Research on human dynamics characteristics under large-scale stock data perturbation

Abstract

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.