Research on Listed Companies’ Credit Ratings, Considering Classification Performance and Interpretability

Zhe Li, Guotai Chi, Ying Zhou, Wenxuan Liu
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引用次数: 2

Abstract

Any credit evaluation system must be able not only to identify defaults, but also to be interpretable and provide reasons for defaults. Therefore, this study uses the correlation coefficient and F -test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining default status. We omit one feature in each iteration by replacing each feature, calculating the changes in validity index values after deleting this feature and, finally, calculating the ratio of the change value to the sum of all change values. This ratio is then used as the feature’s weight. This study also introduces a data gravity model in predicting defaults, as predicting a validation set’s default status derives the classification threshold to maximize classification accuracy. An empirical analysis of the listed company samples reveals that the feature system selected from 610 features can distinguish between both defaults and nondefaults. Compared with eight other models, our data gravity model not only exhibits good classification performance, but also has interpretability; further, this model can provide at least five-year-ahead forecasting, and can offer a timely risk warning for banks.
考虑分类绩效和可解释性的上市公司信用评级研究
任何信用评价系统都必须不仅能够识别违约,而且能够解释并提供违约的原因。因此,本研究采用相关系数和F检验对信用评价体系的初始特征进行选择,然后采用效度指标进行二次选择,以保证特征体系在判断违约状态时具有最优的判别能力。我们在每次迭代中省略一个特征,通过替换每个特征,计算删除该特征后有效性指标值的变化,最后计算变化值与所有变化值之和的比值。然后将这个比率用作特征的权重。本研究还引入了预测默认值的数据重力模型,因为预测验证集的默认状态会派生分类阈值以最大化分类精度。对上市公司样本的实证分析表明,从610个特征中选取的特征系统可以区分违约和非违约。与其他8种模型相比,我们的数据重力模型不仅具有良好的分类性能,而且具有可解释性;此外,该模型可以提供至少未来五年的预测,并可以为银行提供及时的风险预警。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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