1993) 'Neural network performance on the bankruptcy classification problem', Computers and Industrial Engineering, Vol. 25, No. 1-4, pp.377-380.
15. Van-Roy, P. (2006) 'Is there a difference between solicited and unsolicited bank ratings and if so, why?', National Bank of Belguim Working Paper No. 79. Verbeek, M. (2012) A Guide to Modern Econometrics, 4 th ed., John Wiley & Sons, Inc., West Sussex, United Kingdom. Vives, X. (2006) 'Banking and regulation in emerging markets: the role of external discipline', The World Bank Research Observer, Vol. 21, No. 2, pp.179-206.
16. Wagner, W. (2007) 'The liquidity of bank assets and banking stability', Journal of Banking and Finance, Vol. 31, No. 1, pp.121-139.Wagster, J. (1999) 'The Basel Accord of 1988 and the international credit crunch of 1989- 1992', Journal of Financial Services Research, Vol. 15, No. 2, pp.123-143.
17. Wall, L.D. and Peterson, D.R. (1987) 'The effect of capital adequacy guidelines on large bank holding companies', Journal of Banking and Finance, Vol. 11, No.4, pp.581-600
18. . Weber, R.H. and Darbellay, A. (2008) 'The regulatory use of credit ratings in bank capital requirement regulations', Journal of Banking Regulation, Vol. 10, No. 1, pp.1-16.
19. West, D. (2000) 'Neural network credit scoring models', Computers and Operations Research, Vol. 27, No. 11-12, pp.1131-1152.
20. Westgaard, S. and Wijst, N.V.D. (2001) 'Default probabilities in a corporate bank portfolio: a logistic model approach', European Journal of Operational Research, Vol. 135, No. 2, pp.338-349.
21. Wheelock, D.C. and Wilson, P.W. (2000) 'Why do banks disappear: the determinants of U.S. Bank failures and acquisitions', The Review of Economics and Statistics, Vol. 82, No. 1, pp.127-138.
22. White, L.J. (2010) 'Markets: The credit rating agencies', Journal of Economic Perspectives, Vol. 24, No. 2, pp.211-226. Widrow, B., Rumelhart, D.E. and Lehr, M.A. (1994) 'Neural networks: applications in industry, business and science', Communications of the ACM, Vol. 37, No. 3, pp.93- 105.
23. Wiginton, J.C. (1980) 'A note on the comparison of logit and discriminant models of consumer credit behavior', Journal of Financial and Quantitative Analysis, Vol. 15, No. 3, pp.757-770.
24. Wilcox, J.W. (1971) 'A simple theory of financial ratios as predictors of failure', Journal of Accounting Research, Vol. 9, No. 2, pp.389-395.
25. Wilson, R.L. and Sharda, R. (1994) 'Bankruptcy prediction using neural networks', Decision Support System, Vol. 11, No. 5, pp.545-557. 240
26. Wong, B.K., Bodnovich, T.A. and Selvi, Y. (1997) 'Neural network applications in business: a review and analysis of the literature (1988-95)', Decision Support Systems, Vol. 19, No. 4, pp.301-320.
27. Yang, Z., Wang, Y., Bai, Y. and Zhang, X. (2004) 'Measuring scorecard performance', Computational Science-ICCS 2004, Vol. 3039, pp.900-906.
28. Yap, B.W., Ong, S.H. and Husain, N.H.M. (2011) 'Using data mining to improve assessment of credit worthiness via credit scoring models', Expert Systems with Applications, Vol. 38, No. 10, pp.13274-13283.
29. Zhang, G., Hu, M.Y., Patuwo, B.E. and Indro, D.C. (1999) 'Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis', European Journal of Operational Research, Vol. 116, No. 1, pp.16-32.
30. Zhao, H., Sinha, A.P. and Ge, W. (2009) 'Effects of feature construction on classification performance: an empirical study in bank failure prediction', Expert Systems with Applications, Vol. 36, No. 2, pp.2633-2644.
Downloads
Published
2017-04-26
How to Cite
Salih Memon, D. M., Ahmed Bhatti, D., Mirza, A., Shaikh, D., Ali Kartio, D., & Muhammad Shaikh, D. (2017). Time Series Analysis of Performance Efficiency of MCB Bank Limited. INTERNATIONAL JOURNAL OF MANAGEMENT &Amp; INFORMATION TECHNOLOGY, 12(1), 3113–3122. https://doi.org/10.24297/ijmit.v12i1.6062
Issue
Section
Articles
License
All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.
