A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model. (4th April 2022)

• Record Type: Journal Article
• Authors: Xu, Weijun; Fu, Zhineng; Li, Hongyi; Huang, Jinglong; Xu, Weidong; Luo, Yiyang
• Is Part Of: Statistical analysis and data mining. Volume 16:Number 1(2023)
• Page Start: 5
• Page End: 15
• Publication Date: 2022-04-04
• Subjects: COVID‐19 -- FTSE China A50 Index -- multiple ARMA -- stock forum -- textual sentiment; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11582
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: Coronavirus 2019 (COVID‐19) has caused violent fluctuation in stock markets, and led to heated discussion in stock forums. The rise and fall of any specific stock is influenced by many other stocks and emotions expressed in forum discussions. Considering the transmission effect of emotions, we propose a new Textual Multiple Auto Regressive Moving Average (TM‐ARMA) model to study the impact of COVID‐19 on the Chinese stock market. The TM‐ARMA model contains a new cross‐textual term and a new cross‐auto regressive (AR) term that measure the cross impacts of textual emotions and price fluctuations, respectively, and the adjacent matrix which measures the relationships among stocks is updated dynamically. We compute the textual sentiment scores by an emotion dictionary‐based method, and estimate the parameter matrices by a maximum likelihood method. Our dataset includes the textual posts from the Eastmoney Stock Forum and the price data for the constituent stocks of the FTSE China A50 Index. We conduct a sliding‐window online forecast approach to simulate the real‐trading situations. The results show that TM‐ARMA performs very well even after the attack of COVID‐19.