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This study aims to understand how quickly Japanese banks readjust their capital ratios (leverage, regulatory capital, tier-I capital and common equity) following an economic shock.

This study uses a two-step system GMM framework to test the study's hypotheses using the annual data of Japanese commercial and cooperative banks ranging from 2005 to 2020.

The findings show that banks adjust their leverage ratio faster than regulatory capital, tier-I capital and common equity ratios. In addition to that, the results reveal that the speed of capital adjustment is higher for commercial banks than for cooperative banks, suggesting higher economic costs and implications for commercial banks. Furthermore, it is worth noting that well-capitalised (under-capitalised) banks tend to prioritise the adjustments to common equity (leverage) before considering the adjustments to leverage (common equity). According to the results, high-liquid (low-liquid) banks alter their regulatory capital and tier-I capital ratios (leverage) more quickly (more slowly) than low-liquid (high-liquid) banks.

The findings suggest that when formulating and implementing new banking regulations, particularly in assessing and adjusting specific capital requirements under Pillar II of Basel III, management (including bankers, regulators and policymakers) should consider the heterogeneity observed in the rate of capital adjustment across various bank characteristics. Additionally, bank managers should also consider the speed of adjustment when determining optimal half-life and target capital structures.

To the author's knowledge, this study represents a pioneering investigation into the rate of adjustment of capital ratios (leverage, regulatory, tier-I and common equity) within Japan's banking sector. The study employs a comprehensive dataset encompassing both commercial and cooperative banks to facilitate this analysis. A notable contribution to the existing body of literature, this study offers a detailed analysis and emphasises the varying degrees of adjustment in capital ratios. The study also highlights the heterogeneous nature of the adjustment rate in these ratios by categorising the data into well-capitalised, under-capitalised, highly liquid and low-liquid banks.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Academic research has intensively analyzed the relationship between market concentration or market power and banking stability but provides ambiguous results, which are summarized under the concentration-stability/fragility view. We provide empirical evidence that the mixed results are due to the difficulty of identifying reliable variables to measure concentration and market power.

Using data from 3,943 banks operating in the European Union (EU)-15 between 2013 and 2020, we employ linear regression models on panel data. Banking market concentration is measured by the Herfindahl–Hirschman Index (HHI), and market power is estimated by the product-specific Lerner Indices for the loan and deposit market, respectively.

Our analysis reveals a significantly stability-decreasing impact of market concentration (HHI) and a significantly stability-increasing effect of market power (Lerner Indices). In addition, we provide evidence for a weak (or even absent) empirical relationship between the (non)structural measures, challenging the validity of the structure-conduct-performance (SCP) paradigm. Our baseline findings remain robust, especially when controlling for a likely reverse causality.

Our results suggest that the HHI may reflect other factors beyond market power that influence banking stability. Thus, banking supervisors and competition authorities should investigate market concentration and market power simultaneously while considering their joint impact on banking stability.

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

This paper aims to examine the relationship between board structure and risk-taking, exploring how this association is influenced by advanced technologies in the banking sector.

This study uses a panel sample of 22 Pakistani banks from 2011 to 2018. To test the authors’ hypothesis, the authors use regression analysis with two-way cluster robust standard errors. Further, the authors also check the robustness of the authors’ findings using alternate proxies of board structure and bank risk-taking behavior. To address endogeneity concerns, the authors use the two-stage least square technique.

In the era of the Fourth Industrial Revolution, Pakistani banks’ digitalization is modeled by the presence of Temenos-T24/Oracle as their core banking system (software providing end-to-end operational integration). Its interactional effect with corporate governance is evaluated to implicate informed risk-taking by the board as a result of improved information access and analysis. The authors find that board size has a positive association with risk-taking, and the use of modern technology reshapes this association in the banking sector.

The contribution of this paper is twofold. First, the impact of board structure on bank risk-taking has not been extensively researched in Pakistan – a highly volatile and unpredictable economy. Second, the evaluation of the role of technology on bank risk is being researched for the very first time – a uniqueness of this paper.

This study examines the impact of competition and concentration on bank income smoothing in Central and Eastern European (CEE) countries.

The two-step system GMM method was used to analyse the impact of competition and concentration on bank income smoothing in 17 CEEs from 2004 to 2015.

Loan loss provisions (LLPs) are negatively related to bank competition and concentration. The authors find no evidence for income smoothing using LLPs in a high-competition or high-concentration environment.

A limitation of the study is that the analysis was restricted to commercial banks. The authors did not examine investment banks or microfinance banks in this study. Also, not having access to databases does not allow them to include recent years in the study.

CEE commercial banks will likely keep fewer provisions or engage in under-provisioning when they face intense competition, and this can expose them to credit risk, which may threaten their stability.

This study is the first to investigate the effect of concentration and competition on income smoothing among CEE banks.

This paper aims to investigate the role of corporate governance on the bank profitability of Indian banks vis-à-vis South Asian Association for Regional Cooperation (SAARC) nations.

For the Corporate Governance Index, the authors examined board accountability, transparency and disclosure and audit committee, while Tobin’s Q, return on equity and return on assets are used to measure the bank’s profitability. The study used a two-stage analysis based on balanced panel data for robust findings. Sample of this study consists of 60 commercial banks from India and 60 banks from SAARC nations for the period of 2009–2021. This study used panel regression and a generalized method of moment approach using the CAMELS framework on banking industry-specific variables to determine their respective impacts.

The findings of this study suggest that board accountability is positive and significantly affects the profitability of banks as indicated by return on assets, return on equity and Tobin’s Q. In contrast, the audit committee has a positive and insignificant impact on return on assets, return on equity and Tobin’s Q, while transparency and disclosure have a negative and significant impact on these metrics. Furthermore, the country dummy result shows a significant positive impact on all the bank performance parameters, implying that Indian banks have the highest degree of convergence with corporate governance as compared to other SAARC nations.

This study provides insight to the regulators, policymakers and financial institutions to evaluate the role of corporate governance in emerging economies. However, the findings of the study should be interpreted with caution, as the results are sensitive to the disparity between India and other SAARC nations' government policies, climatic circumstances and cultural or religious traditions.

To the best of the authors’ knowledge, this is the first attempt to gauge the performance of Indian banks vis-à-vis SAARC nations using the CAMELS framework approach. Further, findings of this study suggest some novel evidence tying corporate governance quality with the profitability of banks among SAARC nations.

Agricultural banks likely respond differently to economic downturns compared to nonagricultural banks. Limited previous research has examined the performance of agricultural banks under economic crisis and in the presence of banking regulations. This study aims to explore agricultural banks' responses to economic and regulation shocks relative to nonagricultural banks.

This study uses bank-quarter level data from 2002 to 2022 for virtually all commercial banks in the U.S. In this research, the Z-score measures the bank’s default risk, the return on assets measures bank profitability and changes in amount of farm loans indicate the wider impact on the agricultural sector. Effects of the financial crisis, Basel III reforms to banking regulation and the coronavirus (COVID-19) pandemic on these banking measures are assessed using distinct empirical frameworks. The empirical estimations use various subsamples based on bank types, bank sizes and time periods.

Economic downturns are associated with fluctuations in returns and the risk of default of commercial banks. Agricultural banks appeared to be more resilient to economic downturns than nonagricultural banks. However, Basel III regulated agricultural banks were more likely to fail amidst the pandemic-related economic shocks than the regulated non-agricultural banks.

This study examines the resiliency of agricultural banks during economic downturns and under postfinancial crisis regulation. This is one of the first empirical works to analyze the effectiveness of Basel III regulation across bank types and sizes considering the COVID-19 pandemic. The key finding suggests that banking regulation should consider not only size heterogeneity but also the heterogeneity in lending portfolios.

This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the level of customer deposits on a sample of Tunisian commercial banks.

Training and testing datasets are developed to evaluate the level of customer deposits of 15 Tunisian commercial banks over the 2002–2021 period. This study uses two predictive modeling techniques: the MLR using a FE model and ANN. In addition, it uses the mean absolute error (MAE), R-squared and mean square error (MSE) as performance metrics.

The results prove that both methods have a high ability in predicting customer deposits of 15 Tunisian banks. However, the ANN method has a slightly higher performance compared to the MLR method by considering the MAE, R-squared and MSE.

The findings of this paper will be very significant for banks to use additional management support to forecast the level of their customers' deposits. It will be also beneficial for investors to have knowledge about the capacity of banks to attract deposits.

This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables.