CnOpenData's Snowball Listed Company Trading Database comprehensively records investor posts and interaction information within individual stock trading boards. It encompasses fields including user nickname, post content, source, stock code and name, posting time, engagement metrics (likes, reposts, views, replies), external links, and mapped related stocks. These data provide a rich informational foundation for studying market sentiment, investor behavior, and listed company dynamics, holding significant academic and commercial value.
Data Uniqueness:
- Investor Behavior Data Focused on Trading Scenarios: Unlike announcements or news-based information, this database concentrates on real-time discussions by investors within trading boards, offering more direct insights into the sentiment and perspectives driving market trading behavior.
- Quantitative Records of Engagement: The system captures multi-dimensional metrics such as likes, replies, reposts, and views, providing data support for modeling investor attention and information diffusion.
- Precise Company-Level Mapping: Each content entry is bound to specific security codes, enabling cross-temporal and cross-firm comparative analysis, making it suitable for research on correlations between investor behavior and company characteristics.
Data Application Value:
- Investor Sentiment Research: By integrating natural language processing with quantitative engagement metrics, investor sentiment indices can be constructed to validate correlations between sentiment and stock prices/trading volumes.
- Information Diffusion and Market Microstructure Analysis: Leveraging engagement behaviors to explore information propagation pathways among investor groups and its potential impact on market fluctuations.
- Corporate Sentiment and Risk Monitoring: Provides listed companies and research institutions with investor perspective-based sentiment data, aiding in identifying potential risk signals and shifts in market expectations.
CnOpenData's Snowball Listed Company Trading Data serves as a high-value micro-level data source for researching investor behavior, market sentiment, and corporate sentiment. Its unique data context and engagement dimensions make it widely applicable in finance, behavioral economics, and risk management.
Time Coverage
Through September 2025
Field Display
Sample Data
Data Update Frequency
Annual update