In order to identify novel qualitative determinants of transitions in sovereign credit ratings, we construct six different textual sentiment and subjectivity measures using dictionary-based, and machine learning approaches on sovereign credit rating reports issued by Moody’s and Fitch in the period from 2002 to 2017. After controlling for macroeconomic and fiscal strength, soft information, as well as known sources of proximity biases, we find that, on average, these novel text-based measures improve the classification accuracy of downgrades and upgrades. The improvement is more notable for sentiment than subjectivity measures, and for downgrades compared to upgrades. Next, we find evidence that credit rating agencies seem to follow the through-the-cycle rating philosophy by taking a longer horizon into account. Finally, to the best of our knowledge, we offer the most comprehensive analysis of textual sentiment measures and their effect on sovereign credit ratings thus far.

为了识别主权信用评级转变的新的定性决定因素，我们使用基于字典的机器学习方法构建了六种不同的文本情感和主观度量，用于穆迪和惠誉在 2002 年至 2017 年期间发布的主权信用评级报告。在控制宏观经济和财政实力、软信息以及邻近偏差的已知来源后，我们发现，平均而言，这些基于文本的新颖措施提高了降级和升级的分类准确性。与主观衡量相比，情绪的改善以及与升级相比的降级更为显着。接下来，我们发现证据表明信用评级机构似乎通过考虑更长的视野来遵循贯穿整个周期的评级理念。最后，