مقارنة الانحدار المتعدد و الشبکات العصبية الاصطناعية للتنبؤ بالمصروفات البنکية

نوع المستند : المقالة الأصلية

المؤلفون

کلية التجارة - جامعة المنصورة

المستخلص

Prediction of bank expenses is one of the main tools for planning in banks as well as to maintain the balance and stability of banks in general. This study aims to estimate and use a statistical model to predict the appropriate banking expenses in the period (1995- 2014) in Libya, where multiple regression method and neural networks have been applied. A
comparison between the multiple regression models and neural networks has been made using measurement standards: coefficient of determination(R2),the mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and Thiel's inequality coefficient (TC). The research concluded that the best predicition model is the neural networks compared to the multiple regression model.

الموضوعات الرئيسية

المراجع

1. Asteriou and Hall (2007) Applied Econometrics, A Modern Approach Using Eviews and Microfit Revised.
2. McKinnon, R., (1999), "Critical Values for Co integration Tests," in Engle, R., and Granger, W., (ed.), "Long-Run Economic Relationships: Readings in Co
integration", by Oxford University Press, Oxford.
3. Anwar S, Watanabe K (2010), Performance Comparison of Multiple Linear Regression of Islamic Bank, 2010 International Conferenceon E-Business, Management and Economics Ipedr Vol.3 Iacsit Press, Hong Kong.
4. Anyaeche C O, Lghravwe D E, (2013), Predicting Performance Measures Using Linear Regression and Neural: A Comparison African Journal of Engineering Research Vol.1 (3), pp.84-89.
5. Apostolos Nichols (1994), Stock Performance Modeling Using Neural Network: A Comparative Study with Regression Models, Copyright  2014 Elsevier B.V Except Certain Content Provided by Third Parties Science direct is Registered Trademark of Elsevier B.V.
6. Asteriou and Hall (2007) Applied Econometrics, A
Modern Approach Using Eviews and Microfit Revised.
7. Bruce d , Baker, Craig e, Richards (1999), A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending, European Journal of 122(2000) 31-40.
8. Dickey, D.A. and Fuller, W., (1979), "Distribution of the Estimators for Autoregressive Time Series with a Unit Root", Journal of the American Statistical Association, Vol. 74, pp. 427-431.
9. Dickey, D.A. and Fuller, W., (1981), "Distribution of the Estimators for Autoregressive Time Series with a Unit Root", Econometrica, Vol. 49, pp. 1057-1072.
10. Fausett,L. (1994)"Fundamentals of Networks: Architectures and Applications" Uper Saddle River, NJ: Prentice Hall.
11. Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010).
Multivariate data analysis. (7th Edition). Upper Saddle River: Prentice Hall.
12. Haykins, S. (1999)"Neural Networks: A Comprehensive Foundation", Prentice-Hall, Inc., New Jersy, USA.
13. Hendry, D., (2006), Dynamic Econometrics, Oxford University Press.
14. Hodrick, R. J. and E. C. Prescott, (1997), "Postwar U.S. Business Cycles: An Empirical Investigation"", Journal of Money, Credit, and Banking, Vol. 29, pp. 1–16.
15. Inmaculada. Pulido - Calvo (2007), Linear Regression and Neural Forecasting in Irrigation Districts with Telemetry System, http://dx.doi.org/10.1016/j.biosystemseng.2007.03.003.
16. Johnson, R. A. and Wichern, W., (1996), Applied Multivariate Statistical Analysis (4th), New York: Patrice-Hell international.
17. Kumar.satish (2004)"Neural Networks a Classroom Approach. Tata, McGraw-Hill, New Delhi.
18. McKinnon, R., (1999), "Critical Values for Co integration Tests," in Engle, R., and Granger, W., (ed.), "Long-Run Economic Relationships: Readings in Co integration", by Oxford University Press, Oxford.
19. Patterson, D. (1995)"Artificial Neural Networks Theory and Application" Fourth Edition Prentice Hall Simon Schuster (Asia) Pte Itd Singapore.
20. Phillips, P. and Perron, P., (1988), "Testing for a Unit Root in Time Series", Econometrica, Vol. 75, pp. 335-346.
21. Sarokolaei M A, Alinezhad P and Khosroshahi M A (2012),A Comparative Study of Iranian Banks Efficiency by Using Artificial Neural Networks and Multi-Linear Regression, 2012 International Conference on Management and
Intelligence Ipedr vol.35 Lacsit Press, Singapore.
22. Shankar, T.N. (2008)."Neural Networks". University Science Press.
23. SPSS (2051). SPSS BASE 20.0 User's Guide. Chicago, IL: SPSS Inc.
24. Turban,E.(1999)"Integrating Expert Systems and Neural Computing for Decision Support" Expert Systems with Application, American University Washington,DC.
25. Victor R. Prybutok , Junsup Yi, David (2000), Comparison of Network Models with ARIMA and Regression Models for Prediction of Houston Daily MaximumOzone Concentrations, Economics of Education Review 18(1999) 405-415.
26. WWW.acc4arab.com
27. WWW.accdisussion.com
28. WWW.hrdiscussion.com
29. Wooldridge J. M. (2003) Econometric Analysis of Cross Section and Panel Data, MIT Press.

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