We update the widely used banking crises database by Laeven and Valencia (2008, 2010) with new information on recent and ongoing crises, including updated information on policy responses and outcomes (i.e. fiscal costs, output losses, and increases in public debt). We also update our dating of sovereign debt and currency crises. The database includes all systemic banking, currency, and sovereign debt crises during the period 1970-2011. The data show some striking differences in policy responses between advanced and emerging economies as well as many similarities between past and ongoing crises.
Nasif Ozkan, Sinan Cakan, Assoc. Dr. Murad Kayacan
The purpose of this study is to analyze the relationship between the intellectual capital performance and financial performance of 44 banks operating in Turkey between 2005 and 2014. The intellectual capital performance of banks is measured through the value added intellectual coefficient (VAIC) methodology. The intellectual capital performance of the Turkish banking sector is generally affected by human capital efficiency (HCE). In terms of bank types, development and investment banks have the highest average VAIC. When VAIC is divided into its components, it can be observed that capital employed efficiency (CEE) and human capital efficiency (HCE) positively affect the financial performance of banks. However, CEE has more influence on the financial performance of banks compared to HCE. Therefore, banks operating in the Turkish banking sector should use their financial and physical capitals if they wish to reach a higher profitability level.
M. Farah, Muhammad Junaid Shahid Hasni, Abbasi Abbas
Purpose The purpose of this paper is to study the important factors which help explain consumer intention and use behavior in mobile banking (m-banking) adoption. All constructs of the unified theory of acceptance and use of technology 2 are studied. Non-monetary value is studied through perceived value. Trust and perceived risk are also included to predict intention. Design/methodology/approach A questionnaire was utilized to evaluate customer responses on a five-point Likert scale. A convenience sampling technique was used to collect data from a sample of 490 respondents in Pakistan. The data were analyzed using AMOS and SPSS for Cronbach’s α, CR, CMV, AVE, Harmon’s single factor test, correlation and structural equation modeling. Findings The results of the study show that most of the predictors of intention, including perceived value, performance expectancy, habit, social influence, effort expectancy, hedonic motivation (except for facilitating condition), perceived risk and trust, are significant. All predictors of usage behavior are significant. Research limitations/implications A cross-sectional study was conducted due to time constraints. Practical implications Bank managers must focus on improving customers’ intentions to use m-banking as well as on providing facilitating conditions to increase its actual use. To boost mobile banking, banks’ management must consider the customers’ habits while designing their m-banking products. Originality/value The findings of this paper are not only interesting in terms of boosting m-banking diffusion rate, but also in terms of financial inclusion of the vast majority of mobile users. Further the impact of intention, facilitating condition and habit were checked on actual use behavior since people tend not always to act upon their intentions.
This study investigates the determinants of profitability among Indian commercial banks from 2005 to 2024, with a specific focus on the novel role of dynamic growth efficiency (GE), a concept capturing a bank's ability to transform input growth into output growth, alongside conventional static efficiency measures such as level efficiency (LE) and cost-to-income ratio (CIR). As the first to operationalize GE in the Indian context, the study employs data envelopment analysis (DEA) on a panel dataset of 50 commercial banks (12 public, 17 private, and 21 foreign). Results from a system GMM estimator reveal GE to be a consistently significant driver of profitability, outperforming both LE and CIR across various market power indicators and model specifications. A key methodological advance supporting this analysis is the inclusion of technology expenditures (which account for 29% of operating and 13% of total expenses in 2024) as a fundamental input, correcting a major misspecification in prior literature. We demonstrate that omitting this crucial input artificially inflates market power and deflates efficiency estimates. The positive impact of GE is more pronounced for public-sector and new private banks, underscoring divergent strategic drivers across ownership structures and highlighting the paramount importance of fostering dynamic capabilities for sustaining profitability in a rapidly evolving banking landscape.
Abstract Government of India aims at making the Indian Banks internationally competitive. In the wake of intense competition and changing global and national business environment, the efficiency issues have emerged as an important pillar of success in the Indian banking sector. Therefore, it is an essential task to comprehend the efficiency levels of the overall Indian banking sector and also across different ownership structures (viz. Public, Private and Foreign). The present study endeavors to carry out an assessment of intra-sector efficiency in the Indian banking sector based on a cross-sectional data of 66 banks for the year 2015-16. The authors employ directional distance function based meta-frontier DEA approach and the results reveal that the Indian banking sector is 73.44% efficient. It also confirms the existence of different production functions across different ownership structures of the industry. Among the different ownership structures, the group frontier of foreign banks coincides with the meta-frontier. The group frontier of private sector banks is the second closest to the meta-frontier and public sector banks are found to be the laggards in the overall industry. The study gains special significance in the backdrop of the recommendations floated by the Reserve Bank of India and Ministry of Finance (Government of India) to consolidate the public sector banks in order to retain fewer but healthier banks. The finding of the study fully support these recommendations and affirms that consolidation in the industry will bring positive synergies and will lead to the enhancement of efficiency levels in the industry.
We use causal inference analysis with Bayesian structural time series modelling to identify the changes in the direction and amplitude of Ukrainian and Polish consumer and professional inflation expectations after the outbreak of war in Ukraine. The study spans January 2018–March 2024. The pre-invasion period is considered a training period. The most interesting finding of this study is about the behaviour of Ukrainian consumer who did not change their expectations during the war. As a reaction to the intervention, we reported an increase in expectations for Polish economic agents and Ukrainian professionals. This study provides the first empirical evidence of the evolution of expectations during the war in Ukraine and a neighbouring country.
Reza Pahlepi, Idhia Sriliana, Winalia Agwil
et al.
This study investigates the application of a truncated spline nonparametric regression model for biresponse analysis of longitudinal data, focusing on modeling monthly stock prices specifically opening and closing prices of three private banks in Indonesia: Bank Mayapada, Bank Mega, and Bank Sinar Mas. The data used in this research are secondary data sourced from the website Id.Investing.com and monthly financial statement publications of three private banks in Indonesia. Longitudinal data, combining cross-sectional and time-series dimensions, are utilized to capture trends and patterns not detectable in traditional cross-sectional analysis. The truncated spline method is selected for its adaptability to nonlinear relationships and abrupt data behavior changes. The model incorporates three predictor variables traded stock volume, total assets, and total liabilities and evaluates their influence on stock prices. Assumptions of longitudinal data are validated using the Ljung-Box autocorrelation test, Bartlett’s sphericity test, and Pearson correlation. Results confirm significant within-subject correlations, independence between subjects, and strong interdependence between response variables. The optimal configuration is determined using Generalized Cross Validation (GCV), with up to three knots considered for segmentation. Weighted Least Squares (WLS) is employed for parameter estimation, accounting for within-subject correlations. Model evaluation based on Mean Absolute Percentage Error (MAPE) indicates high accuracy, with all MAPE values below 5%. The highest MAPE value is 4.41% for the closing price of Bank Mayapada, while the lowest is 2.65% for the opening price of the same bank. The segmentation analysis reveals that traded stock volume and total assets positively influence stock prices, while total liabilities exhibit a predominantly negative impact. The model is limited to internal financial indicators and does not include external macroeconomic factors such as interest rates or inflation. This study is the first to apply a biresponse truncated spline nonparametric regression approach to analyze stock prices of private banks in Indonesia by simultaneously modeling both opening and closing prices, providing a flexible and effective method for capturing complex patterns in longitudinal financial data.
Georgeta Vintilă, Vasilica Izabela Fometescu, Alexandra Ioana Vintilă
<p>Public administration, as the implementing body of public policies, plays a fundamental role in maintaining financial stability and fostering sustainable private sector development. Our study investigates the extent to which the quality of public governance – a direct reflection of administrative capacity – influences corporate taxation outcomes in the energy sector, deeply affected by financial crises and armed conflicts all over the world. Administrative capacity plays an important role in supporting competition, simplifying bureaucracy, and ensuring transparency in public spending. Strengthening the role of administrative capacity, particularly in the energy sector, is a key factor in public sector reform.</p><p>The empirical study analyzes the non-financial companies from the EU-27 countries, over the period 2004-2023, and integrates as main independent variables the public governance indicators. Our findings indicate that stronger governance, particularly where public administration is effective and transparent, contributes to the increase in corporate income tax and, implicitly, to increasing revenue collection. From a public policy perspective, the quantitative study results underscore the importance of strengthening administrative institutions and public governance mechanisms to improve taxpayers’ voluntary compliance.</p>
Political institutions and public administration (General)
This paper examines the relationship between trade and financial openness, as well as the operational efficiency and growth of Turkish banks, from 2010 to 2023. Utilizing CAMELG-DEA and dynamic panel data analysis, the study finds that increased trade openness significantly enhances banking efficiency, primarily due to heightened demand for banking services related to international trade. Financial openness further boosts growth by facilitating capital flows, expanding banks' credit portfolios, and increasing fee income from cross-border transactions. However, poverty levels have a negative impact on bank performance, reducing financial intermediation and innovation opportunities. The results underscore the crucial role of trade and financial openness in fostering banking sector growth in developing economies.
Jeppe H. Mikkelsen, Thomas T. Enevoldsen, Bugge T. Jensen
et al.
Vessels navigating in confined waters are subject to banking effects, which are hydrodynamic forces and moments arising from pressure differentials between the vessel sides, significantly affecting manoeuvrability and safety. Existing numerical approaches such as computational fluid dynamics (CFD) can accurately capture these effects but are computationally expensive and unsuitable for real-time control or estimation. This paper presents a closed-form, first-principles model of banking effects. The model coefficients are identified using physics-informed regression on towing tank experiment data for a scaled container vessel. Validation through Shapley value analysis confirms the significance of the banking terms in reproducing the measured forces and moments. Lastly, the derived coefficients are shown to be non-dimensional, making the model applicable across different scales that preserve vessel geometry.
A novel network-based approach is introduced to analyze banking systems, focusing on two main themes: identifying influential nodes within global banking networks using Bank for International Settlements data and developing an algorithm to detect suspicious transactions for anti-money laundering. Leveraging the concept of adversarial networks, we examine Bank for International Settlements data to characterize low-key leaders and highly-exposed nodes in the context of financial contagion among countries. Low-key leaders are nodes with significant influence despite lower centrality, while highly-exposed nodes represent those most vulnerable to defaults. Separately, using anonymized transaction data from Rabobank, we design an anti-money laundering algorithm based on network partitioning via the Louvain method and cycle detection, identifying unreported transaction patterns indicative of potential money laundering. The findings provide insights into system-wide vulnerabilities and propose tools to address challenges in financial stability and regulatory compliance.
Purpose Providing high-quality e-banking services is considered a basic strategy for attracting and retaining customers with electronic-banking platforms. The purpose of this paper is to empirically investigate a comprehensive moderated mediated mechanism for enhancing customer loyalty toward e-banking platforms via e-banking service quality (EBSQ) practices. Reliability, website design, privacy and security and customer service and support are the dimensions of EBSQ. Design/methodology/approach Data were collected through structured questionnaires from a sample of 1,028 e-banking users in India. To test the hypotheses, a structural equation modeling approach was used. Findings The findings showed that of the EBSQ dimensions, reliability along with privacy and security enhanced customer loyalty to e-banking. The initial trust in e-banking mediates the effects of EBSQ dimensions on customer loyalty except for website design. The mediation effects of initial trust varied between high and low-involved consumers. Research limitations/implications This study was conducted with e-banking users in one country using cross-sectional data. Hence, the model should be replicated among e-banking users in other countries and with the longitudinal data. Practical implications Establishing a loyal customer base is an important goal for banks. This study demonstrates which specific EBSQ dimensions banks should emphasize to enhance consumers’ initial trust and loyalty toward e-banking services. Originality/value This study suggests a moderated mediated mechanism for enhancing customer loyalty to e-banking, which incorporates initial trust as a mediator and consumer involvement as a moderator. It applies cognitive-motivation-relational theory to link EBSQ dimensions with customer loyalty. Thus, this study enables a better understanding of this theory in the e-banking context.
Abstract As we can see in recent studies on mobile banking, there is an increasing number of papers addressing this new technology. Mobile banking contributes to the quality of life of people living in both developed countries, and also in emerging economies. In this context, we develop this paper in order to compare the determinants of mobile banking use between respondents from two countries with different levels of development: Brazil and the United States. Our theoretical model includes six variables as determinant factors of mobile banking use. In order to analyze path coefficients and test the six hypotheses, we employed a structural equation model. We also employed a quantitative test (multi-group analysis) to analyze the difference of path coefficients between the models of the two countries. The main results indicate similarities among the perceptions of the respondents that participated in the survey, but differences in coefficient magnitude.