Credit Rating Research Articles

Role transition, investment practice and risk Management of Peer-to-peer Lending Investors: Based on the perspective of investor learning

Online peer-to-peer (P2P) lending has emerged as a result of the integration of finance and technology. Within the P2P lending market, individual investors have the opportunity to make repeated investments and engage in two-way transactions involving loans and investments. This setting provides an ideal scenario for studying the impact of different learning types on investors' risk management. Drawing on transaction data from the online lending platform Renrendai.com, we empirically examine the influence of role transition and investment practice on investors' risk identification, risk preference, and investment performance. We introduce variables related to loans and investment portfolios to analyze the effects. Our findings reveal that investment practice has a greater effect than role transition in reducing the investment default of online lending investors. Additionally, role transition is associated with a decrease in the investment amount set by investors and a reduced reliance on borrowers' credit ratings. Historical investment experience also leads to a decrease in the investment amount, while experiences of past investment failures improve investors' reference to borrowers' credit ratings. Furthermore, we observe that borrowing experience and previous investment failures contribute to a lower probability and proportion of high-risk loans in the portfolio. We shed light on the “black box” of investor learning and expand the research methodology for understanding investor behavior characteristics in the Fintech era.

Estimating Risk of Debt Instruments Using the CreditMetrics<SUP>TM</SUP> Method: On the Example of JSCMB ‘Ipoteka-Bank’, Uzbekistan

In the article, credit risk assessment metric of a debt financial instrument, in particular a corporate Eurobond, is studied within the framework of the relevance of risk assessment in the financial sector due to the dangers and complexities of time. Joint-Stock Commercial Mortgage Bank ‘Ipoteka-Bank’, operating in the banking sector of the Republic of Uzbekistan, which is considered one of the important links of the financial sector of the Republic, was selected as the object of analysis. As input data for the analysis, statistical data of the bank provided in its official web-site’s open sources, in particular, data on Eurobonds issued and placed by the bank in London Stock Exchange, are used. At the same time, one of the advantages of the research is the described and disclosed in details and widely used global benchmarking data, published by Standard&Poor's rating agency, as part of the methodology requirement. According to the methodology, credit risk assessment analysis was performed following a strict and detailed calculation algorithm for debt instrument risk assessment. The calculation of credit risk assessment of one debt instrument, especially the Eurobond, according to this procedure followed with the authors’ discussion, conclusion and recommendation is also reflected in the article.

Factors of Credit Ratings for Transfer Pricing of Loans in European Conditions

Factors of Credit Ratings for Transfer Pricing of Loans in European Conditions

Informal Credits in India’s Agriculture Sector: Debt Incidence, Size and Distribution across Major Farm-size Groups

Until recently, studies on agricultural finance in India mostly focus on farmers’ access to formal credits, thereby largely sidelining their informal borrowings. This study makes an effort to present an analysis of farmers’ informal debts and their distribution across the major farm-size groups in India. The study uses unit-level data from the 77th round ‘Situation Assessment Survey (SAS)’ of agricultural households conducted by the National Statistical Office (NSO), Ministry of Statistics and Programme Implementation (MOSPI), Government of India (GOI), during January–December, 2019. Data were collected for the reference period of July 2018–June 2019. We use descriptive statistics and multivariate regression procedures for analysis. Results show that about one-half of the borrowers continue to depend on informal credit sources—partially or fully. Informal sources also account for about one-third of the total debt of the agricultural households. Informal debt incidence (IDI) and informal debt share (IDS) are found to be higher among the smallholders. Our findings are imperative of policy measures to improve the current state of the antecedents of formal credit access to benefit the otherwise high default risk and credit-rationed smallholders. Future studies may focus on formal credit rationing, informal credit terms and repayment burden of the agricultural households in India.

Bond market structure and volatility

AbstractWe apply variance ratio methodologies to examine market quality in the US corporate bond market. We find that the open‐to‐open to close‐to‐close return variance ratio is greater than one suggesting that the corporate bond market is less efficient during the opening hours than during the closing hours. We show that the higher variance ratio at the open is related to the market power of dealers at the open and the sources of power are from lower cost of inventory, lower asymmetric information, and more flexibility to intermediate a trade. Dealers appear to exert less market power for bonds with low volume and credit rating. The results are consistent with dealers behaving strategically to unload risky assets and take on safer assets.

Can information imprecision be valuable? The case of credit ratings

We develop a model in which credit ratings are endogenously coarse relative to the underlying default probabilities, and ratings precision is countercyclical. Ratings coarseness arises from the profit-maximizing behavior of rating agencies, and coarseness may maximize welfare even when greater ratings precision is costlessly available. Because the private outcome may differ from the socially desirable outcome, a social planner can improve welfare by putting a ceiling (floor) on the rating agency’s fee if the desired outcome is coarseness (precision). Strikingly, when information production is costless, ratings coarseness is socially optimal, but it does not arise in the laissez-faire equilibrium, thus inviting regulatory intervention.

The Impact of R&D Investments, Including AI, on Economic Growth and the Country’s Capacity to Improve Its Credit Rating

The Impact of R&D Investments, Including AI, on Economic Growth and the Country’s Capacity to Improve Its Credit Rating

Institutional Investors and the Fight Against Climate Change

ABSTRACTResearch Question/IssueThis article examines the role of institutional investors in the fight against climate change. We explain the institutional context, provide evidence highlighting institutional investors' bright and dark sides in this fight, and develop multiple ideas for future research.Research Findings/InsightsWe show that climate change has a significant impact on institutional investors. Simultaneously, we demonstrate that institutional investors can have a significant positive impact on fighting climate change, particularly if they actively engage with portfolio firms to reduce carbon emissions. For risk management reasons, this is in their own interest, and it is also in the interests of society.Theoretical/Academic ImplicationsWe highlight possible future research avenues on the link between institutional investors and climate change, emphasizing issues related to environmental, social, and governance (ESG) rating agencies, greenwashing, and the risk of a loss of trust in ESG products. Climate change constitutes one of the grand challenges of our time, and substantially more research on the role of finance is required.Practitioner/Policy ImplicationsClimate change imposes financial risks on institutional investors' portfolio firms, which must be actively addressed in the investment process. Nascent evidence indicates that markets have begun pricing these risks. Institutional investors can positively influence climate change by engaging portfolio firms on their emissions and simultaneously reducing climate transition risks.

Does the impact of credit rating downgrade on bond returns vary by region: Empirical evidence from China

Utilizing trading data from corporate and enterprise bonds that experienced credit rating downgrades between 2012 and 2022, this study employs event study methodology and regression analysis to examine the regional differences in the impact of credit rating downgrades on bond pricing and the mechanisms behind these effects. The study found that, during the window period when the bond credit rating is downgraded, there is a significant difference in the cumulative abnormal return of bonds between the eastern and non-eastern regions. Heterogeneity analysis indicates that bonds with higher credit ratings and those issued by non-state-owned enterprises are more significantly affected by credit rating downgrades. This study provides empirical evidence of regional disparities in the impact of credit rating downgrades, and proposes a theoretical mechanism whereby differences in the degree of marketization influence these effects through varying expectations of government implicit guarantees. The findings offer a theoretical foundation for optimizing decision-making by participants in the Chinese bond market.

Are ESG scores driven by financial information? Evidence from European banks

In recent years, investors' increasing focus on sustainable investments and the sustainability orientation of companies has led to parallel growth in the market for environmental, social and governance (ESG) performance and ESG rating agencies. However, even though ESG rating agencies have become very influential institutions, the literature has found that ESG performance ratings provided by different agencies often differ from each other. This causes consequences that should be considered, such as complex evaluation of companies' ESG performance and uncertainty in ESG investment decisions. Therefore, it is necessary to identify which determinants influence ESG performance. This study aims to identify the internal determinants of an ESG score using bank-specific balance sheet indicators such as capital and risk ratios. The analysis focuses on the European banking sector from 2018 to 2022. Banks mainly foster the transition to a more inclusive and sustainable economy. Moreover, after the recent financial crises, banks have increased their social responsibility practices, strengthening their credibility, trust and reputation. Generalised estimating equations with standard error robust to heteroscedasticity were used. The results reveal that the factors that most influence the ESG score provided by ESG rating agencies are bank size and liquidity risk exposure. The larger the size of the bank and the lower the exposure to liquidity risk, the higher the ESG score assigned.

Juxtaposing Gender Differentials in Credit Assessment of Farmers in Nigeria: A Hybridized Credit-Scoring Approach

Using data from 360 smallholder farmers in Southeast Nigeria, the study creates the architecture for a new farmer's hybrid credit rating system used in classifying farmers who applied for microfinance loans based on their creditworthiness. We discovered new evidence that the hybridized credit scoring algorithm demonstrated unprecedented concordance in assessing the financial viability of farmers along gender lines. The discriminant analysis, in particular, closely aligned with the credit score model, with 34.4% and 46.7% of male and female farmers grouped as creditworthy, reflecting the model's estimates of 33.3% and 45.5%, indicating gaps of 12.3% and 12.2%, respectively, to the advantage of the female farmers. Our findings further suggest that annual income, marital status, and farm size strongly influence the separation between creditworthy and non-creditworthy farmers. While age, loan term, and a history of defaults had a negative impact on discrimination, in light of the findings, we recommend a collaboration between authorities, financial institutions, and extension workers in offering tailored trainings to both male and female farmers, assisting them in meeting up-to-date credit prerequisites, adopting modified farming techniques, and improving their general preparedness to be accepted for loans in this changing credit evaluation landscape so as to bridge the disparity and promote financial inclusion for farmers irrespective of gender affiliations.

STATUS AND DEVELOPMENT TRAJECTORIES CONSUMER AND MORTGAGE LENDING IN THE REPUBLIC OF KAZAKHSTAN

The purpose of the article is to determine the optimal development trajectories based on the research of the mortgage and consumer lending segment in the Republic of Kazakhstan. The mechanism of management of the consumer and mortgage lending system in the system of financial relations between the population and the institution of banking structures; the level of optimal relationships between participants in this process and their regulation through the bank credit rate, investment and marketing activities of the country's banking institutions. The article uses methods of transformation of analytical data display, analysis and synthesis based on qualitative and quantitative approaches. The official statistics of the NBRK and the second-tier banks of the Republic of Kazakhstan were used as the initial information of the analysis. Based on the analysis of the interdependence of mortgage and consumer lending, the level of the base rate of the National Bank of the Republic of Kazakhstan and other factors, recommendations for the development of an optimal trajectory of long-term development are proposed. The problems and prospects of achieving the optimal level of mortgage and consumer lending in the Republic of Kazakhstan, taking into account the interests of both banking institutions and consumers of the country, are considered.

The effect of financial statement comparability on the gaps between credit ratings and bond implied ratings: Focusing on the moderating effect of Korean business groups

This study analyzes whether financial statement comparability (FSC) affects the gaps between credit ratings (CRs) and bond implied ratings (BIRs), and Korean business groups mitigate the relation between FSC and rating gaps. Korean business groups have the diversified corporate governance structure and complex circulating investments, which makes it difficult for outside stakeholders to compare accounting information between firms in the same industry. Therefore, we propose the hypothesis that business groups are likely to moderate the negative relation between FSC and rating gaps. We find evidence, consistent with the hypotheses, that rating gaps are lower when FSC is greater. It also documents that business groups mitigate the negative relation between FSC and rating gaps. To the best of our knowledge, this research is the first empirical study to test the relation between FSC and rating gaps and the moderating effect of Korean business groups.

Are investor‐paid credit ratings superior?

Are investor‐paid credit ratings superior?

Does firm culture influence corporate financing decisions? Evidence from debt maturity choice

This study establishes a relation between corporate culture and debt maturity choice. Specifically, superior corporate culture is associated with the choice of shorter-term debt, supporting the notion that superior culture reduces managerial agency problems resulting in managers being more receptive to external monitoring through the choice of shorter-term debt. The culture subcomponents of integrity, teamwork, and innovation are found to have a meaningful influence on the debt maturity structure choice. The relation between culture and debt maturity is more pronounced in firms with higher managerial stock ownership and those that are financially constrained, but is weakened in firms with a greater CEO sensitivity to stock prices. Additionally, firms with superior culture are shown to have higher long-term credit ratings. These findings contribute at the confluence of corporate culture and debt financing literatures. A battery of robustness tests, including addressing endogeneity concerns, validate the findings.

Evaluating the Gendered Credit Constraints and Uptake of an Insurance-Linked Credit Product Among Smallholder Farmers in Kenya

Using data from a multi-arm experiment among maize and bean smallholder farmers in Kenya, we evaluate the gender differences in credit rationing, and whether an insurance-bundled credit product, known as risk-contingent credit (RCC) could enhance credit access for both men and women. Using 3 years of household survey data, we find that female-headed households are significantly more risk-rationed (or demand-side rationed) than men. Post-intervention, we find that the impacts of RCC on credit uptake decisions are significantly larger than those of conventional credit. We also find that RCC impacts do not vary across the different gender categories.

A Weak MLMC Scheme for Lévy-Copula-Driven SDEs with Applications to the Pricing of Credit, Equity and Interest Rate Derivatives

This paper develops a novel weak multilevel Monte-Carlo (MLMC) approximation scheme for Lévy-driven Stochastic Differential Equations (SDEs). The scheme is based on the state space discretization (via a continuous-time Markov chain approximation [Mijatović, Aleksandar, Matija Vidmar, and Saul Jacka. 2012. “Markov Chain Approximations for Transition Densities of Lévy Processes.” Electronic Journal of Probability 19]) of the pure-jump component of the driving Lévy process and is particularly suited if the multidimensional driver is given by a Lévy copula. The multilevel version of the algorithm requires a new coupling of the approximate Lévy drivers in the consecutive levels of the scheme, which is defined via a coupling of the corresponding Poisson point processes. The multilevel scheme is weak in the sense that the bound on the level variances is based on the coupling alone without requiring strong convergence. Moreover, the coupling is natural for the proposed discretization of jumps and is easy to simulate. The approximation scheme and its multilevel analogous are applied to examples taken from mathematical finance, including the pricing of credit, equity and interest rate derivatives.

A new approach to K-nearest neighbors distance metrics on sovereign country credit rating

This study introduces feature importance K-nearest neighbors (FIKNN), an innovative adaptation of the K-nearest neighbors (KNN) algorithm tailored for classifying sovereign country credit ratings. The primary objective is to enhance KNN's predictive accuracy by integrating a feature importance mechanism derived from the random forest algorithm, which prioritizes significant features and reduces the impact of less relevant ones, refining the distance computation within KNN. Utilizing a comprehensive dataset of sovereign credit ratings, the performance of FIKNN was assessed against traditional KNN using various feature sets and bootstrap samples. The FIKNN model consistently outperformed the standard KNN by approximately 1% in classification accuracy, attributed to the weighted distance metric adjusting feature influence based on importance. Key findings indicate that FIKNN effectively manages datasets with varying feature relevance and demonstrates a positive correlation between feature diversity and model performance. Future research will explore other distance metrics and refine the feature importance weighting mechanism to broaden FIKNN's applicability in diverse predictive tasks.

Credit ratings: strategic issuer disclosure and optimal screening

Abstract We consider a model in which a security issuer can manipulate information observed by a credit rating agency (CRA). We show that stricter screening by the CRA can sometimes lead to increased manipulation by the issuer. Accounting for the issuer’s behavior pulls optimal CRA screening toward the extremes of laxness or stringency. Surprisingly, an improvement in prior asset quality can result in more rating errors. In a two-period version of the model, stricter screening can result in more short-run rating errors. Our results suggest complex interplay between issuer and CRA behavior, complicating the evaluation of CRA policy effectiveness.

Corporate bond default prediction using bilateral topic information of credit rating reports

The default of corporate bonds can result in large financial losses as well as irreparable harm to investors’ trust and the economy as a whole, which implies that the identification of corporate bond default must be done promptly and properly. Current studies mainly rely on accounting and/or macroeconomic data and use the credit rank (CR) to disclose the credit status of corporate bonds in the default prediction task. However, the textual data of credit rating reports (CRRs) contain richer and more comprehensive information and are neglected in related work. In this paper, we propose a novel framework that draws on the unstructured data in CRR to predict the default of corporate bonds. We extract the rating opinion sentences (categorized as positive and negative) from the collected CRR files and use latent Dirichlet allocation (LDA) models to mine topic information. The bilateral topic information of positive and negative opinions can reflect the anti-risk ability and potential risk of corporate bonds, respectively, based on which the constructed topic features are used for default prediction. Results on real-world Chinese corporate bonds dataset show that the bilateral topic information of CRR can significantly improve the predicting power of models (LR, SVM, KNN and MLP) under three performance metrics (AUC, KS and H-measure). By analyzing the ranking of topic features using SHAP value, the proposed framework can explain the factors that affect bond defaults, which can provide a basis for the decision-making of investment behavior.