Partisan perception affects the actions of professionals in financial sector. Linking credit rating analysts to party affiliations from voter records, we show that not affiliated with U.S. president's downward-adjust corporate ratings more frequently. Since compare different covering same firm quarter, differences fundamentals cannot explain results. We also find a sharp divergence Democratic a...

Credit models are increasingly interested in not just the probability of default, but in what happens to a credit on its way to default. Attention is being focused on the probability of moving from one credit level, or rating, to another. One convenient way of expressing this information is through a transition matrix. The primary source for these probabilities has been the rating agencies. As ...

In credit modeling, default intensity is known to depend on rating history-specific factors, notably credit rating, but variation in aggregate default rates over time presumably also reflects changes in general economic conditions. We fit Cox intensity models for defaults, as well as for major upgrades and downgrades in credit rating, with both rating history-specific factors and a broad range ...

Fuzzy adaptive network (FAN) is proposed to help decision makers in credit scores and to assign the amount of loan. By combining with neural networks to incorporate the learning ability, FAN provides an alternative approach for the imprecision and fuzziness of the credit rating system. A loan approval example is given and the performance of FAN is compared with the regression algorithm. The res...

Giulia Mennillo’s Credit Rating Agencies provides an advanced introduction to the complex phenomenon of credit ratings. It up-to-date and meticulous information on rating agencies their regulatory context, it engages with some theoretical issues.