Central bank digital currency, loan supply, and bank failure risk: a microeconomic approach

Central bank digital currencies (CBDCs), which are legal tenders in digital form, are expected to reduce currency issuance and circulation costs and broaden the scope of monetary policy. In addition, these currencies may also reduce consumers’ need for conventional demand deposits, which, in turn, increases banks’ loan provision costs because deposits require higher rates of return. We use a microeconomic banking model to investigate the effects of introducing an economy-wide, account-type CBDC on a bank’s loan supply and its failure risk. Given that a CBDC is expected to lower the cost of liquidity circulation and become a strong substitute for demand deposits, both the loan supply and the bank failure risk increase. These increases are countered by subsequent increases in the rates of return on term deposits and loans, which, in turn, reduce the loan supply and thus bank failure risk. These offsetting forces lead to no significant change in banking, as long as the rate of return on loans is below a certain threshold. However, once the rate is above the threshold, bank failure risk increases, thereby undermining banking stability. The problem is more pronounced when the degree of pass-through of funding costs to the loan rate is high and the profitability of a successful project is low. Our results imply that central banks wishing to introduce an economy-wide, account-type CBDC should first monitor yields on bank loans and consider policy measures that induce banks to maintain adequate liquidity reserve levels.

Forecasting Bank Failure with Machine Learning Models: A study on Turkish Banks

Forecasting bank failures has been an essential study in the literature due to their significant impact on the economic prosperity of a country. Acting as an intermediary player, banks channel funds from those with surplus capital to those who require capital to carry out their economic activities. Therefore, it is essential to generate early warning systems that could warn banks and stakeholders in case of financial turbulence. In this paper, three machine learning models named as GLMBoost, XGBoost, and SMO were used to forecast bank failures. We used commercial bank failure data of Turkey between 1997 and 2001, where we have 17 failed and 20 healthy banks. Our results show that the Sequential Minimal Optimization and GLMBoost provide the same performance when classifying failed banks, while GLMBoost performs better in AUC and SMO when considering total classification success. Lastly, XGBoost, one of the most recent and robust classification models, surprisingly underperformed in all three metrics we used in research.

The Role of Audit Firms in Spreading Depositor Contagion

Auditor credibility is important in the banking industry due to the opacity of bank assets and the use of financial statements by external parties to facilitate monitoring. Depositors monitor and discipline bank behavior, but they can also contribute to the spread of shocks from one bank to another. We argue that depositors perceive bank failure as an audit failure, which reduces their assessment of auditor credibility. We document that exposure to failure through the audit firm is associated with lower uninsured deposit growth following the failure, consistent with depositors perceiving failures as a negative signal of auditor credibility. We further document that this association is stronger when depositors perceive connection to failure to reflect a pervasive issue within the audit firm. Collectively, our results suggest that depositors consider accounting signals at other banks in assessing financial reporting credibility.

Observing Enforcement: Evidence from Banking

This paper finds that the disclosure of supervisory actions is associated with changes in regulators' enforcement behavior. Using a novel sample of enforcement decisions and orders (EDOs) and the setting of the 1989 Financial Institutions Reform, Recovery, and Enforcement Act (FIRREA), which required the public disclosure of EDOs, we find that U.S. bank regulators issue more EDOs, intervene sooner, and rely more on publicly observable signals after the disclosure regime change. The content of EDOs also changes, with documents becoming more complex and boilerplate. Our results are stronger in counties with higher news circulation, indicating that disclosure plays an incremental role in regulators' changing behavior. We evaluate the main potentially confounding changes around FIRREA, including the S&L crisis and competition from thrifts, and find robust results. We also study changes in bank outcomes following the regime change and find that uninsured deposits decline at EDO banks, especially for banks with EDOs covered in the news. Finally, we observe that bank failure accelerates despite improvements in capital ratios and asset quality.

“The Crooked Smile of TCR”: Banks’ Solvency and Restructuring Costs in the European Banking Industry

In this study, we analyze the probability of bank failure, the expected losses, and the costs of bank restructuring with the application of a lognormal distribution probability function for three categories of European banks, that is, small, medium, and large, over the post-crisis period from 2012 to 2016. Our goal was to determine whether the total capital ratio (TCR) properly reflects banks’ solvency under stress conditions. We identified a phenomenon that one can call the “crooked smile of TCR”. Medium-sized banks with relatively high TCRs performed poorly in stress tests; however, the probability of bank failure increases slightly with the size of the bank, while the TCR decreases. We claim that the focus on capital adequacy measures is not sufficient to achieve the goal of improving banks’ stability and reducing their restructuring costs. Our results are of special importance for medium-sized banks, as these banks are not regularly subjected to publicly available stress tests.

Measurement of Riverbank Erosion Rates of Pusu River Using Erosion Pins Method

Riverbank erosion is a major concern in all parts of the world due to its extensive impacts geomorphologically and economically. This study aims to quantify the rates of riverbank erosion of Pusu River using erosion pins method. Two sections of the river were selected namely site A and site B where site A is a straight section while site B is situated on the outside bend. 21 pins were installed at each site in a grid pattern. Measurement of erosion pins exposure were taken from February 2019 to April 2019. Field observation were made to identify the possible factors influencing the bank erosion. The average rates of bank erosion ranged between 0.05 cm/day to 0.21 cm/day at site A and 0.09 cm/day to 0.51 cm/day at site B. Bank failure occurred at site B towards the end of measurement period due to high flow after heavy rainfall event. Field observations suggest that rates of riverbank erosion were influenced by several factors such as the flow velocity and vegetative cover of the bank.