Are Ratings the Worst Form of Credit Assessment Except for All the Others?
提出一个预测公司违约的模型，在理论上证明其优于评级、Altman Z值和Merton违约距离，实证也显示该组合方法预测能力最强，并给出周期调整后的预测。
We present a prediction model to forecast corporate defaults. In a theoretical model, under incomplete information in a market with publicly traded equity, we show that our approach must outperform ratings, Altman’s Z -score, and Merton’s distance to default. We reconcile the statistical and structural approaches under a common framework; that is, our approach nests Altman’s and Merton’s approaches as special cases. Empirically, the combined approach is indeed the most powerful predictor, and the numbers of observed defaults align well with the estimated probabilities. With a new transformation method, we obtain cycle-adjusted forecasts that still outperform ratings.