Bank Credit Ratings Research Articles

Structural shifts in bank credit ratings

We investigate the time variation in credit rating standards awarded to financial institutions of commercial bank credit ratings awarded by the three principal CRAs from 1990 to 2015 in a world-wide context by testing for well-defined structural shifts. We focus on the part of the ratings that cannot be accounted using publicly available information. We test whether major financial events are conditioning, ex-post such changes Distinctively in this paper’s timespan our analysis covers four periods: (i) before and (ii) after the 2001–2 corporate collapses, followed by (iii) before the global financial crisis and (iv) after the global financial crisis. We find substantial differences in the assignment of bank credit ratings among the three major agencies, Moody’s, Fitch, and S&P. Agencies differ both in terms of re-adjustment of ratings but also on the speed of response to the evens. All three agencies tightened ratings during the 2008 crisis and kept reducing them in its aftermath.

Traditional Banking Evolution in China Amidst the Fintech Revolution

This paper aims to investigate how the evolution of fintech could impact the traditional banking industry in China by reviewing the existing research. It provides a brief introduction to Chinese banks, outlining their structures, and functions against the background of the development in the Chinese financial market. The paper also discusses how fintech could potentially interact with Chinese banks and the possible changes in bank financing systems, clearing systems, and credit rating systems in light of the fintech revolution. The final section of the report provides a summary of the accomplishments of fintech in the banking industry. It also explains the reasons behind the fintech revolution in the banking sector and suggests practical solutions for traditional Chinese banks to integrate fintech into their regular operations. This integration can help improve the efficiency of financing, clearing, and credit rating systems by utilizing fintech techniques already in use such as P2P lending, blockchain, and artificial intelligence (AI).

Corporate Governance and Credit Rating of Islamic Banks: Moderating Role of Shariah Governance Attributes

Shariah governance is the mechanism to monitor and implicate shariah compliance in Islamic banks. The study’s goal of exploring the Islamic bank’s governance attributes and credit scores or rating relationship in the presence of shariah board attributes as moderators. The study collected time-variant data from 22 Asian banks (286 observations) from 2006 to 2018. Applied descriptive statistics, correlations, Likelihood Ratio (LR) test and the Ordered Logistic Regression model, a suitable technique for the ordinal dependent variable. The study findings provide evidence of shariah governance’s moderating role in the relationship between corporate governance attributes and credit rating. Moreover, shariah board characteristics strengthen the association between the corporate board and credit worthiness nexus. This research recommends that credit score evaluating agencies consider the shariah governance characteristics in evaluating Ib’s credit rating. The shariah governance attributes as part of credit rating can be an appropriate method for investors to measure the shariah compliance level of Ibs. Accordingly, Ibs can gain the confidence of investors or sukuk investors by improving shariah compliance and can access competitive fund sources. The study’s uniqueness is in determining the impact of shariah governance attributes as moderators on the board-rating nexus. This study suggested that credit rating agencies revise or amend their assessment procedures for Ibs. Abundant literature is available from the owner’s point of view. Nonetheless, this research explores governance and shariah governance attributes concerning Sukuk holders.

The impact of sentiment consistency on bank risk: A dual perspective from bank managers and credit rating experts

The article examines the predictive role of the consistency of tone between credit rating experts and bank managers on bank bankruptcy risk and systemic risk. Firstly, the article collected 716 reports, which are the intersection of credit rating reports and bank annual reports of all US commercial banks from 2006 to 2022. Secondly, the FINBERT model was fine-tuned to calculate the tone scores of all reports and the similarity of tones between parties. Empirical analysis revealed that the more positive the tone of credit rating reports, the lower the bankruptcy risk and systemic risk of banks. Furthermore, integrating the perspective of bank managers, the article used a moderation effect experiment to further explore the moderating effect of the tone of bank annual reports and the consistency of tone between parties on the tone of credit rating reports regarding bank risk. The empirical results indicate that the tone of bank annual reports can effectively moderate the predictive effect of credit rating reports on bank risk. Additionally, the consistency of tone index has a significant positive moderating effect on the impact of the tone of credit rating reports on bank risk. This study provides important insights into the exploration of financial text tones on bank bankruptcy risk and systemic risk, suggesting the use of multidimensional text tones for bank risk measurement. It also demonstrates for the first time the importance of consistency of financial text tones in different dimensions in bank risk warning.

A study of the factors Affecting the Profitability of Zambian Commercial Banks. A Case of Zanaco

All businesses regardless of the nature require profitability for them to progress. Therefore, this thesis is aimed at assessing Zanaco’s profitability using ratio analysis, through three major objectives (i) To ascertain Zambia National Commercial Bank’s ability to generate return to its shareholders during the period 2016-2021. (ii) To determine Zambia National Commercial Bank’s ability to convert sales into profit during the period 2016-2021. (iii) To identify internal factors influencing Zambia National Commercial Bank’s ability to generate profit and returns to its shareholders during the period 2016-2021. The study used both primary and secondary information. Primary information was collected through structured questionnaires. Data analysis was done through SPSS V26 for primary data and excel for secondary data for the period 2016 – 2021, the response rate of primary data collection which was physically administered to Zanaco employees was 35 respondents indicating a 65%. Secondary data was collected through the analysis of Zanaco’s financial statements for the period 2016-2021. The findings review that the profitability ratios show that the bank has a stable ROA, and ROE, but low EPS and ROCE. The Low NPM below 30% for the years under review thus implying that Zanaco is minimally profitable, because of Zanaco’s high Expense operational ratios EOR. Zanaco bank is liquid as it is able to convert its assets to cash easily, but does not have enough cash flow. Additionally, the bank is affected, by inflation and macro-economic factors, however due to the bank size, capital, asset quality, credit rating and effective cost management strategies the bank is able to withstand turbulent economic impacts.

АНАЛІЗ ІНФОРМАЦІЙНИХ ПЕРЕДУМОВ ОБРАННЯ БАНКІВСЬКИХ ПОСЛУГ СПОЖИВАЧАМИ В УКРАЇНІ

The article examines the role of consumer credit in the modern economy and its impact on meeting the needs of the population and promoting socio-economic development. Consumer credit is an important financial tool that allows individuals to access a wide range of goods and services, including longterm assets such as real estate, automobiles, education, and healthcare. The paper analyzes the conditions of consumer lending, including repayment terms, interest rates, and borrower requirements. Alongside consumer loans, microcredit is rapidly developing in the financial market, focusing on shortterm loans with higher interest rates and less stringent borrower requirements. The study compares consumer loans and microcredits, highlighting their similarities and differences to provide a better understanding of these financial products. Particular attention is paid to the statistics on the use of microcredits in Ukraine in 2023, which shows a significant increase in the number of loans issued. During this period, more than 8 million microloans were issued, amounting to a total of 40.62 billion UAH, with the average loan amount decreasing to 4564 UAH. The article also presents an analysis of the advertising campaigns for consumer loans, the network infrastructure of banks, and annual interest rates in various Ukrainian banks, such as Oschadbank, PrivatBank, Ukrsibbank, OTP Bank, and Credit Agricole. A separate section is dedicated to the credit ratings of banks according to Fitch Ratings’ international scale, demonstrating the reliability of financial institutions. Additionally, the article explores the credit risks of banks and the quality of their loan portfolios, which are crucial for understanding the financial stability of banks and their ability to serve their clients. The analysis revealed that the highest credit risks are concentrated among clients with critical financial conditions. The conclusions emphasize the importance of maintaining customer loyalty to banks by improving credit products and enhancing service quality, which will contribute to the further development of the banking sector and the growth of lending volumes.

HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment.

As a prominent application of deep neural networks in financial literature, bank credit ratings play a pivotal role in safeguarding global economic stability and preventing crises. In the contemporary financial system, interconnectivity among banks has reached unprecedented levels. However, many existing credit risk models continue to assess each bank independently, resulting in inevitable suboptimal performance. Thus, developing advanced neural networks to model intricate temporal dynamics and interconnected relationships in the banking system is essential for an effective credit rating and risk assessment learning system. To this end, we propose a novel hierarchical fusion transformer for interbank credit rating and risk assessment (HFTCRNet), which includes the long-term temporal transformer (LT 3 ) module, short-term cross-graph transformer (STCGT) module, attentive risk contagion transformer (ARCT) module, and hierarchical fusion transformer (HFT) module to capture the long-term growth trajectories of banks, the short-term interbank network variance, the potential propagation of risks within interbank network, and integrate these information hierarchically. We further develop an interbank credit rating dataset, encompassing quarterly financial data, interbank lending networks, and key indicators such as credit ratings and systemic risk (SRISK) for 4548 banks from 2016Q1 to 2023Q1. Notably, we also adapt the minimum density algorithm to stabilize the interbank loan network over time, aiding in the analysis of long-term and short-term network effects. Our learning system uses semi-supervised learning to handle labels of varying sparsity, integrating credit ratings and SRISK for a comprehensive assessment of individual bank creditworthiness and systemic interbank risk. Extensive experimental results on our interbank dataset show that HFTCRNet not only outperforms all the baselines in terms of credit rating accuracy but also can evaluate the systemic risk within the interbank network. Code will be available at: https://github.com/AI4Risk/HFTCRNet.

The effect of corporate social responsibility on European bank credit ratings

The effect of corporate social responsibility on European bank credit ratings

Too-Big-to-Fail Banking in Europe: An Enduring Challenge

ABSTRACT Is banking in the European Union still too-big-to-fail (TBTF)? We address this question by providing a critical overview of post-crisis banking regulation and examining whether financial markets continue to expect European governments to bailout TBTF banks. To the latter end we use a novel set of primary data, gathered from fieldwork in Europe, analyse credit ratings of TBTF banks, and compare them with the ratings of other European banks. Our results suggest that the expectation of government support for European banks is still present. Most notably, TBTF banks command a long-term credit rating about three notches higher than what would be the case in the absence of the expectation of government support. Other European banks enjoy significant rating uplifts too, albeit smaller in size.

Stress testing programs and credit risk opacity of banks: USA vs Europe

Regulators strengthened banking supervision in the aftermath of the Great Financial Crisis by stress testing banks intending to increase the amount of information available about the risks they face, improving their transparency and restoring market confidence. This study examines whether the results of stress tests conducted between 2009 and 2019 in the US and the EU have reduced the opacity of information about banks' credit risk. We study changes in banking sector opacity around the disclosure of stress test results in a panel data framework. We measure opacity by discrepancies in bank credit ratings issued by different agencies. The findings indicate a lower opacity level after disclosing the US test results. The most significant reduction occurs for systemic banks with higher leverage that fail the test. The European testing programme has specific disclosure features that could justify that the effect of disclosure of stress test results is more attenuated for EU banks. Some indirect evidence suggests that differences in stress test programmes and banking sector structure between the two regions may explain the result.

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

The rapid development of Chinese online loan platforms (OLPs), as well as their risks, has attracted widespread attention, increasing the demand for a complete credit rating mechanism. The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators, including platform transaction volume and average expected rate of return. We also consider two qualitative indicators of online loan background, namely platform background and guarantee mode, that reflect Chinese characteristics. Subsequently, a factor analysis was conducted to reduce the 14 indicators’ dimensions. The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor, fund dispersion factor, security factor, and profitability factor. Finally, a K-means clustering algorithm was employed to cluster the factor scores of each OLP, thereby obtaining credit rating results. The empirical results indicate that the proposed machine learning–based credit rating method effectively provides early warnings of problem platforms, yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China, namely, Wangdaitianyan and Wangdaizhijia.

Credit Scoring Model for Farmers using Random Forest

One of the problems faced by farmers in Indonesia is capital. Based on Indonesian Central Statistics Agency survey results, the number of farmers who borrow capital from formal institutions such as banks is still small. This is because the process of applying for loans at banks is lengthy, farmers are considered high-risk and unbankable, and the rating of the agricultural sector is unattractive to banks. This study aims to determine the attributes and design a model of agricultural credit assessment. This study uses secondary data related to bank credit ratings and land productivity from banks in the Telagasari sub-district in 2018–2020 and Cipayung sub-district in 2020. Data were analyzed using random forests. The research process includes four stages: data collection, data pre-processing, model building, and model analysis and evaluation. This study produced five important variables that are relevant to farmers: planting costs, sales, land productivity, total production, and land area. The model built produces the most optimal accuracy of 83% with an AUC score of 81%. Based on the AUC performance classification, it can be concluded that the model that has been made is good at predicting the credit status of farmers because the AUC value is included in the good classification predicate.

Assessment of the activities of Ukrainian banks by rating agencies under the conditions of marital state

Introduction. The market of rating services is gradually developing both at the international and national levels. Over the years of operation of rating agencies, a significant number of various ratings have been developed and used. In particular, typical ratings are credit ratings of the issuer and debt obligations. One of the rating objects, along with countries, municipalities, large industrial enterprises, are banking institutions. If at the beginning of the activity of rating agencies in Ukraine, mainly banks with foreign capital and large banks had a rating, then in recent years all banks received a rating, which is one of the measures of their financial stability and reliability. At the same time, in recent years, in the conditions of the corona crisis, and currently in the conditions of martial law, the issues of expediency, objectivity and the need for rating assessment, including banks, have been actively raised, since there are significant differences in the assigned ratings on a national and international scale, and quite often, banks that received sufficiently high investment grade ratings from national rating agencies later became bankrupt. Purpose. Study of the main approaches and activities regarding the rating assessment of banks' activities by international and national rating agencies in the conditions of martial law. Methodology. On the basis of comparative and statistical analysis, a study of the rating assessment of the activity of banking institutions of Ukraine was carried out, the decisive factors influencing bank credit ratings and deposit reliability ratings were analyzed, the relationship between the sovereign rating of the country and the credit ratings of banks assigned by international rating agencies was determined. Results. The necessity of determining the ratings of banks, which act as an element of maintaining the reputation and image of the bank and provide an opportunity to enter international markets, attract investments, and also increase the level of awareness of clients about the financial condition and reliability of banks, has been proven. Emphasis is placed on the need to improve the legislative regulation of rating and unification of the methodology and scale of bank deposit reliability ratings.

The effect of corporate social responsibility on European bank credit ratings

The effect of corporate social responsibility on European bank credit ratings

Estimating the Risk of Financial Distress Using a Multi-Layered Governance Criterion: Insights from Middle Eastern and North African Banks

In this study, we explored the association of bank-level governance and state-level governance with the likelihood of banks’ financial distress in developing economies. Using a panel data sample of 954 bank-year observations of 106 conventional banks across 14 Middle Eastern and North African (MENA) countries from 2010 to 2018, we found that bank governance arrangements seemed to be negatively attributed to the probability of financial distress. We also found that the relationship of political stability with financial distress prospects is—contrary to our expectation—insignificant, whereas government effectiveness negatively influences the likelihood of financial distress. Our empirical evidence offers practical implications for bank managers, regulators, and credit rating agencies, and suggests several future research avenues that can build on our findings.

Application of regression function model based on panel data in financial risk management of bank resource allocation

Abstract This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of lender credit rating in the internal rating system of commercial banks, and analyzes the influence of a large number of subjective factors contained in the bank’s credit rating system on the rating effect. Influence. A sort-response panel regression function model with random effects (including random intercept terms and random coefficients) is proposed, and the rationality of the existing bank credit rating system is tested and analyzed. The experimental results show that the model’s prediction accuracy rate is 48.27%, and the percentage of prediction level error within two levels reaches 86.8%. The application of this model can find the redundant indicators of the existing system, which are highly fitted with the actual situation and can achieve quite ideal forecasting results, and provide a forecasting reference for the weight setting, which provides a basis for the revision of the bank’s credit rating system.

Bank risk aggregation based on dual perspectives of bank managers and credit rating agencies

In this paper, the risk disclosure text information from the dual perspectives of bank managers and credit rating agencies is innovatively incorporated into the bank risk aggregation, which to some extent avoids the deviation of bank risk assessment caused by the risk perception from a single perspective, and measures the overall bank risk more comprehensively. In the empirical analysis part, this paper extracted 342 risk profit and loss data, 511 text risk information in 10-K forms and 356 available bank rating reports from 134 listed commercial banks in the United States. The Sent-LDA model was used to identify and compare risk factors from different perspectives. In addition, by constructing a total risk-adjusted index, this paper studies the effectiveness of textual risk disclosure from the dual perspectives of bank management and credit rating agencies in the overall risk measurement of commercial banks. The empirical results show that the text contains the incremental information of risk assessment, and the inclusion of double-perspective text risk information into the risk aggregation will amplify or reduce the overall risk of banks calculated only based on financial numerical data. In practice, it will be more effective to measure the overall risk of banks by considering the textual risk disclosure from dual perspectives.

Retailer's Optimal Ordering and Financing Policies under Two -Level Credit Ratings

Different from bank credit rating, the dominant supplier usually ranks retailers to different credit ratings according to their transaction history, such as their order quantities. In this paper, the retailer is ranked either at low credit level or high credit level according to the retailer's historical order quantities. The retailer with low credit rating can only operates from hand to mouth whereas the retailer with high credit rating can apply for partial trade credit as well as a bank loan through the credit guarantee provided by supplier. We analytically derive the retailer's optimal ordering and financing policies. The trade credit and bank financing model is shown to be better off than self-financing model in terms of the retailer's optimal ordering. Hence it is worth to implement credit rating differentiation by the supplier. Observing from the retailer's ordering behavior, the upstream supplier can gain more information on its downstream's capital position through differentiating the credit level by ordering.

Employees’ Perception towards the Merger and Acquisition of Bank and Financial Institutions of Nepal

Merger and acquisition an outcome of consolidating two or more than two companies in one institution. Merger and acquisition both are the tools of consolidating the companies. The study was conducted with the objective to identify the employees’ perception towards the merger and acquisition of bank and financial institutions of Nepal. The study was conducted among the 385 respondents of financial institutions. The respondents were randomly selected for the questionnaire survey. The study was based on the quantitative design so statistical analysis was done to compare the perception between category A and B bank. The result shows that there was significant association between the employees of category A and B bank on their perception towards the contribution of merger and acquisition. majority of the respondents had positive perception towards the effectiveness of merger and acquisition to improve the service quality, organizational performance, exchange organizational culture, maximizing the profit and revenue, increase the credit rating of banks, introduce the new service and facilities, implement the new innovative ideas. So, it has encouraged financial institutions to go in process of merger and acquisition for the better performance and sustainability of institutions.

An Analysis of Bank Financial Strength Ratings and Credit Rating Data

In this study, data from two credit rating agencies are analyzed to consider how different Bank Financial Strength Ratings and Credit Ratings from two rating agencies compare. To my knowledge, prior research has not analyzed Bank Financial Strength Ratings from different rating agencies, nor has it compared Bank Financial Strength Ratings to general credit ratings. These facts make this research unique. Univariate analyses are utilized to show relationships in the ratings data, along with parametric and non-parametric tests to make statistical inferences about the ratings data. There are five findings. First, ratings from different rating agencies are highly correlated. Second, different types of ratings from the same rating agency are highly correlated. Third, bank financial strength ratings are more conservative than credit ratings. Fourth, bank financial strength ratings declined in rating more quickly at the start of the financial crisis. Fifth, bank financial strength ratings from the Kroll Bond Rating Agency were more conservative than ratings from Moody’s Investors Service. The research findings and results are important for investors who consider ratings agency data to determine the risk of banking institutions. The results are also important to businesses that rely on bank credit rating data and policy makers who regulate banking institutions.