The credit quality of an entity is essential information that reflects that entity’s financial health and its ability to meet debt obligations. Credit quality can be expressed as a credit score, but it is most explicit when expressed as a probability of default. These probabilities have many uses in finance — from meeting regulatory requirements to loan origination to portfolio construction.
In this paper, we delve into two different probability of default datasets, both available via Bloomberg. We compare the theoretical basis for their different approaches to measuring probability of default and show how these datasets can inform a sector-by-sector risk analysis as well as industry-specific deep dives.
Alternative data from Credit Benchmark
Available via Bloomberg’s Alternative Data solution and the Bloomberg Terminal, the Credit Benchmark dataset includes credit consensus ratings and analytics calculated based on contributed risk views from 40+ of the world’s leading financial institutions. Contributions are collected, anonymized, and updated twice monthly to provide an independent, real-world perspective of risk. The dataset encompasses a wide spectrum of close to 36,000 unique entities, over 75% of which are unrated by credit rating agencies, with history back to May 2015.
Bloomberg’s Default Risk Dataset
Bloomberg’s Default Risk dataset provides daily data for over 400,000 unique entities with history back to February 2014. This default risk data is mean to represent a snapshot view of an entity’s credit condition at different time horizons.
Different approaches to default probability
There are two main paradigms through which to view Default Probability: Through-the-Cycle (TTC) and Point-in-Time (PIT). The two datasets we study represent (to some extent) examples of these.
TTC focuses on a company’s long-term credit risk trend; the Credit Benchmark data is more closely aligned with this paradigm, and aims to strike a balance between stability and accuracy
Sector and Company Analysis
The Credit Benchmark dataset is useful in identifying long-term credit outlooks across industries; we show how we can use the data to quantify a differential impact of the shock from COVID-19 on various sectors. We also use Bloomberg’s Default Risk dataset to analyze three recent corporate defaults: J. C. Penney, Hertz and Wirecard.
Download the white paper to learn more about how to gain insights using the credit data available via Bloomberg.