The CnOpenData A-Share Listed Companies' Stock Bar Textual Database originates from East Money Net's Stock Bar, China's most influential stock investment exchange community. It systematically compiles investor discussions from as early as 1992 up to the latest updates. This database encompasses multi-dimensional data including posts, replies, user information, and social relationships, comprehensively documenting the sentiment fluctuations, information dissemination, and interactive behaviors of Chinese individual investors. It serves as an authoritative foundational data resource for observing retail investor sentiment in the A-share market and conducting textual analysis and behavioral finance research.
Data Uniqueness: Multi-Version Strategy Adaptable to Full-Scenario Research Needs
- Historical Snapshot Edition (2022 Edition, 2023 Edition) — Preserving Precious Early Data Legacy: Permanently preserves comprehensive stock bar data from 1992 to the collection period under early website structures. For instance, the 2022 Edition contains significantly higher post volumes (e.g., 15.30 million and 49.77 million posts) for years such as 2009 and 2015 than subsequent editions can retrospectively obtain. These data have become irreplaceable resources for studying the early market ecosystem.
- Annual Rolling Update Edition — Providing the Latest and Most Complete Current Data: Although this edition has limited retrospective access to early historical data, it undergoes annual rolling updates, offering a continuous and complete data stream from the starting year to the present. It is the preferred choice for recent event analysis and high-frequency in-depth research.
- Historical Full-Merge Edition — The Most Comprehensive and Longest-Spanning Ultimate Data Solution: Vertically integrates historical snapshot editions and rolling update editions through primary-key-based deduplication, forming a single dataset with "the longest time span and the most comprehensive data volume." The merged dataset contains 778 million posts and 1.52 billion replies, achieving seamless coverage from 1992 to the present.
- API Real-Time Update Edition — Direct Market Pulse Access Supporting Instant Decision-Making and Research: Provides real-time data streams with daily increments, ensuring researchers can capture minute-level and hour-level discussions and sentiment reactions to breaking news, financial reports, and policies. It is suitable for high-frequency trading strategy validation, real-time public opinion monitoring, and other frontier fields requiring extreme timeliness.
Data Completeness: Ultra-Long Time Series and Massive Scale Build a Solid Research Foundation
- Extremely Long Time Span: Data records of all editions start in 1992, continuously covering almost all key cycles and major events in the development of China's stock market up to the latest date. This enables multi-decade trend comparisons, cycle analysis, and policy effect evaluations.
- Massive Data Scale: Cumulatively integrates nearly 780 million posts and over 1.52 billion replies, involving more than 26 million users, constituting one of the world's largest Chinese financial social media textual databases. It sufficiently supports complex machine learning model training and large-sample statistical analysis.
- Continuous Annual Sequence: Since 2006, annual post and reply records range from hundreds of thousands to hundreds of millions, with no missing data in any year, ensuring the continuity of time series analysis.
Potential Application Scenarios:
- Investor Sentiment and Market Volatility Research: Stock bar posts and replies directly reflect retail investor sentiment fluctuations, enabling the construction of investor sentiment indices and analysis of their dynamic relationships with stock prices and trading volumes. This provides empirical evidence for behavioral finance.
- Information Dissemination and Market Efficiency Analysis: Stock bars often serve as "fermentation pools" for market rumors and policy interpretations. Analyzing textual propagation paths and network structures can study information diffusion efficiency, public opinion influence, and their impact on pricing processes.
- Corporate Governance and Event Studies: For major corporate events such as financial report releases, M&A, and executive changes, analysis can reveal correlations between discussion intensity, sentiment tendencies, and market reactions, providing references for corporate information disclosure and investor relations management.
- User Behavior and Social Network Research: Combining user profiles, follower relationships, and interaction data enables in-depth exploration of investor community structures, opinion leader influence, and group decision-making behaviors, expanding the application of sociology and communication studies in financial contexts.
The CnOpenData A-Share Stock Bar Textual Database redefines the depth and breadth of financial textual data resources with its epic time span, massive data scale, and meticulously designed multi-version system. Whether for historical retrospection, current insights, or future predictions, this database provides researchers with the most comprehensive, suitable, and reliable data support, serving as an indispensable core resource for exploring the behavioral logic of China's capital market.
Time Range
As of 2025 (real-time updates)
Data Scale
Field Display
Sample Data
A-Share Listed Companies' Stock Bar Post Details Table
A-Share Listed Companies' Stock Bar Reply Details Table
A-Share Listed Companies' Stock Bar User Details Table
A-Share Listed Companies' Stock Bar User Followers Table
参考文献
- 姜富伟、李梦如、孟令超，2024：《金融稳定沟通与银行系统性风险》，《世界经济》，2024第10期。
- 郑建东、吕晓亮、吕斌、郭峰，2022：《社交媒体平台信息交互与资本市场定价效率——基于股吧论坛亿级大数据的证据》，《数量经济技术经济研究》第11期。
- 尹必超、孔东民、季绵绵，2022：《散户积极主义提高上市公司审计质量吗》，《会计研究》第10期。
- 范小云、王业东、王道平、郭文璇、胡煊翊，2022：《不同来源金融文本信息含量的异质性分析——基于混合式文本情绪测度方法》，《管理世界》第10期。
- 朱孟楠、梁裕珩、吴增明，2020：《互联网信息交互网络与股价崩盘风险：舆论监督还是非理性传染》，《中国工业经济》第10期。
- 孙鲲鹏、王丹、肖星，2020：《互联网信息环境整治与社交媒体的公司治理作用》，《管理世界》第7期。
- 王丹、孙鲲鹏、高皓，2020：《社交媒体上“用嘴投票”对管理层自愿性业绩预告的影响》，《金融研究》第11期。
- 部慧、解峥、李佳鸿、吴俊杰，2018：《基于股评的投资者情绪对股票市场的影响》，《管理科学学报》第4期。
- Yuqin Huang, Feng Li, Tong Li, Tse-Chun Lin, 2024, “Local Information Advantage and StockReturns: Evidence from Social Media“. ContemporaryAccounting Research.
- 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 updates