Understanding and Attaining an Investment Grade Rating in the Age of Explainable AI

Abstract

Specialized agencies issue corporate credit ratings to evaluate the creditworthiness of a company, serving as a crucial financial indicator for potential investors. These ratings offer a tangible understanding of the risks associated with the credit investment returns of a company. Every company aims to achieve a favorable credit rating, as it enables them to attract more investments and reduce their cost of capital. Credit rating agencies typically employ unique rating scales that are broadly categorized into investment-grade or non-investment-grade (junk) classes. Given the extensive assessment conducted by credit rating agencies, it becomes a challenge for companies to formulate a straightforward and all-encompassing set of rules which may help to understand and improve their credit rating. This paper employs explainable AI, specifically decision trees, using historical data to establish an empirical rule on financial ratios. The rule obtained using the proposed approach can be effectively utilized to understand as well as plan and attain an investment-grade rating. Additionally, the study investigates the temporal aspect by identifying the optimal time window for training data. As the availability of structured data for temporal analysis is currently limited, this study addresses this challenge by creating a large and high-quality curated dataset. This dataset serves as a valuable resource for conducting comprehensive temporal analysis. Our analysis demonstrates that the empirical rule derived from historical data, yields a high precision value, and therefore highlights the effectiveness of our proposed approach as a valuable guideline and a feasible decision support system.