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Abstract

It is wellknown that there are many mathematical financial failure models that have been proposed in the financial literature for specific stock markets. Some researchers are not aware these mathematical models were constructed to be fitted for that market data, not for other markets. Iraq stock market exchange is one of these markets in which the researchers used imported models such as Kida, Sherrod, Altman, and others to predict financial failure. Therefore, the development of a financial failure warning model for banks has become very crucial for the Iraqi bank sector in the stockmarket exchange. Unfortunately, there is no clear information about the financial failure of Iraqi banks as a response variable, and the financial indicators contain outliers. The objective of this paper is to propose an algorithm to know the performance of efficient and inefficient banks based on their indicators during specific time periods. The output of this algorithm will be consideredas response variables. Then,a weighted adaptive lasso logistic regression algorithm that has a high breakdown pointis usedto tackle outliers’ problem. Thirteen banks have been chosen as the most traded during the period (2010-2017), andfor each bank (27) financial indicators were collected. Our proposed model is compared with adaptive lasso logistic regression by using Deviance, Misclassification, Area Under Curve, Mean Square Errors, and Mean Absolute Errors. Consequently, the results showed that the weighted Adaptive Lasso Logistic Regression model is more robust and relevant than others to be a financial model to warn of the failure of the banks in Iraq's stock market

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