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摘要翻译：
随着互联网金融的快速发展，大量研究表明，互联网金融平台在受到宏观经济冲击或脆弱的内部危机时，具有不同的金融系统性风险特征。本文从互联网金融区域发展的角度，运用t-SNE机器学习算法，获得了涉及31个省份、335个城市和地区的中国互联网金融发展指数数据挖掘。得出峰值和厚尾特征，进而提出互联网金融系统性风险的三种分类，为互联网金融系统性风险提供更具地域性针对性的建议。
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英文标题：
《Systemic Risk Clustering of China Internet Financial Based on t-SNE
Machine Learning Algorithm》
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作者：
Mi Chuanmin, Xu Runjie, Lin Qingtong
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最新提交年份：
2019
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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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英文摘要：
With the rapid development of Internet finance, a large number of studies have shown that Internet financial platforms have different financial systemic risk characteristics when they are subject to macroeconomic shocks or fragile internal crisis. From the perspective of regional development of Internet finance, this paper uses t-SNE machine learning algorithm to obtain data mining of China's Internet finance development index involving 31 provinces and 335 cities and regions. The conclusion of the peak and thick tail characteristics, then proposed three classification risks of Internet financial systemic risk, providing more regionally targeted recommendations for the systematic risk of Internet finance.
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PDF链接：
https://arxiv.org/pdf/1909.03808
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