英文标题：
《Predicting Distresses using Deep Learning of Text Segments in Annual
  Reports》
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作者：
Rastin Matin, Casper Hansen, Christian Hansen and Pia M{\\o}lgaard
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最新提交年份：
2018
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英文摘要：
  Corporate distress models typically only employ the numerical financial variables in the firms\' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors\' reports and managements\' statements. Our model consists of a convolutional recurrent neural network which, when concatenated with the numerical financial variables, learns a descriptive representation of the text that is suited for corporate distress prediction. We find that the unstructured data provides a statistically significant enhancement of the distress prediction performance, in particular for large firms where accurate predictions are of the utmost importance. Furthermore, we find that auditors\' reports are more informative than managements\' statements and that a joint model including both managements\' statements and auditors\' reports displays no enhancement relative to a model including only auditors\' reports. Our model demonstrates a direct improvement over existing state-of-the-art models.
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中文摘要：
公司困境模型通常只在公司年报中使用数字财务变量。我们开发了一个模型，该模型还使用了报告中的非结构化文本数据，即审计师报告和管理层声明。我们的模型由一个卷积递归神经网络组成，当与数值财务变量连接时，该网络学习适合于企业困境预测的文本描述。我们发现，非结构化数据在统计学上显著提高了困境预测的性能，特别是对于准确预测至关重要的大型公司。此外，我们发现，审计师的报告比管理层的声明更具信息量，与仅包含审计师报告的模型相比，包含管理层声明和审计师报告的联合模型没有显示出任何增强。我们的模型显示了对现有最先进模型的直接改进。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computation and Language        计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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