Open Access
Issue |
SHS Web Conf.
Volume 36, 2017
The 2016 4th International Conference on Governance and Accountability (2016 ICGA)
|
|
---|---|---|
Article Number | 00016 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/shsconf/20173600016 | |
Published online | 24 July 2017 |
- Abu Bakar, N.M. and Mohd Tahir. I. (2009), “Applying Multiple Linear Regression and Neural Network to Predict Bank Performance”, International Business Research, vol 2, no 4, pp.176–181. [Google Scholar]
- Alifiah, M. N., Salamudin, N., & Ahmad, I. (2013), “Prediction of Financial Distress Companies in the Consumer Products Sector in Malaysia”, Jurnal Teknologi, 64(1). [CrossRef] [Google Scholar]
- Al-Osaimy, M.H. (1998), “A Neural Networks System for Predicting Islamic Banks Performance”, Journal of King Abdul Azis University: Islamic Economic & Administration, vol.11, no.1, pp.33–46. [CrossRef] [Google Scholar]
- Altman, E., G. Marco and F. Varetto, (1994), “Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience)”, Journal of Banking and Finance, Vol. 18, pp. 505–529 [CrossRef] [Google Scholar]
- Athanasoglou, P P, Brissimis, S N, and Delis, M. D. (2008), “Bank-specific, industry-specific and macroeconomic determinants of bank profitability”. International, Financial Markets, Institution and Money, vol. 18, 21–136. [CrossRef] [Google Scholar]
- Bashir, A. (2000), “Assessing the performance of Islamic banks: Some evidence from the Middle East”. Paper presented at the ERF 8th meeting in Jordan. [Google Scholar]
- Bourke, P. (1989), “Concentration and other determinants of bank profitability in Europe, North America and Australia”, Journal of Banking and Finance, vol.13, pp.65–79. [Google Scholar]
- Celik A.E. & Karatepe Y. (2007), “Evaluating and forecasting banking crises through neural network models: An application for Turkish banking sector”. Expert Systems with Applications, 33 pp: 809–815. [CrossRef] [Google Scholar]
- Coats, P.K. and Fant, L.F. (1993), “Recognizing financial distress patterns using a neural network tool”, Financial Management, Vol. 22 No. 3, pp. 142–56. [CrossRef] [Google Scholar]
- Demirguc-Kunt, A. and Huizinga, H. (2000), “Financial structure and bank profitability”. Policy Research Working Paper Series 2430. The World Bank. [Google Scholar]
- Dutta, S. and S. Shekhar, (1988), “Bond Rating: A Non-Conservative Application of Neural Networks”, Proceedings of IEEE International Conference on Neural Networks, IEEE Press, Alamitos, CA, Vol. 2, pp. 443–450. [Google Scholar]
- Ernst and Young, (2014), World Islamic Banking Competitiveness Report 2013–14: The Transition Begins (London). [Google Scholar]
- Eletter, S.F. & Yaseen, S. G. (2010), “Applying Neural Networks for Loan Decisions in the Jordanian Commercial Banking System”, International Journal of Computer Science and Network Security, VOL.10 No.1, pp: 209–214 [Google Scholar]
- Gunay, O. E. N. and Ozkan, M. (2007), “Prediction of bank failures in emerging financial markets: an ANN approach”. The Journal of Risk Finance, Vol. 8 No. 5, pp. 465–480 [CrossRef] [Google Scholar]
- Hassan, M. and Bashir, A. (2003), “Determinants of Islamic banking profitability”. Paper presented at the ERP 10th Annual Conference in Morroco. [Google Scholar]
- Haron, S. (2004), “Determinants of Islamic Bank Profitability”, Global Journal of Finance and Economics. USA, vol 1, No 1. [Google Scholar]
- Handzic, M.T, Rawibawa F. and Yeo J. (2003). “How Neural Networks Can Help Loan Officers to Make Better Informed Application Decisions”, Informing Science Insite [Google Scholar]
- How, J. C., Melina, A. K. and Verhoeven, P. (2005), “Islamic financing and bank risks: the case of Malaysia”, Thunderbird International Business Review, 47(1),pp.75–94. [CrossRef] [Google Scholar]
- Islamic Financial Services Board (IFSB), (2014), Islamic Financial Services Industry Stability Report (Kuala Lumpur) [Google Scholar]
- Kamijo, K. and T. Tanigawa, (1990), “Stock Price Pattern Recognition: A Recurrent Neural Network Approach”, Proceedings of IEEE International Conference on Neural Networks, IEEE Press, Alamitos, CA, Vol. 1, pp. 215–221. [Google Scholar]
- Koster, A., N. Sondak and W. Bourbia, (1990), “A Business Application of Artificial Neural Network System”, The Journal of Computer Information Systems, Vol. XI, pp. 3–10 [Google Scholar]
- Kryzanwski, L., M. Galler and D. Wright, (1993), “Using Artificial Neural Networks to Pick Stock”, Financial Analysis Journal, Vol. 12, No. 1, pp. 21–27. [CrossRef] [Google Scholar]
- Malhorta R. And Malhorta D.K., (2003), Evaluating Consumer Loans Using Neural Networks, Elsevier Science Ltd. [Google Scholar]
- Mitchell D. and Pavur R., (2002), “Using Modular Neural Networks for Business Decisions”, Management Decision, Vol. 40 Issue 1 pp. 58–63 [CrossRef] [Google Scholar]
- Molyneux, P. and Thornton, J. (1992), “Determinants of European bank profitability: a note”, Journal of Banking and Finance, vol.16, pp.1173–1178. [Google Scholar]
- Nienhaus, V. (1983), “Profitability of Islamic PLS Banks Competing with Interest Banks- Problems and Prospects”, Journal of Research in Islamic Economics, vol. 1 (1), pp. 37–47. [Google Scholar]
- Odom, M. and Sharda, R. (1990), “A neural network model for bankruptcy prediction”. Proceedings of the IEEE International Conference on Neural Networks 2, 163–168. [Google Scholar]
- Ravi, V., Kurniawan, H., Thai, P.N.K. and Kumar, P. R. (2008), “Soft computing system for bank performance prediction. Applied Soft Computing”, Volume 8, Issue 1, January 2008, Pages 305–315 [Google Scholar]
- Rosly, SA and Abu Bakar MA (2003), “Performance of Islamic and mainstream banks in Malaysia”. International Journal of Social Economics, 30(12),pp.1249–1265. [CrossRef] [Google Scholar]
- Samad, A., and Hassan, M. K. (2000), “The performance of Malaysian Islamic Bank During 1984-1997: An Explanatory Study”. Thoughts on Economics, 10(1&2), 7–26. [Google Scholar]
- Sanusi, N. and Mohammed, N. (2005), “Profitability of an Islamic Bank: Panel Evidence from Malaysia”, in Sanusi, N. Harun, M and Samsudin, S. (Ed.), Reading in Islamic Economics and Finance, Penerbit Universiti Utara Malaysia, Kedah, pp: 97–116. [Google Scholar]
- Sapuan, N.M. and Roly, M.R. (2015), “Bank Profitability and Bank-Specific Variables in Malaysia: A Panel Cointegration and Error Correction Model”. Journal of Islamic Finance and Business Research, Vol. 3. No. 1. March 2015 Issue, pp: 50–61 [Google Scholar]
- Surkan, A. and J. Singleton, (1990), “Neural Networks for Bond Rating Improved by Multiple Hidden Layers”, Proceedings of IEEE International Conference on Neural Networks, IEEE Press, Alamitos, CA, Vol. 2, pp. 157–162 [Google Scholar]
- Tam, K.Y. and Kiang, M.Y. (1992), “Managerial applications of neural networks: the case of bank failure predictions”, Management Science, Vol. 38, pp. 926–47. [CrossRef] [Google Scholar]
- Tsukuda, J. and S. Baba, (1994), “Predicting Japanese Corporate Bankruptcy In Term of Financial Data Using Neural Network”, Selected papers from the 16th Annual Conference on Computers and Industrial Engineering, Elsevier Science Ltd., Vol. 27, Nos 1-4, pp. 445–448. [Google Scholar]
- Turen, S (1995), “Performance and risk analysis of the Islamic Banks: The Case of Bahrain Islamic Bank”. Journal of Islamic Economics, vol. 7, pp.3–13. [Google Scholar]
- White, H., (1988), “Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns”, Proceedings of IEEE International Conference on Neural Networks, IEEE Press, San Diego, CA, pp. 451–459. [Google Scholar]
- Yoon, Y. and G. Swales, (1990), “Predicting Stock Price Performance”, Proceedings of the 24th Hawaii International Conference on System Sciences, IEEE Press, Alamitos, CA, Vol. 4, pp. 156–162 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.