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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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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中文摘要：
公司破产可能对经济产生毁灭性影响。随着越来越多的公司在海外扩张以利用外国资源，跨国公司破产可能会破坏世界金融生态系统。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。随着通信和信息技术的发展，收集和存储公司数据的难度已经降低。剩下的挑战在于挖掘隐藏在大量数据下的有关公司健康状况的相关信息，并利用这些信息预测破产情况，以便管理者和利益相关者有时间作出反应。近年来，机器学习因其在学习复杂模型方面的成功而成为大数据分析中的一个热门领域。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（具有较高的精确度），这些模式可能对人类分析师来说并不明显。本文分别运用专家意见和财务指标对韩国和波兰制造业企业破产进行了研究。使用公开可用的数据集，应用了几种机器学习方法来了解公司当前状态与未来命运之间的关系。结果表明，当使用诸如专家评估之类的信息特征时，使用任何机器学习技术都可以实现精度大于95%的预测。然而，当使用纯粹的财务因素来预测一家公司是否会破产时，相关性并没有那么强。
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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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