摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-关于机器学习的最新研究结果已经发表。根据中国人民共和国长沙的新闻报道，NewsRx编辑的研究表明：“我们研究金融科技和传统金融机构在正常和极端市场条件下的系统性风险。”本研究的资助机构包括湖湘市青年人才支持项目、国家自然科学基金(NSFC)、国家社会科学基金、湖南省自然科学基金。我们的新闻记者引用了湖南大学的研究，“我们使用机器学习(ML)技术(即随机森林和梯度提升回归树)来评估宏观经济变量、企业特征和网络拓扑作为系统风险驱动因素的作用，并用Shapley个人值和交互值进行基于ML-Based的解释。”我们发现：(i)市场波动率(MVOL)、个股波动率(IVOL)和市值(MC)是系统性风险的正驱动因素，而在正常市场条件下，高市盈率机构、大MC机构低IVOL在稳定市场方面发挥着至关重要的作用；(ii)宏观经济变量是最重要的外部系统性风险驱动因素，而公司特征在正常市场条件下更重要；(iii)IVOL与MC或MVOL之间的互动是极端系统性风险的重要来源，而MC是正常市场条件下最关键的互动属性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Changsha, People's R epublic of China, by NewsRx editors, research stated, "We study systemic risk dr ivers of FinTech and traditional financial institutions under normal and extreme market conditions." Financial supporters for this research include Huxiang Youth Talent Support Prog ram, National Natural Science Foundation of China (NSFC), National Social Scienc e Fund of China, Natural Science Foundation of Hunan Province. Our news journalists obtained a quote from the research from Hunan University, " We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm charact eristics, and network topologies as systemic risk drivers and perform the ML-bas ed interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; n amely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downsid e and upside) market conditions, while under normal market conditions, instituti ons with high price-earnings ratio, large MC, and low IVOL play an essential rol e in stabilizing markets; (ii) macroeconomic variables are the most important ex treme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions."
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