摘要翻译：
公司破产会对经济产生毁灭性的影响。随着越来越多的公司向海外扩张以利用国外资源，跨国公司的破产可能会扰乱世界金融生态系统。公司不会瞬间倒闭；对定性（如品牌）和定量（如计量经济学因素）数据的客观衡量和严格分析有助于识别公司的财务风险。随着最近通信和信息技术的进步，收集和存储关于公司的数据变得不那么困难了。剩下的挑战在于挖掘隐藏在大量数据下的公司健康状况的相关信息，并利用这些信息预测破产情况，以便管理人员和利益相关者有时间做出反应。近年来，机器学习因其在复杂模型学习方面的成功而成为大数据分析的热门领域。支持向量机、自适应boosting、人工神经网络和高斯过程等方法可用于识别数据中的模式（以高度的精确度），这些模式可能对人类分析人员不明显。本文分别从专家观点和财务措施两个方面对韩国和波兰制造业企业破产进行了研究。利用公开可用的数据集，几种机器学习方法被应用于学习公司当前状态与近期命运之间的关系。结果表明，当使用专家评估等信息特征时，使用任何机器学习技术都可以实现准确率大于95%的预测。然而，当单纯用财务因素来预测公司是否会破产时，相关性就不那么强了。
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英文标题：
《Analysis of Financial Credit Risk Using Machine Learning》
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作者：
Jacky C.K. Chow
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最新提交年份：
2018
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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英文摘要：
  Corporate insolvency can have a devastating effect on the economy. With an increasing number of companies making expansion overseas to capitalize on foreign resources, a multinational corporate bankruptcy can disrupt the world's financial ecosystem. Corporations do not fail instantaneously; objective measures and rigorous analysis of qualitative (e.g. brand) and quantitative (e.g. econometric factors) data can help identify a company's financial risk. Gathering and storage of data about a corporation has become less difficult with recent advancements in communication and information technologies. The remaining challenge lies in mining relevant information about a company's health hidden under the vast amounts of data, and using it to forecast insolvency so that managers and stakeholders have time to react. In recent years, machine learning has become a popular field in big data analytics because of its success in learning complicated models. Methods such as support vector machines, adaptive boosting, artificial neural networks, and Gaussian processes can be used for recognizing patterns in the data (with a high degree of accuracy) that may not be apparent to human analysts. This thesis studied corporate bankruptcy of manufacturing companies in Korea and Poland using experts' opinions and financial measures, respectively. Using publicly available datasets, several machine learning methods were applied to learn the relationship between the company's current state and its fate in the near future. Results showed that predictions with accuracy greater than 95% were achievable using any machine learning technique when informative features like experts' assessment were used. However, when using purely financial factors to predict whether or not a company will go bankrupt, the correlation is not as strong.
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PDF链接：
https://arxiv.org/pdf/1802.05326