论文标题
每个公司都拥有其形象：通过卷积神经网络的公司信用评级
Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
论文作者
论文摘要
信用评级是对与公司相关的信用风险的分析，这反映了投资的风险和可靠性水平。有许多研究实施机器学习技术来处理公司信用评级。但是，这些模型的能力受到财务报表报告中大量数据的限制。在这项工作中，我们分析了传统机器学习模型在预测公司信用评级时的性能。为了利用强大的卷积神经网络和庞大的财务数据，我们提出了一种新颖的端到端方法，即通过卷积神经网络，CCR-CNN的企业信用评级，以使其简洁。在拟议的模型中，每个公司都转化为图像。基于此图像，CNN可以捕获数据的复杂特征相互作用，这很难由以前的机器学习模型揭示。我们构建的中国公共列出的公司评级数据集进行了广泛的实验，证明CCR-CNN的表现始终超过最先进的方法。
Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.
