While investors and lenders rely on one or more external credit rating agencies (eg, EthiFinance, Standard & Poor’s, Moody’s or Fitch Ratings) to directly assess their obligors’ credit quality, or simply to benchmark their own estimates, part of their portfolio might not be covered by these credit assessment institutions. Consequently, some practitioners estimate obligors’ probability of default using models derived from Merton’s classic 1974 model and then translate them into market-implied ratings. These measures are highly volatile and sensitive to overall market movement; that is, probabilities of default are high in a bear market and low in a bull market, whereas an obligor’s actual credit quality might stay relatively stable. In this paper we propose an alternative approach based on Merton’s distance to default. We compute the distance to default from market data and transform it into a distance-to-default-based rating for each firm listed on the stock market. Further, we use a machine learning algorithm to link the firm’s distance to default and sector to its credit rating. Ultimately, this approach allows us to assign a distance-to-default-based rating to unrated firms. To show the relevance of this method, we compare the explanatory power of the model’s equity-implied ratings with those of actual credit ratings in relation to the interest/debt ratio, and we find similar results (ie, a coefficient of determination of 87% in both cases). This demonstrates that our model explains the interest rates paid as well as the credit ratings.