Othman, J. and Asutay, M. (2018) 'Integrated early warning prediction model for Islamic banks : the Malaysian case.', Journal of banking regulation., 19 (2). pp. 118-130.
It is increasingly becoming important to predict the performance of Islamic banks in order to anticipate a problem before it materializes and negatively affects banks’ performance and financial standing. Benefiting from the earlier research on the subject, this study aims to develop a preliminary integrated early warning model for Islamic banks in Malaysia to assess their financial standing by using quarterly data for the 2005–2010 period. Factor analysis and three parametric models (discriminant analysis, logit analysis, and probit analysis) are used in this study. Out of 29 variables used in the early stage of study, only 13 were selected as predictor variables in this study. Results show that, overall, classification accuracy is relatively high in the first few quarters before the benchmark quarter (2010 Q3) for all the estimated models. Correct classification rates are high during the first few quarters and decrease subsequently. Based on these results, therefore, it is obvious that the first few quarters before the benchmark quarter are the most important for making a correct prediction. These results show the predictive ability of the integrated model to differentiate healthy and non-healthy Islamic banks, thus reducing the expected cost of bank failure.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1057/s41261-017-0040-5|
|Publisher statement:||This is a post-peer-review pre-copyedit version of an article published in Journal of Banking Regulation. The definitive publisher-authenticated version Othman, J. and Asutay, M. (2017) 'Integrated early warning prediction model for Islamic banks : the Malaysian case.', Journal of banking regulation is available online at: https://doi.org/10.1057/s41261-017-0040-5|
|Date accepted:||No date available|
|Date deposited:||31 May 2017|
|Date of first online publication:||21 April 2017|
|Date first made open access:||21 October 2018|
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