基于文本分析的中国系统性金融风险研究

随着金融系统的复杂性和相互依赖性日益增强，精准度量系统性金融风险变得日益重要,大量基于量化数据的监测指标因此被开发。然而，统计数据的滞后性、统计敞口的片面性、监测截面的单一性，极大地限制了系统性风险的有效监测。而随着自然语言处理技术的发展，文本信息成为了量化数据的有效补充。新闻媒体作为市场的“信息池”，成为了风险信息提取的重要来源。
参考文本分析方法，本文首先采用词典法，通过构建“系统性金融风险词典”，囊括多类风险敞口，采用三阶段词频统计，拟合指数并对宏观指标进行预测。词典法指数有效预测了经济总量、流动性等指标，并对多个经济状况指标具备领先的预测效果。为减少词典法的主观性，本文采用词嵌入及卷积神经网络拟合风险指数。采用词嵌入对风险种子词进行上下文词义匹配，定位风险文章、拟合指数，该指数对宏观经济指标具备良好的预测性。随后本文使用三类预标注样本对比常见的人工智能算法（支持向量机、卷积、循环神经网络），遴选性能最优的模型拟合指数，指数预测性能进一步提升。为减少模型的主观性，本文进一步探索了使用非监督学习模型进行指标构建，有效丰富了风险指标工具库。
文本基于主观程度各异的新闻信息度量方法，构建了系统性金融风险指标体系。具备以下创新点：一，本文依据新闻文本构建了有别于传统依赖量化统计数据的风险测度指数，为金融风险的实时监控和预警提供了新的工具。二，本文所构建的指数可在截面对系统性风险进行细分部门的风险敞口度量，为实施针对性防治提供精准指导。三，本文对提供风险预测的媒体的异质性进行分析，揭示了不同媒体类型蕴含风险信息的差异。四，本文基于不同的人工智能模型，筛选了不同目标下的最优模型进行指数构建，形成了泛化能力各异的风险指标工具，对风险管理的理论和实践具备重要的启示和应用价值。五，本文所构建的系统性金融风险指标体系对宏观经济指标的广泛预测性为理解金融市场信息影响宏观经济与金融系统提供了新的理论视角，这为新闻媒体的金融市场影响机制做出贡献。

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