• ISSN: 2010-023X (Print)
    • Abbreviated Title: Int. J. Trade, Economics and Financ.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJTEF
    • Editor-in-Chief: Prof.Tung-Zong (Donald) Chang
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/ Indexing:  ProQuest, Crossref, Electronic Journals Library , EBSCO, and Ulrich's Periodicals Directory
    • E-mail: ijtef@ejournal.net
IJTEF 2011 Vol.2(1): 44-51 ISSN: 2010-023X
DOI: 10.7763/IJTEF.2011.V2.77

Warning: error_log(/www/wwwroot/ijtef.org/caches/error_log.php): failed to open stream: Permission denied in /www/wwwroot/ijtef.org/phpcms/libs/functions/global.func.php on line 540

Comparing Accuracy Performance of ANN, MLR, and GARCH Model in Predicting Time Deposit Return of Islamic Bank

Saiful Anwar and Yoshiki Mikami
Abstract—The utilization of artificial neural networks (ANN) in Islamic banking research is rarely reported. Therefore, this paper aims to examine the possibility of ANN utilization in case of predicting mudharabah time deposit return. This paper compares the accuracy performance of artificial neural networks (ANN), multiple linear regressions (MLR) and generalized autoregressive conditional heteroscedasticity (GARCH) model. Ten years monthly data of six macroeconomic variables are selected as independent variables. Meanwhile, the average rate of return of one month mudharabah time deposit of Indonesian Islamic banks (RR) is selected as dependent variable. For this purpose, the research employs Alyuda neuro intelligent software version 2.2 to develop ANN model and Eviews software version 5.0 to develop MLR and GARCH model. The performance is evaluated using visual methodology by analyzing predicted graph and statistical parameters such as R2, Akaike's information criterion (AIC), mean absolute error (MAE) and mean absolute standard error (MASE). Accordingly, this research found that ANN outperforms MLR and GARCH model in explaining the volatility of RR. Even though GARCH model outperforms ANN in making out of sample data prediction, ANN achieves better accuracy performance in predicting one and two month ahead of out of sample data. All evidences demonstrate that ANN model provides more accurate prediction and is appropriate to be used in Islamic banking research.

Index Terms—Islamic bank, rate of return, macroeconomic variables, artificial neural networks, multiple linear regression.


Cite:Saiful Anwar and Yoshiki Mikami, "Comparing Accuracy Performance of ANN, MLR, and GARCH Model in Predicting Time Deposit Return of Islamic Bank," International Journal of Trade, Economics and Finance vol.2, no.1, pp. 44-51, 2011.

Copyright © 2008-2021. International Journal of Trade, Economics and Finance. All rights reserved.
E-mail: ijtef@ejournal.net