Introduction to A-share listed company text statistics data

  Chinese listed companies publish daily market quotes on major market network platforms and establish a platform for interaction and communication among investors. **Investors from all over the country interact and post posts and replies on online stock exchange platforms of listed companies, forming a large amount of text corpus data related to Chinese stock market investment. **Researchers are able to observe investors' market judgments, personal emotions and other multi-dimensional information through large-scale investor interaction data.

 In recent years, domestic and foreign researchers have discovered:

Media text sentiment can more accurately measure changes in investor sentiment in my country's stock market, and has significant in-sample and out-of-sample prediction capabilities for my country's stock returns. Media text sentiment also has significant predictive ability for some important macroeconomic indicators, and has important academic and practical application value.

——Jiang Fuwei, Meng Lingchao, Tang Guohao: "Media Text Sentiment and Stock Return Forecast", "Economics (Quarterly)", Issue 04, 2021.

 For the convenience of scholars, CnOpenDatalisted A shares The company stock bar text data's posts and reply information tables have been statistically processed at the quantitative level and content level (sentiment analysis), including stock code, company name, posting time period, number of posts, proportion of positive and negative posts, Fields such as the number of positive and negative postsprovide high-quality data support for related research.

Data characteristics

• In terms of time, this data mainly covers stock forum postings and replies since 2008, and when counting, the posting and reply times are accurate to before the opening of the market, morning/afternoon trading Time, midday break, after closingFive different time periods;
• In terms of statistical sources, this statistical data involves a total of 350 million post subjects and a total of 650 million replies, which is a very large amount of text corpus data;
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• In terms of field richness, this data not only counts the post and reply information of each company in each time period, but is also based on Chinese Financial Sentiment Dictionary Data conducts sentiment analysis on each post and reply information, showing the positive, negative, and neutral performance of each company in each year. The number and proportion of posts and replies.

Data scale

Time interval

• Posting time: 1988-2022
• Reply time: 2007-2023

Field display

Sample data

Statistical data of text posted by A-share listed companies on the stock forum

Statistical data of reply text of A-share listed companies’ stock forum

Related literature

• Zheng Jiandong, Lu Xiaoliang, Lu Bin, Guo Feng, 2022: "Social media platform information interaction and capital market pricing efficiency - Evidence based on billion-level big data of stock forums", "Quantitative Economics and Technology Economic Research" No. 11.
• Yin Bichao, Kong Dongmin, Ji Mianmian, 2022: "Does Retail Activism Improve the Audit Quality of Listed Companies?" "Accounting Research" Issue 10.
• Fan Xiaoyun, Wang Yedong, Wang Daoping, Guo Wenxuan, Hu Xuanyi, 2022: "Heterogeneity Analysis of Information Content of Financial Texts from Different Sources—Based on Hybrid Text Sentiment Measurement Method", "Management World" Issue 10.
• Zhu Mengnan, Liang Yuheng, Wu Zengming, 2020: "Internet Information Interaction Network and Stock Price Collapse Risk: Public Opinion Supervision or Irrational Contagion", "China Industrial Economy" Issue 10.
• Sun Kunpeng, Wang Dan, Xiao Xing, 2020: "Internet Information Environment Regulation and the Corporate Governance Role of Social Media", "Management World" Issue 7.
• Wang Dan, Sun Kunpeng, and Gao Hao, 2020: "The Impact of "Voting with Your Mouth" on Social Media on Management's Voluntary Performance Forecasts", "Financial Research" Issue 11.
• Bu Hui, Xie Zheng, Li Jiahong, and Wu Junjie, 2018: "The Impact of Investor Sentiment on the Stock Market Based on Stock Reviews", "Journal of Management Science" Issue 4.
• Chang, Yen-Cheng and Hong, Harrison G. and Tiedens, Larissa and Wang, Na and Zhao, Bin, 2015, “Does Diversity Lead to Diverse Opinions? Evidence from Languages ​​and Stock Markets ”, Rock Center for Corporate Governance at Stanford University Working Paper No. 168, Stanford University Graduate School of Business Research Paper No. 13-16.
• Sheridan Titman, Chishen Wei, Bin Zhao, 2021, "Corporate actions and the manipulation of retail investors in China: An analysis of stock splits", Journal of Financial Economics.

Data update frequency

Annual Update

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