A study of the impact of COVID-19 on the Chinese stock market based on a new textual multiple ARMA model.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2022-04-04 DOI:10.1002/sam.11582

Weijun Xu, Zhineng Fu, Hongyi Li, Jinglong Huang, Weidong Xu, Yiyang Luo

{"title":"A study of the impact of COVID-19 on the Chinese stock market based on a new textual multiple ARMA model.","authors":"Weijun Xu, Zhineng Fu, Hongyi Li, Jinglong Huang, Weidong Xu, Yiyang Luo","doi":"10.1002/sam.11582","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111149/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Analysis and Data Mining","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/sam.11582","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

基于一个新的文本多重ARMA模型的COVID-19对中国股市影响研究

2019冠状病毒（COVID-19）导致股市剧烈波动，并在股票论坛上引发激烈讨论。任何特定股票的涨跌都会受到许多其他股票和论坛讨论中表达的情绪的影响。考虑到情绪的传递效应，我们提出了一个新的文本多重自回归移动平均（TM‐ARMA）模型来研究新冠肺炎对中国股市的影响。TM‐ARMA模型包含一个新的跨文本术语和一个新交叉自回归（AR）术语，分别测量文本情绪和价格波动的交叉影响，并动态更新测量股票之间关系的相邻矩阵。我们通过基于情感字典的方法计算文本情感得分，并通过最大似然方法估计参数矩阵。我们的数据集包括东钱证券论坛的文本帖子和富时中国A50指数成分股的价格数据。我们采用滑动窗口在线预测方法来模拟真实的交易情况。结果表明，即使在新冠肺炎发作后，TM‐ARMA也表现良好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.