论文标题
深度神经网络的应用评估公司信用评级
Application of Deep Neural Networks to assess corporate Credit Rating
论文作者
论文摘要
最近的文献实施了机器学习技术，以根据财务报表报告评估公司信用评级。在这项工作中，我们分析了四个神经网络体系结构（MLP，CNN，CNN2D，LSTM）的性能，以预测标准和穷人发行的公司信用评级。我们从我们的能源，金融和医疗保健领域分析公司。分析的目的是改善机器学习算法在信用评估中的应用。为此，我们专注于三个问题。首先，我们调查使用选定的功能子集时该算法是否表现更好，或者是否更好地允许算法选择自己的功能。其次，财务数据中固有的时间方面对于通过机器学习算法获得的结果很重要？第三，是否有特定的神经网络体系结构在输入功能，扇区和保留集方面始终如一地优于其他人？我们创建了几个案例研究来回答这些问题并使用ANOVA和多重比较测试程序分析结果。
Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selected subset of features, or if it is better to allow the algorithms to select features themselves. Second, is the temporal aspect inherent in financial data important for the results obtained by a machine learning algorithm? Third, is there a particular neural network architecture that consistently outperforms others with respect to input features, sectors and holdout set? We create several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedure.
