[论文解读] Analysis of Financial Credit Risk Using Machine Learning

Jacky Chow|arXiv (Cornell University)|Jan 1, 2017

Financial Distress and Bankruptcy Prediction参考文献 3被引用 18

一句话总结

本硕士论文评估了用于预测韩国和波兰制造业企业破产的机器学习模型，采用财务比率和专家评估。研究发现，结合定性专家判断可使准确率超过95%；而纯财务模型由于维度灾难的限制，表现受限，逻辑回归在高维波兰数据上的准确率为66.4%。

ABSTRACT

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.

研究动机与目标

• 评估用于预测制造业企业破产的机器学习模型。
• 比较不同机器学习技术在财务和定性数据上的预测性能。
• 研究数据维度对模型泛化能力和准确率的影响。
• 评估降维与特征选择在提升模型稳健性方面的有效性。
• 为基于数据驱动方法的财务风险管理提供实际洞见。

提出的方法

• 应用多种机器学习模型：逻辑回归、支持向量机（SVM）、决策树、AdaBoost、人工神经网络（ANN）和高斯过程。
• 使用来自UCI机器学习存储库的公开可用数据集，涵盖韩国和波兰的企业破产案例。
• 执行数据预处理，包括缺失值处理和特征缩放。
• 采用主成分分析（PCA）及其他降维技术以应对高维数据。
• 通过交叉验证选择模型，并利用准确率、精确率、召回率和F1分数评估性能。
• 将专家评估作为定性特征整合，以增强预测能力。

实验结果

研究问题

• RQ1 仅使用财务比率时，机器学习模型在预测企业破产方面的准确率如何？
• RQ2 整合专家评估对破产预测准确率有何影响？
• RQ3 维度灾难如何影响高维财务数据集中模型的性能？
• RQ4 在破产预测任务中，哪种机器学习模型在泛化能力和稳健性方面表现最佳？
• RQ5 降维技术在稀疏高维数据上的模型性能提升程度如何？

主要发现

• 将专家评估纳入后，机器学习模型在预测韩国企业破产时准确率超过95%。
• 在波兰数据集上，仅使用财务数据的模型中，逻辑回归的最高准确率为66.4%，表明其预测能力有限。
• 高维财务数据导致复杂模型因维度灾难而出现过拟合，降低泛化能力。
• 降维技术（如PCA）提升了模型的稳定性和性能，尤其对非线性模型效果更显著。
• 在高维稀疏数据集上，逻辑回归等简单模型的表现优于复杂模型。
• 本研究证实，定性因素能显著提升预测准确率，即使在财务数据本身相关性微弱的情况下亦然。