Predicting Bank Return on Equity (ROE) using Neural Networks

Measuring the performance and profitability of the banking sector, which is the most important part of a country's financial system, is always important. Thanks to the performance measurement, banks can understand the competitive situation, their potential to grow, and the risk, and be more successful in sustaining their lives. This study is considered all state deposit money banks in Turkey. In the literature, using of artificial neural networks (ANN) in banking performance evaluation is rarely studied. Therefore, this paper aims to examine the possibility of ANN utilization for predicting return on equity of Turkey State Deposit Money Banks. The paper compares the accuracy percentages of optimization algorithms of ANN using eleven years quarterly data of six exogenous variables and eight endogenous variables as independent variables and the average return on equity from quarterly of all Turkey state deposit money banks as dependent variable. Given a number of recorded financial parameters, the task is to predict banks' performances using ANN computation methods and to compare prediction results with real results. To evaluate these methods, we built a data set from Banking Regulation and Supervison of Agency, The Banks Association of Turkey and banks' quarterly financial reports. According to all experimental results in optimization models were estimated with above % 80 accuracy. It is determined that the best optimization model is different for each bank.