超高频信息流视角下宏观经济信息对ETF涨跌停的触发作用及制度有效性研究

随着我国A股市场发展不断成熟，ETF因其便捷性、投资分散化、强流动性、相对低成本等受到投资者热烈追捧，呈现持续爆发性发展态势。另一方面，大数据时代背景下信息的采集与传播达到空前的速度和广度，系统深刻地理解超高频宏观经济信息对ETF市场影响成为金融领域亟待解决的现实问题。本课题拟利用精确到毫秒的具有广覆盖面的宏观经济信息数据与高频交易数据，深入分析信息发布之后的市场质量变化，构建考虑宏观信息冲击的日内收益率预测面板回归模型；构建ETF价格涨跌停的Logistic模型，厘清不同属性的宏观经济信息对涨跌停的激发作用；检验涨跌停制度在不同信息环境下的效率。本课题研究不仅可拓展本领域的研究范围，为市场参与者配置资产、管理风险提供参考，也可为政府制定政策、加强监管提供决策依据，有助于我国资本市场稳定健康发展和提升资本市场对实体经济的支持质效。

ETFs provide convenient and low-cost opportunities to invest in liquid and diversified portfolios. Investors’ infectious enthusiasm for ETFs have been growing with the development in the A-share market. The ETF market has seen explosive growth. On the other hand, information collection and dissemination achieve unprecedented speed and breadth in the era of big data. An in-depth understanding of impacts of the ultra-high-frequency macroeconomic news on the ETF market becomes a burning question in finance. With a comprehensive, millisecond-stamped macroeconomic news pool and high-frequency trading data, this project aims to analyze the change of market quality after macroeconomic news releases and construct a panel regression model to explain intraday return predictability with news shocks considered. Furthermore, we divide macroeconomics news into several categories according to news properties and construct logistic models to investigate the impact of each category on price limit hits, and then investigate the efficiency of the price limit rule in various information environments. This project expands the research scopes and contributes to literature in this area. It is also valuable for market participants to allocate assets and manage risks. This project provides empirical evidence for regulators making policies in order to improve market stability and health, promote market development, and enhance the capital market’s support for entity economy.