Green Certification, Credit Rating, and Mainland Chinese Green Bonds in Hong Kong

This chapter discusses green certification and credit rating on Mainland Chinese green bonds in Hong Kong. These green bonds are mostly denominated in USD, distributed to global investors, and issued with international practices of green certification and credit rating. Using qualitative analysis and case study method, the chapter finds four external reviewers sharply different in their assessment framework although they attempt to assess degree of compliance of a bond issuance or a bond issuer with some international green standards. All the three global credit rating agencies claim their incorporation of green assessment into their credit rating process. However, the chapter finds no clear evidence on such claim from their credit rating comments on selected bond issuers.

The ignored tool of corporate governance rating: An overview of the corporate world in the emerging market

Generally, the interest of stakeholders is to see the growth of their entities, also they benchmark their entities through business performance metrics or tools like return on equity, return on assets (Mishra & Kapil, 2018), earnings per share, gross profit margin, employee productivity, sales turnover, ratings given by prominent credit rating agencies, such as Investment Information and Credit Rating Agency (ICRA), Credit Rating Information Services of India Limited (CRISIL), Standard and Poor, etc. In addition to this, internal governance mechanisms, board of directors’ characteristics, their independence, transparency, concentration, and presence of employees in the ownership structure also influence financial and stock market performance (Braendle, Stiglbauer, Ababneh, & Dedousis, 2020). However, assessing the performance of entities through some of these limited angles is not always possible. One more criterion for assessing the performance of entities is corporate governance rating (CGR). However, it is not widely used as a tool to assess a firm’s performance in emerging markets. The present research paper is intended to address the scenario of corporate governance rating in Indian corporate world to assess a firm’s performance. With the help of majorly secondary sources of data, this study was conducted from 2003 to 2021 based on the CRISIL’s rating pattern. The results revealed that only 20 companies adopted the process of corporate governance rating. The findings showed the significance of corporate governance rating, its adoption and future research in the development of the rating mechanisms in India as well as in other emerging markets.

Corporate risk-taking after changes in credit rating

PurposeThe paper aims to investigate corporate risk-taking following changes in firms' credit ratings (CR) and the mechanisms the firms use in implementing the risk-taking.Design/methodology/approachThe paper employs fixed-effect regression models to examine risk-taking behaviour after firms experience changes in CR after their ratings are downgraded to the lower edge of the investment grade rating (i.e. BBB-) and after their CRs are downgraded below the investment rating.FindingsThe paper finds that, whilst in general, changes in CR are negatively associated with post-event risk-taking, firms downgraded to BBB- do not increase their risk-taking. Only when firms are rated below this grade, firms significantly increase their risk-taking, suggesting that the association between downgrades in CR and firm risk-taking following the event is not linear. Further analysis suggests that these downgraded firms do not increase research and development (R&D) expenses or capital expenditures but employ long-term debt as their risk-taking mechanism.Practical implicationsThe findings of the paper have practical implications for investors considering investing in downgraded-rating firms to shareholders of such firms and especially to those overseeing the firms' risk-taking policies.Originality/valueThe study fills the gap in the literature by providing empirical evidence on corporate risk-taking after changes in CR and also contributes to the optimal debt-maturity choice literature.

Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

Credit is one of the most significant elements in banks and financial institutions. It can also be described as unpredicted events, which mainly occur in the form of either assets or liabilities. The risk occurrence is that the facility recipients have no willingness and ability to repay their debt to the bank, which is a default that is synonymous with credit risk. Credit ratings are a way to decrease and measure credit risk and, therefore, manage it appropriately. Credit rating is an approach for estimating the features and recipients of facilities’ performance based on quantitative criteria, including the company’s financial information. The anticipated future performance allows the applicants to obtain facilities with the exact specifications. In this study, due to the need and significance of calculating the credit risk concept, a novel method based on the hybrid method of artificial neural networks and an improved version of Owl search algorithm (IOSA) and forecasting of C5 risk of decision tree credit is done. This algorithm has two major parts. The decision tree runs based on an IOSA to provide the best weighting of the neural network. The weights created along with the problem data are then given as the input to the main network, and the data are classified. The algorithm has the highest level of accuracy, 96% that is much higher than other algorithms. The results also show a precision of 0.885 and a recall of 0.83 for 618 true positive samples. The proposed method has the highest accuracy and reliability toward the other comparative methods. The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.