Corporate Credit Research Articles

Inside and Outside Information

ABSTRACTWe study an economy with financial frictions in which a regulator designs a test that reveals outside information about a firm's quality to investors. The firm can also disclose verifiable inside information about its quality. We show that the regulator optimally aims for “public speech and private silence,” which is achieved with tests that give insiders an incentive to stay quiet. We fully characterize optimal tests by developing tools for Bayesian persuasion with incentive constraints, and use these results to derive novel guidance for the design of bank stress tests, as well as benchmarks for socially optimal corporate credit ratings.

Political sentiment and credit ratings

This study examines the relationship between firms’ political sentiment (PSENT) and their credit ratings. Using US public firms as the sample, we reveal that PSENT is positively associated with corporate credit ratings. Furthermore, we find evidence indicating that a positive PSENT leads to higher credit ratings, while a negative PSENT results in lower credit ratings. We also demonstrate that PSENT is positively (negatively) associated with the investment grade and rating upgrade (rating downgrade). The cross-sectional analysis indicates that the positive relationship between PSENT and credit ratings is more evident among firms facing severe information asymmetry, financial distress risk, and weaker governance. Additionally, we observe that PSENT leads to higher new debt issuance. Finally, we conduct a survey of credit analysts and find evidence that corroborates our findings from empirical analyses. Overall, our study suggests that PSENT has an essential bearing on corporate credit quality.

Corporate bankruptcy and banking deregulation: The effect of financial leverage

We investigate the impact of deregulation-induced banking competition on corporate credit risk. Although banking competition does not, on average, affect corporate bankruptcy rates, we find that it causes corporate bankruptcies to increase significantly for high-leverage firms. We show that higher borrowing costs for high-leverage firms post-deregulation and the resulting credit rationing may be key factors behind our findings. The effect of deregulation lasts for up to seven years after the introduction of deregulation and originates mainly from firms that have high short-term debt and are financially constrained. Our results suggest that banking competition, which is expected to expand lending and reduce its cost, may, in fact, create more challenging credit conditions, particularly for firms that are more heavily dependent on external funding.

Reassessing the inversion of the Treasury yield curve as a sign of U.S. recessions: Insights from the housing and credit markets

Is yield curve inversion a reliable recession signal? Prior research on forecasting recessions investigates the role of the slope of the yield curve, housing, banking, and corporate credit spreads in isolation, without fully considering their interconnectedness. Therefore, we conduct a comprehensive investigation into the ability of house prices, residential investment, bank aggregate liquidity creation (LC) and credit (BC), and corporate credit spreads to forecast recessions. For the 1973–2023 sample, after accounting for the slope of the yield curve, our in- and out-of-sample results show that: i) house prices and credit spreads signal recessions—house prices decline, and credit spreads rise ahead of recession quarters; ii) residential investment’s recession forecasting ability is not as robust as house prices; iii) the recession forecasting ability of LC or BC diminishes when we include other indicators. These findings provide important insights into the interconnectedness among economic indicators and their relationship with recessions. Importantly, we demonstrate that the inversion of the yield curve alone is not the surest sign of a recession. For a recession to occur, house prices must decline, and corporate credit spreads must significantly increase.

Enterprise Credit Risk Decision: Application Based on Improved AHP

The credit risk of Shouguang vegetable enterprises in China is the biggest obstacle to corporate loans. Building a credit risk assessment model for Shouguang vegetable enterprises and accurately rating the risk of loan enterprises is the key to successful loans. This article aims to construct an AHP evaluation model for the credit risk of Shouguang vegetable enterprises. The data is sourced from managers, bank credit personnel, university researchers, and enterprise related customers who are familiar with the enterprise, while considering four risk levels: impact degree(I), occurrence probability(P), risk manageability(M), and government support(S). This article uses AHP and risk index scores to evaluate the credit risk of Shouguang vegetable enterprises. This model calculates the risk index score based on survey data from 41 corporate credit risk professionals, constructs a pairwise comparison judgment matrix, and conducts consistency testing. It calculates the risk level membership vectors of impact degree, occurrence probability, risk manageability, and government support level at four risk levels, and then calculates the comprehensive evaluation membership vector of credit risk for Shouguang vegetable enterprise. The calculation results show that the comprehensive credit risk assessment level of Shouguang vegetable enterprise belongs to the general risk level, with a membership value of 0.5836. The results still show that the credit rating of Shouguang vegetable enterprises in the four risk levels of impact degree, occurrence probability, risk manageability, and government support are all average risk levels, but there are differences in membership values. The maximum membership value under the impact degree level is 0.6163, and the minimum membership value under the risk manageability level is 0.5572. This study provides a feasible and practical model for enterprise credit risk assessment and conducts a detailed evaluation of the credit risk of Shouguang vegetable enterprise, providing valuable reference for enterprise managers, bank credit personnel, and related researchers.

Corporate credit default swap systematic factors

AbstractWe examine a comprehensive set of systematic and firm‐specific determinants of the credit default swap (CDS), using a two‐step approach to explore the factor's effect on CDS spread changes. We show that systematic factors are important and account for the most changes in the CDS spreads (with average of 35%), while firm‐specific factors are limited (with of 5% in panel regression) with only 4 out of 28 firm‐specific factors being significant. It implies that the systematic factors are overlooked in the literature, and they can provide many implications for practitioners in CDS pricing and the firm's credit risk management.

Is climate transition risk priced into corporate credit risk? Evidence from credit default swaps

We study whether climate transition risk is reflected in the credit default swap (CDS) spreads of European firms. Using information on the vulnerability of a firm’s value to the transition to a low-carbon economy, we construct a climate transition risk (CTR) factor, and report how this factor shifts the term structure of the CDS spreads of more but not of less vulnerable firms. Considering the CTR factor, we find that different climate transition policies have asymmetric and significant economic impacts on the credit risk of more vulnerable firms, and negligible effects on less vulnerable firms.

Credit default swaps and corporate carbon emissions in Japan

We examine the relationship between carbon emissions and the market perception of firms’ default risk, measured by corporate credit default swap (CDS) spreads in Japan. While corporate revenue size is the most significant factor of carbon emissions, pressure from investors has a significant decreasing effect on carbon emissions, which is greater for investment-grade companies. We find that carbon emissions have time-varying effects on corporate CDS spreads, which supports the “investor awareness” hypothesis across sectors and credit quality. The sectoral impacts indicate that carbon emissions are priced prominently in the CDS spreads of firms in sectors where the transition to carbon-free energy sources appears to be relatively less complicated and less expensive. Finally, we report the impacts of carbon emissions on the CDS spread curve, where they are priced in both short- and long-term CDS spreads, and high carbon emissions steepen the CDS spread curve.

Determinants of corporate credit ratings: Does ESG matter?

We perform an empirical evaluation of fourteen multinomial classifiers in the prediction of credit ratings on a large dataset consisting of macroeconomic, firm-level financial, and environmental, social, and governance (ESG) variables. Random forests and extremely randomized trees exhibit the highest predictive power for US and global firms. We show that environmental and social responsibility variables are important determinants for the credit ratings, specifically measures of environmental innovation, resource use, emissions, corporate social responsibility, and workforce determinants. The influence of ESG variables become more pronounced following the financial crisis of 2007–2009, and are important across both investment-grade and speculative-grade classes.

The Fed takes on corporate credit risk: An analysis of the efficacy of the SMCCF

This paper evaluates the efficacy of the Secondary Market Corporate Credit Facility, a program designed to stabilize the U.S. corporate bond market during the COVID-19 pandemic. The program announcements on March 23 and April 9, 2020, significantly reduced investment-grade credit spreads across the maturity spectrum – irrespective of the program’s maturity-eligibility criterion – and ultimately restored the normal upward-sloping term structure of credit spreads. The Federal Reserve’s actual purchases reduced credit spreads of eligible bonds 3 basis points more than those of ineligible bonds, a sizable effect given the modest volume of purchases. A calibrated variant of the preferred habit model shows that a “dash for cash” – a selloff of shorter-term lowest-risk investment-grade bonds – combined with a spike in the arbitrageurs’ risk aversion, can account for the inversion of the investment-grade credit curve during the height of turmoil in the market. Consistent with the empirical findings, the Fed’s announcements, by reducing risk aversion and alleviating market segmentation, helped restore the upward-sloping credit curve in the investment-grade segment of the market.

Crucial Challenges In Corporate Credit Risk Assessment: A Case Study

This article aims to assess corporate credit risk by predicting the variable that indicates whether the customer has defaulted or not. The dataset used for this purpose is obtained from one of the leading institutions in the finance sector in Türkiye. It consists of 401 variables generally referring to the applicant's data, corporate data, shareholder data, and the applicant's credit history within the creditor's institution. We reduce this large number of variables by identifying the input variables from the others and then studying those inputs to avoid using strongly correlated variables and variables consisting almost entirely of missing or zero values. Many variables in the dataset have too many missing entries but for justifiable reasons. To solve this issue, we created seven subsets to reflect which group of variables relates to which customer. The dataset is imbalanced, consisting of about 96% non-default instances and only around 4% default instances among approved loans. In this paper, we use three sampling techniques to balance the instances in the training sets; under-sampling, oversampling, and synthetic minority oversampling technique, and we apply six classifiers; Random Forest, Naïve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and K-Nearest Neighbor. To measure the performance of these techniques, we use sensitivity and specificity to measure how well the majority class and minority class were respectively predicted. As a result, we simultaneously achieved greater than 50% sensitivity and specificity, where the under-sampling technique was the best sampling technique for the minority class, and the synthetic minority oversampling technique and oversampling performed better for the majority class.

Readability of Pension Narrative Disclosures, Pension Regulatory Changes, and Corporate Credit Risk

We examine, using corporate bond yield spreads, whether and how the readability of pension narrative disclosures is associated with corporate credit risk. The empirical results show that lower readability of pension narrative disclosures is associated with higher bond yield spreads. This association is driven by theoretical mechanisms, including incomplete information, asset value volatility, and financial leverage. The implementation of SFAS No. 132R-1 and the Pension Protection Act (regulatory mechanisms) has strengthened this association. We also consider two potential mechanisms that weaken the association between the readability of pension narrative disclosures and bond yield spreads: bond seniority and collateralization and shorter duration of debt to pension liability. Furthermore, our results show that pension plan size strengthens this association, whereas the funded status, actual return on pension assets, and equity allocation of pension assets weaken this association. Lastly, the results still hold when considering endogeneity issues, controlling for the tones and the length of pension narrative disclosure, employing difference-in-differences analysis, and examining firms with defined-benefit pension plans only or those with newly issued bond observations.

How does the marketization of public resources alleviate corporate credit mismatches?

Market-oriented allocation of public resources is crucial for supporting the high-quality development strategy of Chinese-style modernization. This paper examines how the market-oriented allocation of public resources affects the credit mismatches of enterprises in different cities by focusing on establishing public resource trading centers in China. The analysis is based on listed firms from 2010 to 2022. A multi-period difference-in-difference model is employed to explore the effects and underlying mechanisms. The findings indicate that marketization of public resources can effectively alleviate corporate credit mismatch, and the impact is achieved by mitigating corporate financing constraints and information asymmetry.

A dynamic model of professional legitimacy: Linking professional logics and jurisdictional threats in the corporate credit rating industry

Abstract Research on the professions argues that inference and routinization are key components of professional practice associated with professional legitimacy. However, the literature fails to provide a mechanism linking these practices to legitimacy. The logic of professionalism literature successfully links professional practices to legitimacy via guiding logics, but ignores the importance of routinized practices in the legitimation process. Furthermore, in explaining the prevalence of routinization versus inference in professional occupations, the organizational literature ignores their contributions to legitimacy and exclusively focuses on their contributions to efficiency and effectiveness in the production process. In this article, I propose to resolve all three issues first by interpreting the professional processes of inference and routinization as deriving from distinct professional logics. As such, they provide professionals with unique ideological rationales for legitimacy buffering them from two specific jurisdictional threats. With data from the corporate credit rating industry, I demonstrate that professional firms shift the degree to which they emphasize inference versus routinization in their production process in response to these jurisdictional threats. Finally, I present a model explaining how professional industries rely on a dynamic combination of inference and routinization to maintain a delicate balance over their claimed jurisdictions. This model links the practices of inference and routinization to professional legitimacy via institutional logics, brings greater attention to the importance that routinization plays in the logic of professionalism, and provides a more comprehensive explanation as to why the relative salience of inference and routinization varies within and between professional industries over time.

Credit Scoring Modelling For Corporate Banking Institutions

This research aims to build a credit scoring modeling simulation of bank corporate loans. The credit scoring model is used in assessing creditworthiness in credit decisions. This model determines whether or not a company is eligible for the corporate credit facility it proposes. Observations were made of 100 companies included in the list of Kompas100 Index formers on the Indonesia Stock Exchange (IDX) that have the potential to apply for loans/credits to Bank Financial Institutions (IKB) in optimizing the corporate capital structure through bank debt facilities in the period 2022. Analysis was conducted on five financial aspects consisting of 14 research variables, including (i) liquidity aspects, including current ratio and quick ratio variables; (ii) solvency aspects, including debt asset ratio and equity ratio variables; (iii) profitability aspects including return on net assets, operating profit ratio, price to earnings ratio variables, (iv) activity aspects including total asset turnover, accounts receivable turnover, inventory turnover, current assets turnover, and (v) growth aspects including operating income growth rate, total assets growth rate, and operating profit growth rate variables. The analysis tool uses Logistic Regression through an assessment conducted on the company's credit rating as a proxy for the dependent variable, worth one if the credit application is feasible and worth 0 if the credit application is not feasible with a cut-off point of 0.5. The results show that credit scoring modeling for corporate credit is significantly formed from liquidity (CR) and solvency (DER) aspects. Out of 61 companies classified as not eligible for credit facilities, 58 companies were classified correctly, and out of 39 companies classified as eligible, 29 companies were classified correctly. The overall percentage shows 68.0, meaning that the logistic regression model has an accuracy of 68%.

Do corporate credit spreads predict the real economy?

We evaluate whether corporate credit-spreads measures contain predictive information about the real U.S. economy in a comprehensive specification that includes financial sectors’ profitability, stock and bond market. We find that corporate credit spreads contain no predictive information about U.S. real GDP or consumption and that corporate credit spreads have minimal information about forthcoming recessions. In comparison, the financial sector’s profitability, the Treasury bond and stock market variables contain leading information about recessions, consumption and real GDP.

Fintech development and corporate credit risk: Evidence from an emerging market

This paper analyzes the impact of fintech development on the credit risk of non-financial firms. Using the city-level fintech development index constructed based on data from Ant Finance Service Group, we find that: (1) non-financial firms in cities with better fintech services have lower credit risk; (2) two channels identified are financial constraints and corporate investment efficiency; (3) the role of fintech development in reducing corporate credit risk are more pronounced for non-state-owned firms, small and medium-sized firms, firms in regions with a lower level of marketization, and firms experiencing economic contraction. Our research provides implications for the development of fintech in an emerging market.

Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

Does ESG Affect Bank Lending to Companies?

with the serious intensification of global warming in recent years, countries around the world are actively developing carbon neutrality, carbon peaking, and reducing carbon emissions to ensure a healthier operation of the earth. To cooperate with the low-carbon policies of various countries, companies have also begun to implement policies to reduce carbon emissions and save energy, so ESG has gradually become a hot topic. ESG indicators are also becoming increasingly important in the financial industry. This research is a review essay. The research direction is to explore whether ESG indicators will affect corporate credit in banks by studying the policies and various data on carbon emissions that have been released by countries and by reading published papers. Through research on government policies and current thesis research, it is shown that there are currently no clear policies or rules stipulating how ESG indicators will affect corporate credit in banks, but the current banking industry will indeed pay more attention to ESG performance of companies when leading to companies, good ESG indicators will lead to better bank credit and lower cost of banking credit.

Movables as Collateral and Corporate Credit: Loan-Level Evidence from Legal Reforms across Europe

Movables as Collateral and Corporate Credit: Loan-Level Evidence from Legal Reforms across Europe