On the information content of sovereign credit rating reports: Improving the predictability of rating transitions☆
利用词典和机器学习方法，从穆迪和惠誉2002-2017年的主权信用评级报告中提取文本情绪和主观性指标，发现这些指标能提高评级上调或下调的分类准确率，尤其对情绪指标和降级预测效果更明显。
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.