英文标题：
《Machine Learning Risk Models》
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作者：
Zura Kakushadze and Willie Yu
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最新提交年份：
2019
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英文摘要：
We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.
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中文摘要：
我们给出了基于机器学习技术构建风险模型的显式算法和源代码。所得协方差矩阵不是因子模型。基于经验回溯测试，我们比较了这些机器学习风险模型与其他结构的性能，包括统计风险模型、基于基本行业分类的风险模型以及基于多级聚类的行业分类的风险模型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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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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