The group behavior of financial and economic systems is the driving force behind many complex economic phenomena. A quantitative understanding of the behavior of the financial and economic systems is an important research topic in modern behavioral science. However, most of the existing research uses mathematical statistics and other traditional methods to mine the characteristics of financial data. Few of them integrate the group behavior of the financial and economic system with high-frequency stock data. In this paper, we innovatively integrate human behavior dynamics indicators with financial analyses. Based on the dynamic factors of $215,029,800$ high-frequency financial data of 3364 stocks in 11 industries during the Sino-US trade war period, we construct a stock price volatility model to analyze the group behavioral characteristics of the stock market under different external disturbances. It is found that different types of stock prices and yields have different power rates and paroxysmal characteristics under different external disturbances. Meanwhile, the kinetic characteristics of stock returns are also significantly different. This paper applies the method of human group dynamics to study the evolution behavior of the financial system under external disturbance, which provides a new perspective for the study of stock price fluctuation and some practical guidance for the practical operation of financial risk management.

金融和经济系统的群体行为是许多复杂经济现象背后的驱动力。对金融和经济系统行为的定量理解是现代行为科学的一个重要研究课题。然而，现有研究大多采用数理统计等传统方法来挖掘金融数据的特征。其中很少有将金融和经济系统的群体行为与高频股票数据相结合的。在本文中，我们创新地将人类行为动态指标与财务分析相结合。基于中美贸易战期间11个行业3364只股票的高频财务数据 $215,029,800$ 动态因素，构建股票价格波动模型来分析股票市场的群体行为特征在不同的外界干扰下。研究发现，不同类型的股票价格和收益率在不同的外部扰动下具有不同的功率率和阵发性特征。同时，股票收益的动力学特征也存在显着差异。本文运用人类群体动力学的方法研究外部扰动下金融系统的演化行为，为股票价格波动研究提供了新的视角，也为金融风险管理的实际操作提供了一定的实践指导。