The Early Warning of Financial Failure for Iraqi Banks Based on Robust Adaptive Lasso Logistic Regression - Aqeel Jaafar Abbas Alshareefi; Hassan S. Uraibi alshamari (Iraqi Journal for Computer Science and Mathematics, 2024)
It is well known 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 stock market 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 considered as response variables. Then, a weighted adaptive lasso logistic regression algorithm that has a high breakdown point is used to tackle outliers’ problem. Thirteen banks have been chosen as the most traded during the period (2010-2017), and for 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.