|تعداد مشاهده مقاله||103,792,849|
|تعداد دریافت فایل اصل مقاله||81,531,460|
An Integrated Neural Networks and MCMC Model to Predicting Bank’s Efficiency
|Advances in Industrial Engineering|
|دوره 54، شماره 1، فروردین 2020، صفحه 1-14 اصل مقاله (426.71 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/jieng.2021.312818.1743|
|Farideh Sobhanifard ؛ Mohammad Reza Shahraki|
|Industrial Engineering Department, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran|
|In the banking industry, there is intense competition between banks to attract resources and facilities. With the development of new services, bank managers try to improve their services and attract more customer deposits by differentiating between competitors' services. This research uses a two-stage TOPSIS method with the combination of neural network model and Monte Carlo simulation trading method to analyze and compare bank productivity forecasts with the 4 efficiency criteria of the banking industry. TOPSIS was first used in two steps to rate the efficiency of banks and then a model was created for banking performance with clear forecasting ability. Secondly, an MCMC sampling method and ANN training was presented. Integrated neural networks and MCMCs were used which are consistent with TOPSIS results. The simulation effect of the selected variables was predicted and their effect on performance was observed. The proposed method was used successfully for predicting performance and ranking banks based on the relative importance of performance criteria expressed by considering the performance levels in the TOPSIS method. Then, the artificial neural network was modeled using the results obtained from the TOPSIS method, an effective model for appropriate prediction of bank performance. Based on the results of the proposed model and the level of importance of performance measures, cost and revenue structure were considered to be the main causes of inefficiency|
|Forecast؛ TOPSIS؛ Neural Networks؛ Monte Carlo؛ Efficiency|
 Matousek, R., Rughoo, A., Sarantis, N., Assaf, G.A., )2014(. Bank performance and convergence during the financial crisis: evidence from the ‘old’ EuropeanUnion and Eurozone. J. Bank. Finance,
 Hemmati, M., Dalghandi, S.A., Nazari, H., )2013(. Measuring relative performance of banking industry using a DEA and TOPSIS. Meas. Sci. Lett. 3 (2), 499–503.
 Sufian, F., Kamarudin, F., Noor, N.H.H.M., )2014(. Revenue efficiency and returns to scale in Islamic Banks: empirical evidence from Malaysia. J. Econ. Coop.Dev. 35 (1), 47–80
 Harvey, A., & Kattuman, P. (2020). Time series models based on growth curves with applications to forecasting coronavirus. Harvard Data Science Review.
 Lampe, H.W., Hilgers, D., )2014(. Trajectories of efficiency measurement: a bibliometric analysis of DEA and SFA. Eur. J. Oper. Res. (in press).
 Maghyereh, A.I., Awartani, B., (2012). Financial integration of GCC banking markets: a non-parametric bootstrap DEA estimation approach. Res. Int. Bus.Finance 26 (2), 181–195.
 Bilbao-Terol, A., Arenas- Parra, M., Canal ˜ Fernandes, V., 2014. Using Topsis for assessing the sustainability of government bond funds. Omega 49, 1–17.
 San, O.T., Theng, L.Y., Heng, T.B., )2011(. A comparison on efficiency of domestic and foreign banks in Malaysia: a DEA approach. Bus. Manag. Dyn. 1 (4),33–49.
 Geisser, S., )1993(. Predictive Inference: an Introduction. Chapman & Hall, New York.
 Azad, A.S.M.S., Yasushi, S., Fang, V., Ahsan, A., (2014). Impact of policy changes on the efficiency and returns-to-scale of Japanese financial institutions: an evaluation. Res. Int. Bus. Finance 32, 159–171.
 Kuhn, M., Johnson, K., )2013(. Applied Predictive Modeling. Springer, New York.
 Chen, M.-C.,) 2007(. Ranking discovered rules from data mining with multiple criteria by data envelopment analysis. Expert Syst. Appl. 33 (4), 1110e1116.
 Wanke, P., Azad, M.D.A., Barros, C.P., )2016(. Predicting effciency in Malaysian Islamic bank: A two-stage TOPSIS and neural networks approach. Business and Finance. 36, 485–498.
 Huang, I.B.; Keisler, J.; Linkov, I. (2011). "Multi-criteria decision analysis in environmental science: ten years of applications and trends". Science of the Total Environment 409: 3578–3594.
 Maschio, C., Schiozer, D.J., (2014).Bayesian history matching using artificial neural network and Markov Chain Monte Carlo. Journal of Petroleum Science and Engineering. Caixa Postal 6122, 13.083-970.
 Ledolter, J., )2013(. Data Mining and Business Analytics with R. Wiley, New Jersey.
 Freund, J. E. (1992). Mathematical Statistics: Prentice- Hall.
 Sufian, F., Mohamad, A., Muhamed-Zulkhibri, A.M., )2008(. The efficiency of Islamic Banks: empirical evidence from the MENA and Asian countries Islamicbanking sectors. Middle East Bus. Econ. Rev. 20 (1), 1–19.
 Simar, L.,Wilson,P.W., (1998). Sensitivity analysis of efficiency scores: how to bootstrap in non parametric frontier models. Manag.Sci. 44(1), 49–61.
 Xu, C., He, H.S., Hu, Y., Chang, et al., (2005). Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation. Ecol. Model. 185, 255–269.
 Osei-Bryson, K.-M., Ngwenyama, O., )2014(. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking. Springer Series, New York.
 Chen, Y.-S., Cheng, C.-H., )2013(. Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry. Knowl. Based Syst. 39, 224e239. http://dx.doi.org/10.1016/j.knosys.2012.11.004.
 Tsui, E., Garner, B.J., Staab, S., )2000(. The role of artificial intelligence in knowledgemanagement. Knowl. Based Syst. 13 (5), 235-239.
تعداد مشاهده مقاله: 243
تعداد دریافت فایل اصل مقاله: 161