Iranian Journal of Finance

Abstract Banks play a vital role in the economy by offering various financial services. One of their core functions is channelling surplus funds from units with excess resources to those with deficits, even when the latter have viable investment opportunities. This intermediary role is particularly crucial in developing economies, where capital markets are often underdeveloped and limited in scope. This study focuses on one of the most essential functions of banks—liquidity creation. Liquidity is generated when banks transform liquid liabilities into illiquid assets. While this process is fundamental to banking operations, it also introduces potential risks, especially when liquidity levels decline. In such cases, banks may become vulnerable to liquidity and credit risks. The capital adequacy ratio (CAR), disclosed in financial statements, serves as an important indicator of a bank’s resilience and its capacity to absorb losses and manage financial risks. This study investigates the relationship between liquidity creation and CAR using data from a sample of banks over the period 2011–2019, incorporating several control variables. The results support the financial fragility–crowding out hypothesis, indicating a negative relationship between liquidity creation and capital adequacy. Among the control variables, the deposit-to-asset ratio, non-interest income ratio, and bank size negatively influence CAR. In contrast, return on assets (ROA) shows a positive association, enhancing capital adequacy.

Abstract In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics.
The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.