Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks

Abstract

The purpose of this study is to find the determinants of the profits for the Development and Investment Banks (IaDB) in Turkey. In Turkish Banking System, the main financial source of the banks is the deposits, which constitute almost%60 of the balance sheet. As being a sub-group of the banking system, IaDB are not allowed to accept deposits in Turkey, which changes the total structure of the profitability compared to other banks. Till today, none of the relevant research was concentrated on the profit structure of the IaDB neither in Turkey nor in any other countries. Such research would fill that unexpectedly disregarded yet highly important gap.Therefore, to address this gap, quarterly financial data (10 balance sheet ratios) of 13 banks in the period of 2002Q4-2014Q3 were utilized. As a profit measurement among all other available measures, Return on Equity was chosen as dependent variable as it was the most used one as well as many other researcher have preferred as well. This study investigates the potential usage of bagging (Bag), which is one of the most popular ensemble learning methods, in building ensemble models, is used to predict the determinants of Turkish IaDB profitability. Three well-known tree-based machine learning (ML) models (i.e., Decision Stump (DStump), Random Tree (RTree), Reduced Error Pruning Tree (REPTree)) are deployed as base learner. This empirical study indicates that bagging ensemble models (i.e., Bag-DStump, Bag-RTree, Bag-MLP and Bag-REPTree) are superior to their base learners and could improve the prediction accuracy of individual ML models (i.e., DStump, RTree, REPTree).