Financially engineering a "self-generative” political economy of creditworthiness: Expertocratic exemption problems for sustainable debt and democracy

Repeated crises have proven credit rating agency (CRA) models/methods erroneous and "business-as-usual” unsustainable. Nevertheless, considerable dubious "default risk” management and technoscientific capitalist expertise remain unchanged. Unpacking sovereign ratings, we appreciate how "debt sustainability analysis” (DSA) distortions underpin expertocratic CRA (default) anomaly. Their neoliberal "politics of limits” performance helps market (shareholder) imperatives trump those of democratic (stakeholder) politics. Given surging inflation and debt (distress) to remedy Covid-19-induced shocks, ratings aid constitute and (re)validate the subjectivities/affinities and organizational conditions advancing a "self-equilibrating,” "self-generative” agencement political economy of creditworthiness (PEC). Antagonizing sustainable budgetary government's programmatic/expertocratic and operational/democratic asymmetry, econophysics ratings diminish fiscal sovereignty. Universal PEC management through hybrid credit risk/uncertainty qualculation mitigates negative externality contestation shielding CRAs from serious reform. Ratings procyclicality and contagion reinforce this precarious sociotechnical agencement PEC as the status quo.

Sovereign credit ratings during the COVID-19 pandemic.

Using 603 sovereign rating actions by the three leading global rating agencies between January 2020 and March 2021, this paper shows that the severity of sovereign ratings actions is not directly affected by the intensity of the COVID-19 health crisis (proxied by case and mortality rates) but through a mechanism of its negative economic repercussions such as the economic outlook of a country and governments' response to the health crisis. Contrary to expectations, credit rating agencies pursued mostly a business-as-usual approach and reviewed sovereign ratings when they were due for regulatory purposes rather than in response to the rapid developments of the pandemic. Despite their limited reaction to the ongoing pandemic, sovereign rating news from S&P and Moody's still conveyed price-relevant information to the bond markets.

Predicting Credit Ratings using Deep Learning Models - An Analysis of the Indian IT Industry

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry.

Are CLO Collateral and Tranche Ratings Disconnected?

Between March and August 2020, S & P and Moody's downgraded approximately 25$\%$ of collateral feeding into CLOs and only 2$\%$ of tranche values, with rating actions concentrating in junior tranches. Both S & P and Moody's modeling indicate that the impacts should have been considerably larger, especially for higher-rated tranches. Neither changes in correlation nor the accumulation of pre-COVID-19 protective cushions can explain the downgrade asymmetry on upper tranches. Instead, CLO managers repositioned their collateral pools to dampen the negative credit shock and rating agencies incorporated qualitative adjustments in their CLO ratings. Important potential policy and market implications from these findings are discussed.

DO SUSTAINABILITY RISKS AFFECT CREDIT RATINGS? EVIDENCE FROM EUROPEAN BANKS

Targets defined in accordance with Environmental, Social and Governance (ESG) criteria confront the business world, particularly the banking industry, with new challenges. The aim of this paper is to study the effect of ESG controversies on the credit rating of the European banking sector, involving 65 European banks from 18 countries in the 2011-2020 period. This empirical study includes different approaches. Firstly, we apply an ordered logit model to ascertain the influence of ESG concerns on credit ratings. Secondly, we analyse the impact of ESG controversies on the probability of obtaining a better rating scale through the marginal effects. And finally, we use matching analysis to measure the real impact of ESG controversies on credit ratings. Our findings suggest that ESG controversies have a negative effect on credit rating. In addition, it is a relevant negative factor in the probability of obtaining a better rating in future reviews of credit assessments. Specifically, the lower the level of ESG controversies, the greater the probability of achieving the highest credit ratings. This research provides a comprehensive view of the impact of ESG controversies on credit ratings awarded to European financial institutions. European banks should take special care to avoid such controversies, as a source of reputational risk, when setting their policies so that their credit ratings would not be affected.

Decision Analysis of Multifactor Credit Risk Based on Logistic Regression and BP Neural Network

Small, medium, and micro enterprises play an important role in the development of the national economy and are of great significance in promoting technological innovation, relieving employment pressure, facilitating people’s lives, and maintaining social stability. But in China, small, medium, and micro enterprises generally exist in the phenomenon of “financing difficulties.” Therefore, we need to find a method to forecast its credit risk. By using Python, SPSS, and other software, based on a two-component logistic regression model, assisted by multievaluation model and supported by game theory, this paper establishes an innovative comprehensive credit risk assessment model for small, medium, and micro enterprises.

News from HAL

[...]based on the improved financial performance and cash flow position, the Credit Rating Agencies CARE Ratings and ICRA Limited have upgraded the Company's credit rating from AA+ Stable to AAA/Stable during the financial year. HAL and BEL sign contract for indigenous IRST HAL and BEL signed a contract for co-development and coproduction of Long Range Dual Band Infra-Red Search and Track System (IRST) for Su-30 MKI on 26 April 2022 under the MAKEII procedure of Defence Acquisition Procedure (DAP) 2020, as part of the 'Make in India' initiative. The proposed IRST system will be a high end strategic technology product in the field of defence avionics and technically competitive to existing IRST system in the global market with features of Television Day Camera, Infrared and Laser sensors in single window for air to air and air to ground target tracking and localisation.

Environmental, Social, and Governance Impact on Energy Sector Default Risk—Long-Term Issuer Credit Ratings Perspective

The aim this study is to analyze the impact of environmental, social, and governance (ESG) measures on energy sector credit ratings. The main hypothesis is as follows: The ESG measures have had a significant impact on energy sector credit ratings during the COVID-19 crisis. The analysis has been conducted by using long-term issuer credit ratings presented by the main credit rating agencies. To verify the hypothesis, quarterly data from financial statements, macroeconomic data, and ESG measures for all companies listed on the stock exchanges from all over the world for the 2000–2021 period were collected. The sector was divided into sub-samples according to the type of sector and the moment of the COVID-19 crisis. It was noticed that a stronger reaction of credit ratings during the COVID-19 crisis on ESG factors, than that before it, was also observed, and confirms the increasing role of ESG measures in the financial market. On the other hand, credit rating agencies take into consideration ESG factors during the first estimation. Later, the mentioned variables lose their importance. This is based on a few reasons. It is still a small sample of entities that publish non-financial statements connected with ESG. Some countries have yet to implement regulations associated with climate risk. The significance of electricity power consumption and CO2 emissions confirms the significance of the mentioned direct or indirect impact of ESG factors. Credit rating agencies are not willing to change credit ratings because usually companies from the energy sector, especially from coal and oil and gas subsectors, are large entities. They sometimes receive financial support from governments. Governments are also stakeholders that create a lower risk of default. In less developed countries, coal is one of the main energy sources, and costs connected with alternative, renewable energy are more expensive. The prepared research also suggests that particular ESG measures have varying significance on credit ratings. Therefore, it can help to analyze and build models by investors. It will not be without significance for estimating the default risk and the cost of the capital. In most cases, the most significant measure is the E factor. Copyright © 2022 Chodnicka-Jaworska.

Banking Risks and Lending to Tourism and Hotel Businesses amid the COVID-19 Pandemic

The purpose of this paper is to explore the risks associated with lending to tourism and hospitality businesses in the context of COVID-19 restrictions. The author proposes a scoring methodology to assess the quantitative and qualitative factors of borrowers' credit standing based on industry-related risks and the quantification of potential loss given default. The analysis of credit standing draws on actual figures from financial statements of the ten biggest tour operators present in the Russian and Kazakh tourism markets, as well as a survey of experts specialising in bank lending. The findings confirm the proposed hypothesis of a negative impact of the pandemic on the financial condition and credit standing of tourism and hospitality businesses and an increase of banks' credit risks. The trends of lending to tourism and hotel businesses have indicated a contraction of credit supply as a result of the low credit standing of borrowers. An industry analysis shows that losses from the materialisation of credit risks in the group of tourism businesses could be significant for banks. The author concludes that to maintain financial stability and the level of credit in the industry, the state should continue to support the sector, including the provision of soft loans to help finance some items of expenses.

Environmental Risk and Credit Ratings, and the Moderating Effect of Market Competition.

This study examines the relationship between environmental risk and corporate bond credit ratings, and the moderating effect of market competition. We focus on Korean firms that are facing increasing risk of environmental crisis after the COVID-19 pandemic. Recently, the Korean government has been controlling businesses while promoting policies to transform the economy into a low-energy, low-carbon economy. We find that a firm's greenhouse gas emission and energy consumption, which are direct indicators of environmental risk, are negatively associated with bond credit ratings. We also report that the negative effect of environmental risk on credit ratings is stronger in firms with low market competition. This study contributes to prior research by improving the understanding of the effect of environmental risk on credit ratings. In particular, it is significant to examine the effect of environmental risk, measured as direct environmental performance not affected by green washing, on credit rating. Therefore, we shed light on environment-oriented management beyond the determinants of credit ratings, which have been discussed in previous studies. We also suggest that policymakers need to manage market competition in terms of environmental justice, given that market competition has a significant moderating effect on the relationship between environmental risk and credit ratings.

Machine learning method to solve the credit decision-making problem of small and medium-sized enterprises

Small and medium-sized enterprises(SMEs) play important roles in our economy. However, the scale of SMEs is relatively small. There is also a lack of pledged assets, which results in a problem of difficulty in borrowing funds. Banks face the problem that how can they determine whether to lend or not when evaluating SMEs credit risk factors, including the strength and reputation, as well as credit strategies, such as loan quota, interest rate and term. In this paper, support vector machine and decision tree method are comprehensively used to learn the enterprises data and evaluate the credit rating of enterprises lacking credit information. A linear optimization model is established based on the bank's principle of maximizing the expected annual profit, and this paper provides the optimal strategy for banks to decide the amount of loans granted to each enterprise. In addition, emergency situation is taken as an example, such as Covid-19 epidemic, by utilizing machine learning method and optimization theory, based on the fact that banks expect to maximize profit, establish an optimization model with wider applicability. And this paper provides credit strategy for banks when facing unexpected environmental emergency. © 2021 SPIE.

Assessing the impact of COVID-19 induced rating downgrades on Eurobond yields in Africa

There was an outcry from policymakers over sovereign credit rating downgrades of African countries during the unprecedented COVID-19 lockdown periods. This study investigates whether sovereign downgrades during the time African countries were hit by COVID-19 had an impact on sovereign bond yields. Applying an event study analysis on the Eurobonds yields of 4 African countries that were downgraded during this period shows that there is significant evidence of excess volatility around the downgrade event and a net increase in yields within the rating event window. The results align to the view that rating agencies negatively impact macroeconomic conditions through their procyclical ratings. Hence, ratings should be regulated and controlled during crises times to avoid the procyclical impact of ratings.

Carbon Emissions and Firm Performance: Evidence from Financial and Non-Financial Firms from Selected Emerging Economies

This paper examines the effects of carbon emissions on the accounting and market-based performance of financial and non-financial firms in emerging economies. Data for 104 financial and 328 non-financial firms constituting 2591 observations operating in 22 emerging economies were collected from the Datastream database for the period 2011–2020. We applied OLS and 2SLS regression techniques to analyze the data. Results show that financial firms emit less carbon than their non-financial counterparts. The results further show that carbon emissions reduce firms’ return on equity, Tobin’s Q, Z-score, and credit rating. Our findings remain robust in different estimation techniques and alternative proxies of performance. Our results have some important policy implications for emerging economies.