Do green finance and green innovation affect corporate credit rating performance? Evidence from machine learning approach.

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability.

COVID-19 and credit risk: A long memory perspective.

The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.

Deep Neural Networks for Behavioral Credit Rating.

Logistic regression is the industry standard in credit risk modeling. Regulatory requirements for model explainability have halted the implementation of more advanced, non-linear machine learning algorithms, even though more accurate predictions would benefit consumers and banks alike. Deep neural networks are certainly some of the most prominent non-linear algorithms. In this paper, we propose a deep neural network model for behavioral credit rating. Behavioral models are used to assess the future performance of a bank's existing portfolio in order to meet the capital requirements introduced by the Basel regulatory framework, which are designed to increase the banks' ability to absorb large financial shocks. The proposed deep neural network was trained on two different datasets: the first one contains information on loans between 2009 and 2013 (during the financial crisis) and the second one from 2014 to 2018 (after the financial crisis); combined, they include more than 1.5 million examples. The proposed network outperformed multiple benchmarks and was evenly matched with the XGBoost model. Long-term credit rating performance is also presented, as well as a detailed analysis of the reprogrammed facilities' impact on model performance.

Statistical Surveillance of Structural Breaks in Credit Rating Dynamics.

The 2007-2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms' credit rating transition dynamics as a proxy of the credit market and discuss how statistical process control tools can be used to surveil structural breaks in firms' rating transition dynamics. After reviewing some commonly used Markovian models for firms' rating transition dynamics, we present several surveillance rules for detecting changes in generators of firms' rating migration matrices, including the likelihood ratio rule, the generalized likelihood ratio rule, the extended Shiryaev's detection rule, and a Bayesian detection rule for piecewise homogeneous Markovian models. The effectiveness of these rules was analyzed on the basis of Monte Carlo simulations. We also provide a real example that used the surveillance rules to analyze and detect structural breaks in the monthly credit rating migration of U.S. firms from January 1986 to February 2017.

The effect of environmental credit rating on audit fees: A quasi-natural experiment from China.

Environmental credit rating (ECR) is a new policy that deeply integrates the construction of ecological civilisation and the social credit system in China; however, there is a paucity of research on the response of external auditors to the ECR. This study takes the environmental credit evaluation policy implementation as a quasi-natural experiment, using China's A-share listed companies in heavily polluting industries from 2008 to 2019 as samples. We construct a heterogeneous timing difference-in-differences model to empirically explore the impact of ECR on audit fees. The results show that the ECR significantly reduces companies' audit fees. Importantly, our analysis suggests that the ECR improves environmental information transparency and enhances sustainable operation ability, thereby reducing audit fees. Further analysis shows that the negative correlation between the ECR and audit fees is more obvious in non-state enterprises, in poor legal environments and low levels of trust. Our study provides scientific evidence for the economic consequences of the environmental credit evaluation policy and enriches the literature on the factors affecting audit fees. It has revelatory significance for China and other developing countries to implement and improve the environmental credit evaluation policies and better guide enterprises to fulfil their environmental responsibilities.

Using a genetic backpropagation neural network model for credit risk assessment in the micro, small and medium-sized enterprises.

In China, with the "Double Carbon" goal within reach, Micro, Small and Medium-sized Enterprises (MSMEs) emerge as pivotal contributors to economic advancement. However, they are now confronted with the imperative of transitioning towards green and low-carbon practices. To facilitate the attainment of peak carbon dioxide emissions and carbon neutrality, a refined approach is imperative. This entails precise capital allocation, enhanced financial services, streamlined management, and robust risk mitigation strategies. Consequently, conducting thorough credit risk assessments for MSMEs becomes a crucial endeavor. However, obtaining substantial loans for them proves challenging due to their elusive credit ratings and potential defaults. To address this issue, this study leverages machine learning and intelligent optimization algorithms to construct a classification model for default and credit ratings of MSMEs, utilizing their daily invoice data. Specifically, twelve indicators pertaining to default and credit ratings are extracted. Subsequently, Principal Component Analysis is employed to reduce dimensionality and synthesize all pertinent information. Following this, the Genetic Algorithm-based Back Propagation Neural Network (GA-BPNN) is utilized to delineate the relationship between indicators and default, as well as credit rating, respectively. The results indicate a prediction accuracy of 0.92 for default risk and 0.86 for credit rating. This underscores the efficacy of GA-BPNN in effectively classifying the underlying default risk and credit ratings of MSMEs, offering a promising approach for decision-making.

The relationship between sovereign credit rating changes and firm risk.

Sovereign credit ratings, extensively studied for their influence on macroeconomics and country risk, have been less explored in the context of their impact on individual firms. This research delves into the effects of sovereign credit rating changes on firm risk. Our findings suggest that an upgrade in sovereign credit ratings decreases firm risk, while a downgrade amplifies it. Furthermore, the magnitude of a country's rating shift positively correlates with changes in firm risk. We also discern a contagion effect between trade-dependent countries: an elevated rating in one country diminishes the firm risk in its trading partner, and vice versa. When categorizing our data into developed and developing markets, we observe that firm risk in developed markets reacts more acutely to rating upgrades. Conversely, rating downgrades, whether domestic or in trade-associated countries, intensify firm risk in developing markets. A robustness check, which evaluates sovereign credit rating fluctuations outside of financial crises, corroborates our core findings.

Does Environmental Credit Rating Promote Green Innovation in Enterprises? Evidence from Heavy Polluting Listed Companies in China.

Environmental credit rating (ECR) is a novel environmental governance tool proposed by China, but its implementation effect is still unknown. This study analyzed whether it achieves the goal of encouraging green innovation in enterprises. Based on the green patent data of listed companies in heavy polluting industries in China from 2010 to 2018, we constructed a heterogeneous timing difference-in-differences model to empirically study the impact of the ECR policy on green innovation. We find that the policy has significantly promoted heavy polluting enterprises' green innovation. Moreover, the results passed a series of robustness tests. Importantly, we find that the policy has a positive effect on enterprises' green innovation through the reputation mechanism and financing mechanism. Furthermore, the incentive effect of the policy varies with enterprise characteristics and regional characteristics: the green innovation effect of the policy is more obvious in large-sized and state-owned companies and companies in regions with low fiscal pressure and a high level of financial development are more likely to induce firms' green innovation. Our research will be of practical value to China's environmental management, as well as global value to other countries.

Research on Bond Participants' Emotion Reactions Toward the Internet News in China's Bond Market.

The literature has widely studied the market response to the financial news or events but mainly focused on the stock market. This article associates the concept of internet news with the bond market response and attempts to examine how credit rating agencies (CRAs) and bond investors, two important bond participants, react to financial news on the internet with a range of multiply regressions. Our empirical study leads to several findings. First, CRAs tend to ignore the warnings of financial news on the internet, whereas bond investors strongly react to such news. Second, there is an asymmetry in bond investors' reactions to good news compared to bad news, with investors being more sensitive to bad news. Third, there is heterogeneity in the psychological reaction where bond investors do not react to the news about central state-owned enterprises (SOEs) but to the news about other enterprises. Finally, there is an asymmetric response driven by news timeliness that bond investors are more sensitive to the latest news articles than old ones. Overall, our study confirms the existence of psychological reactions to the financial news on the internet in China's bond market, which has significance for keeping bond market participants from overreacting or underreacting to market news.

A novel framework of credit risk feature selection for SMEs during industry 4.0.

With the development of industry 4.0, the credit data of SMEs are characterized by a large volume, high speed, diversity and low-value density. How to select the key features that affect the credit risk from the high-dimensional data has become the critical point to accurately measure the credit risk of SMEs and alleviate their financing constraints. In doing so, this paper proposes a credit risk feature selection approach that integrates the binary opposite whale optimization algorithm (BOWOA) and the Kolmogorov-Smirnov (KS) statistic. Furthermore, we use seven machine learning classifiers and three discriminant methods to verify the robustness of the proposed model by using three actual bank data from SMEs. The empirical results show that although no one artificial intelligence credit evaluation method is universal for different SMEs' credit data, the performance of the BOWOA-KS model proposed in this paper is better than other methods if the number of indicators in the optimal subset of indicators and the prediction performance of the classifier are considered simultaneously. By providing a high-dimensional data feature selection method and improving the predictive performance of credit risk, it could help SMEs focus on the factors that will allow them to improve their creditworthiness and more easily access loans from financial institutions. Moreover, it will also help government agencies and policymakers develop policies to help SMEs reduce their credit risks.

Credit rating in dynamic response to the nature of firms and the business model of rating agencies: evidence from the Chinese bond market.

This paper takes the A-share listed companies that issued credit bonds from 2010 to 2021 as the sample to test the probability and degree of credit rating change throughout the enterprise life cycle using the ordered logit and breakpoint regression models. Further, we study the heterogeneity of the above performance from payment models and firm natures. The results show that the credit rating inflation problem generally exists in all stages of the enterprise life cycle. The inflation is lower in the investor-pays model (state-owned enterprises), while the opposite results occur for the issuer-pays model (non-state-owned enterprises). Specifically, (1) the probability of a higher credit rating and the increased credit ratings show as an 'inverse U' in the enterprise life cycle. Credit rating increases if the enterprise successfully enters the growth phase, decreases if the enterprise fell into the decline phase. (2) In the investor-pays model, enterprises have a greater probability of obtaining a higher credit rating in the mature phase and a lower credit rating during the decline period. In the issuer-pays model, although the enterprise gets a smaller credit rating due to falling into the decline phase, the credit rating still has a high probability of belonging to a high credit rating. (3) State-owned enterprises have a higher probability of obtaining a high credit rating in the mature period and are more likely to have a low credit rating in the decline period. Generally, their credit rating quality is better than that of non-state-owned enterprises. In addition, in the context of the financing pressure period, the credit rating of non-state-owned enterprises decreases as they drop into the decline phase.

P2P lending platforms in Malaysia: What do we know?

Background - With the recent evolution of Financial Technology (FinTech), 11 peers to peer (P2P) lending platforms have been regulated by the Securities Commission in Malaysia since 2016. P2P lending platforms offer new investment opportunities to individual investors to earn higher rates on return than what traditional lenders usually provide. However, individual investors may face higher potential risks of default from their borrowers. Therefore, individual investors need to understand the potential exposure to such P2P lending platforms to make an effective investment decision. This study aims to explore the potential risk exposures that individual investors may experience at Malaysia's licensed P2P lending platforms.   Methods - Based on data collected manually from nine P2P lending platforms over five months, relationships between interest rates and various risk classifying factors such as credit rating, industry, business stage, loan purpose, and loan duration are examined.    Results- This study shows that loans with a similar credit rating and with or without similar loan purpose; and a business stage may offer investors significantly different interest rates. In addition, loans with shorter durations may provide investors with higher interest rates than those with longer durations. Finally, loans issued by companies from the same industry appeared to be charged with similar interest. These findings are valuable to investors to prepare themselves before making their investments at the P2P lending platforms.   Conclusion- With first hand-collected data, this study provides an original insight into Malaysia's current P2P lending platforms. Findings obtained for relationships between interest rates and risk classifying factors such as credit rating, industry, business stage, loan purpose and loan duration are valuable to investors of Malaysian P2P lending platforms.

Evaluating the enhancement and improvement of China's technology and financial services platform innovation strategy.

The development of high-tech industry has been prosperous around the world in past decades, while technology and finance have already become the most significant issues in the information era. While high-tech firms are a major force behind a country's economic development, it requires a lot of money for the development process, as well as the financing difficulties for its potential problems, thus, how to evaluate and establish appropriate technology and financial services platforms innovation strategy has become one of the most critical and difficult issues. Moreover, how the chosen intertwined financial environment can be optimized in order that high-tech firms financing problems can be decided has seldom been addressed. Thus, this research aims to establish a technology and financial services platform innovation strategy improvement model, as based on the hybrid MADM model, which addresses the main causal factors and amended priorities in order to strengthen ongoing planning. A DEMATEL technique, as based on Analytic Network Process, as well as modified VIKOR, will be proposed for selecting and re-configuring the aspired technology and financial services platform. An empirical study, as based on China's technology and financial services platform innovation strategy, will be provided for verifying the effectiveness of this proposed methodology. Based on expert interviews, technology and financial services platforms innovation strategy improvement should be made in the following order: credit guarantee platform (C)_credit rating platform (B)_investment and finance platform (A).