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.

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.

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.