Corporate Credit Research Articles

Predicting corporate credit ratings using the content of ESG reports

Predicting corporate credit ratings using the content of ESG reports

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

Does pledging movables as collateral alter corporate borrowing? To answer this question, we study the effect of collateral law reforms on syndicated bank loans granted across nine European countries that facilitated pledging movables between 1995 and 2019, comparing them to 19 countries that did not. We differentiate firms in sectors of higher versus lower asset movability to strengthen the identification. We find that although the reforms have enabled firms in movable-intensive sectors to issue more secured loans, the average cost of the loans and the number of covenants have also increased. Channel tests suggest that banks may demand more to compensate for the potential wealth redistribution induced by newly issued secured credit, or the unique risk involved with using movables as collateral.

Effects of climate change on firm credit risk: evidence from China

ABSTRACT Our study examines the impact of extreme weather on corporate credit risk, identifies the impact pathway, and explores the influences of different heterogeneity contexts. We propose a modified version of the climate risk measure, Modified Regional Wise Climate Risk Index for Company (MRW-CRIc), which considers not only the climate risk on financial loss but also company-specific factors. We find that climate risk significantly affects enterprise credit risk and that the impact of extreme weather varies across three industries. This research contributes to regulatory efforts by enhancing the measurement of climate risk and deepening understanding of its implications for market stability.

Managerial myopia and corporate credit spreads

Using bond issuance data from listed companies, we demonstrate that firms with myopic management face substantially higher credit spreads, a pattern with both strong statistical and economic significance across multiple robustness checks. Myopic management tends to imprudently allocate longer-term funds to short-term projects, potentially prompting investors to demand higher credit premiums. The rise in credit spreads observed only in long-term bond issuances further reinforces our argument. Inefficient investment decisions made by myopic management, characterized by excessive short-term investments and insufficient long-term investments, may also account for elevated credit spreads. While effective corporate governance and enhanced external monitoring can help alleviate the adverse impacts of managerial myopia on corporate credit spreads, intensified industry competition and increased uncertainty may exacerbate these challenges. Encouragingly, the elevated credit spreads could prompt myopic management to adjust their business strategies, thereby mitigating the worsening economic consequences for the corporate.

Digital transformation, information and lending distance -Evidence from Chinese bank branch data

This research aims to empirically investigate the relationship between bank digital transformation and lending distance. In traditional financial geography theory, banks have a natural geographical proximity preference in their credit decision-making, which might lead to suboptimal credit allocation. Can the rapid development of digitalization improve the situation that banks are only capable of undertaking ex ante due diligence and ex post monitoring of proximate borrowers, given that fintech has greatly improved the overall efficiency of the financial industry? Using bank loan data of listed enterprises in China from 2013 to 2021, this paper finds that the digital transformation of banks can significantly extend the lending distance between banks and enterprises. The moderating effect analysis suggests that distance extending effect of bank digitalization is more pronounced for firms with poor information quality and in competitive banking sector. Further, digital transformation extends the lending distance without increasing risk preferences, as evidenced by the empirical finding that the non-performing loan ratio remains unaffected by the increased distance. Also, geographical expansion in lending distance reduces loan concentration. The research in this paper supports the conclusion that banks' digital transformation positively impacts corporate credit allocation and is beneficial to achieving financial stability by improving loan performances.

Corporate credit risk modeling under carbon pricing uncertainty: A Knightian uncertainty approach

This study examines the financial implications of carbon pricing policies within the Knightian uncertainty framework. Employing a dynamic behavioural credit risk model driven by Lévy jump-diffusion, we scrutinise how carbon pricing uncertainty influences default probability and securities value. We explore investors' strategic responses to ambiguity and assess their impact on their investment decisions. Our findings reveal that carbon pricing uncertainty exacerbates the margin of default risk, has a moderating effect on stock value, and makes investors more cautious, thereby altering corporate capital structures. This study contributes to the discourse on carbon credit risk assessment and sustainable finance by addressing policy-driven uncertainties in the financial markets.

Reputation resources and debt maturity structure: Evidence from Chinese university-based listed companies

This study focuses on the relationship between reputation resources and debt maturity structure and finds that enhancing reputation resources can optimize debt maturity structure, in which corporate credit ratings play a mediating role; moreover, there are differences in the impact of reputation resources on debt maturity structure for firms of different natures, in which the impact is more significant for non-state-owned firms. The findings of this study remain valid after the robustness and endogeneity tests.

The Impact of Digital RMB Promotion on China's Corporate Financial System and Policy Recommendations

With the deep development of the digital economy era, digital RMB, as an important form of central bank digital currency, plays an increasingly crucial role in China's financial system. It not only has all the functions of traditional currencies, but also shows unique advantages in enhancing payment efficiency, reducing transaction costs, and improving financial regulatory capabilities. This study conducted an in-depth analysis of the impact of the promotion of the digital RMB on China's corporate financial system by visiting and researching a number of enterprises and collecting financial data over a five-year period. The study finds that the promotion of digital RMB has facilitated the convenience of inter-company payments and settlements, enhanced the traceability of financial data, and positively impacted the enhancement of corporate credit availability and financial service innovation. However, there are also challenges such as increased costs for enterprises to adapt to the new system and risks of digital currency custody. Based on this, this paper constructs a multi-level analytical framework to systematically sort out the mechanisms of the impact of digital RMB on the corporate financial system, and puts forward a series of policy recommendations accordingly. These recommendations involve strengthening the construction of corporate digital infrastructure, coordinating the interface between digital RMB and existing financial regulatory policies, and enhancing corporate digital currency knowledge training. This study aims to provide theoretical support and empirical references for policy formulation and financial decision-making by government departments, financial regulators, and enterprises in the digital currency era.

Corporate credit mismatch, financial leverage and corporate investment efficiency

We empirically test the relationship between credit mismatch and corporate investment efficiency. The study analyzes the heterogeneity of mismatch direction, corporate nature, and corporate life cycle. Results reveal that corporate credit mismatch has a significant negative impact on corporate investment efficiency; corporate financial leverage fully mediates this impact, indicating that corporations may increase their financial leverage to bridge financing gaps in cases of credit mismatch. This practice, however, may bring about greater financial risk and affect corporate investment efficiency. This study provides an important reference for enterprises and financial institutions for optimizing the financing environment, ensuring the effective allocation of financing resources, promoting the sustainable development of enterprises, thereby enhancing overall economic efficiency. This study also provides new ideas and future directions for studying corporate financing and investment behavior.

Divergent credit risk responses to local government debt among Chinese banks: a stress test analysis

ABSTRACT This study explores the heterogeneous impact of local government debt on credit risk across ‎state-owned and urban and rural commercial banks in China, through the analysis of stress test ‎methodology and quarter-based data in the period 2014–2021. It found that if the scale ‎of local government debt expands, the Non-Performing Loan (NPL) rates will reduce in state-owned commercial banks, ‎while the rural and urban commercial banks will increase. In the case of further decay in the scale of local government debt and macroeconomic indicators, there would be more credit risk in a heterogeneous manner among different-sized commercial banks. Medium-sized urban commercial banks have the smallest rise in non-performing loan rates due to the higher individual and corporate credit risks they face. Moreover, the study finds the resiliency of the risks weak and poor preparation for loan impairment in covering the potential loan losses in a pressured scenario compared to their counterpart urban and state-owned rural commercial banks. The findings suggest that rural commercial banks need to improve their risk ‎resilience to cope with extreme shocks.

The application of Bayesian inference under SAFE model

This paper responds to Professor Paolo Giudici's call for papers in ‘Safe Machine Learning’ and explores the application of Bayesian inference within the SAFE model framework, aiming to enhance the accuracy and reliability of environmental, social, and governance (ESG) factor analysis in the financial sector. The paper begins by introducing the basic concept of the SAFE model, which integrates ESG factors into the assessment of corporate credit ratings to promote sustainable development in the financial field. Furthermore, this paper discusses forecast estimation, uncertainty quantification, Gaussian processes, iterative optimization, and model robustness within the SAFE model framework. It is important to note that the SAFE model is not limited to the aforementioned applications; it also has the capacity to understand the model's extensive utility in other financial sectors, which can reflect the model's comprehensive scope.

Management team categories, social network characteristics, and corporate credit risk

We investigate the association between the social networks of management team (AMT) and corporate credit risk using American corporate bond market data. We find that the social network size of AMT is significantly and positively related to corporate bond yield spreads, consistent with the risk-taking mechanism. In addition, the relative social networks of AMT to CEO and subordinate executives to CEO are significantly and positively related to bond yield spreads while that of middle managers to CEO have the opposite association. This implies that relatively greater social networks of subordinate executives could potentially influence the CEO to undertake greater risks, whereas relatively greater social networks of middle managers reduce corporate credit risk mainly through the information-sharing mechanism rather than the risk-taking effect since MM has no direct decision-making authority. Moreover, the variations in social networks among AMT are positively associated with corporate bond yield spreads, suggesting that an inequitable distribution of power among AMT members increases corporate credit risk. Additionally, we find that non-invesment-grade bonds, managerial entrenchment, information asymmetry, and overinvestment all intensify the positive association between AMT social networks and credit risk, namely enhancing the bondholders’ perceptions about the risk-taking mechanism of AMT social networks. Finally, our findings are robust after considering endogeneity concerns and alternative model settings.

Corporate Credit Risk and Bond Yield Spreads: Market Reactions to the Spreads

This article utilizes panel data from corporate bonds between 2014 and 2022 to explore the relationship between corporate credit risk and bond yield spreads. The findings indicate a positive correlation between corporate credit risk and bond yield spreads, a relationship that is mediated through market liquidity. Further analysis reveals that the impact of corporate credit risk on bond yield spreads is more significant in large corporations and in samples post-2018. These findings provide solid empirical support for corporate decision-makers to focus on corporate credit risk.

The impact of Macroeconomic and Microeconomic variables on Credit Risk: A Comparative Study between Retail and Corporate Credit Risks in the banks of Egypt

The impact of Macroeconomic and Microeconomic variables on Credit Risk: A Comparative Study between Retail and Corporate Credit Risks in the banks of Egypt

Credit risk early warning for listed companies based on ESG information: evidence from China

ABSTRACT This study investigates the credit risk early warning of environment, social, and governance (ESG) information using financial and market models. We find that the ESG rating is negatively associated with the corporate credit risk. The addition of ESG information has significantly improved the prediction accuracy of the credit risk early warning model. This result remains robust after considering ESG rating divergence and endogeneity. Further analysis shows that cash flow volatility is an important mechanism for ESG information to play a role in identifying corporate credit risk.

Inhibit or stimulate? Green credit guidelines and corporate financialization

Although the green credit guidelines (GCGs) can force firms to engage in green transformation, it is still unknown whether the GCGs inhibit or stimulate corporate financialization. This research explores the effects and mechanisms of GCGs on financialization of heavy polluters using difference-in-differences estimators. The results indicate that the GCGs tend to strengthen heavy polluters’ motivations for precautionary saving and stimulate them to allocate liquid financial assets, while weakening their motivations for investment substitution and inhibiting their speculative allocation of financial assets. After several robustness tests, these conclusions remain valid. The mechanism tests suggest that the guidelines affect corporate financialization through increased financing constraints and reduced agency costs. Further analysis reveals that both types of financial asset allocation used by heavy polluters increase their levels of total factor productivity and green innovation. Heterogeneity analysis demonstrates that the impact of GCGs is more pronounced in nonstate-owned enterprises and firms which are located in regions with underdeveloped financial development and weak environmental law enforcement. In summary, this research explores the differentiated impacts and economic outcomes of GCGs, which not only provides insights into how financial institutions prevent corporate credit risk and achieve the targeted implementation of GCGs but also has certain reference value for the rationalization of asset allocation and green transformation for heavy polluters.

Government social capital and bond credit ratings

Few studies have explored the impact of heterogeneous government investment on corporate bond ratings from the perspective of government social capital allocation. This paper examines the bond market's response to government social capital allocation, utilizing empirical data from the bond ratings of listed companies issued between 2008 and 2020. The analysis begins with a rational explanation, conceptual definition, and index construction of government social capital. The empirical research finds that government social capital allocation significantly enhances corporate credit ratings. Mechanism tests reveal that local governments provide “government support” signals through short-term and long-term borrowing, enabling firms to secure more long-term loans and reduce their default risk. These signals are captured by rating agencies, which then influence the rating outcomes. Heterogeneity tests indicate that the positive impact of government capital allocation on ratings is more pronounced during periods of corporate financial distress. Furthermore, foreign rating agencies are better able to capture signals of government social capital allocation, and their independence confirms that high ratings are not the result of rating inflation.

Financial Sustainability and Corporate Credit Risk: Moderating Role of Earnings Management

Financial Sustainability and Corporate Credit Risk: Moderating Role of Earnings Management

Time series properties of corporate credit rating transitions by regions and rating agencies

ABSTRACT The study confirms the time series properties of the credit rating transitions and finds commonalities and differences across regions and rating agencies. Significant differences were found between regions but not between rating agencies.

Monetary policy, corporate credit and digital transformation

This paper investigates whether monetary policy and digital transformation of enterprises can increase corporate credit, using Chinese A-share listed companies in Shanghai and Shenzhen from 2014 to 2021. The results show that loose monetary policy can significantly increase corporate credit, while the higher the degree of enterprise digital transformation, the less favorable loose monetary policy is to increase corporate credit. Meanwhile, the heterogeneity analysis finds that the higher the degree of digital transformation of non-state-owned enterprises relative to state-owned enterprises, the more unfavorable loose monetary policy is in increasing corporate credit. The enterprise liquidity ratio is larger, the asset-liability ratio is smaller, and the size is moderate, the digital transformation of enterprises is more conducive to increasing corporate credit.