Morningstar has the resources and experience to execute for our issuer clients. Morningstar has created a transparent framework for our credit ratings built from four quantitative and qualitative metrics—Business Risk, Morningstar® Cash Flow Cushion™ Morningstar® Solvency Score™ and Distance to Default. Our openness allows issuers to know where they stand because they understand our well-defined methodology and their rating. And our Methodology is applied consistently across all industries and sectors.
Standing behind our research is an experienced and seasoned team of credit analysts with a deep understanding of all aspects of corporate finance, including credit modeling, fundamental credit risk analysis, industry analysis and legal and structural concepts. We build our analytical teams by taking advantage of diverse backgrounds, which allows our clients to benefit from the experience gathered at rating agencies, banks, fund management companies, insurance companies, and legal firms.
Since 2010, Morningstar has been providing insightful corporate credit research that resonates with investors. Hundreds of Institutional Investors and Advisors access our analysis and are familiar with and trust our credit research and opinions.
Our transparent framework is built from four quantitative and qualitative metrics—Business Risk, Morningstar® Cash Flow Cushion,™ Morningstar® Solvency Score,™ and Distance to Default. The first, business risk, is qualitative in nature and depends on Morningstar’s assessment of competitive advantages. The latter three are primarily quantitative. The Cash Flow Cushion is based on our forward looking view of cash flow generation as compared to future obligations. Solvency Score is based on a company’s financial ratios and Distance to Default is a market-driven measure of financial health and distress. All of these metrics are utilized to measure credit risk and are encapsulated within the Morningstar® Corporate Credit Rating™ Uncertainty Assessment, and Stewardship Grade. For each of these four categories, we assign a score, which we then use to translate into a scale of Very Good / Good / Fair / Poor / Very Poor.
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