CHINESE STOCKS
This unique dataset tracks and quantifies the impact of social media on Chinese stocks and generates ALPHA providing a detailed analysis of activities of influencers, financial professionals, retail investors, and bots across Social Media platforms.
Datasets -> Chinese Stocks
Introduction
The dataset includes sentiment scores derived from social media content related to Chinese stocks. The output provides time series sentiment data, which allows users to track sentiment over time, including historical trends. This aspect is crucial for analyzing how sentiment correlates with market movements. ZENPULSAR's methodology includes identifying content from influencers, financial professionals, retail investors, and bots. The output might differentiate these sources, providing insights into how different types of social media participants influence sentiment.
Data Summary
Start Date: December 2003
Asset Coverage: 5742 Chinese Stocks
Delivery Method: API or BIGQUERY
Resolution: 15 Minutes
Source List
The dataset includes sentiment scores derived from social media content related to Chinese stocks. The output provides time series sentiment data, which allows users to track sentiment over time, including historical trends. This aspect is crucial for analyzing how sentiment correlates with market movements. ZENPULSAR's methodology includes identifying content from influencers, financial professionals, retail investors, and bots. The output might differentiate these sources, providing insights into how different types of social media participants influence sentiment.
Data Summary
Start Date: December 2003
Asset Coverage: 5742 Chinese Stocks
Delivery Method: API or BIGQUERY
Resolution: 15 Minutes
Source List
- Ptt.cc
- Seeking Alpha
- Telegram
- Youtube
- Sina Weibo
- Guba.Eastmoney
- JRJ.com
- Yicai.com (China Business Network)
- Shanghai Securities Journal
- ETNet
- Finet HK
- Finance.sina
- Mingpao.com
Pulse Sentiment
This dataset offers sentiment scores from the complete asset universe-related social media, enabling tracking of sentiment over time and analysis of its correlation with market movements, with insights into how influencers, financial professionals, investors, and bots impact sentiment. Request ->
Dominating Sentiment
This dataset analyzes sentiment from social media, tracking trends over time and correlations with market movements. Metrics like Dominating sentiment provide nuanced views of overall sentiment direction, considering bullish, bearish, and neutral mentions. Request ->
Influencers
This dataset analyzes the impact of key opinion-makers on retail investors and sentiment. It gathers data from over 10 million social media accounts, offering insights into topics covered by influencers, including topic popularity within specific influencer groups. Request ->
News and Reactions
This dataset focuses on trending financial articles among influencers and professional investors, capturing social media momentum and providing early market signals. With a 99% data accuracy rate and specialized financial asset focus, it offers comprehensive insights through news sentiment and social media reactions. The dataset includes news text, source, sentiment, and labels like virality and popularity among professionals. Request ->
Oracle
This dataset provides detailed sentiment analysis and classifying mentions into facts, opinions, or predictions. It offers comprehensive insights including sentiment scores (neutral, bullish, bearish), asset codes, and fact_level labels indicating mention nature (fact, opinion, prediction) from different data sources. Request ->
This dataset offers sentiment scores from the complete asset universe-related social media, enabling tracking of sentiment over time and analysis of its correlation with market movements, with insights into how influencers, financial professionals, investors, and bots impact sentiment. Request ->
Dominating Sentiment
This dataset analyzes sentiment from social media, tracking trends over time and correlations with market movements. Metrics like Dominating sentiment provide nuanced views of overall sentiment direction, considering bullish, bearish, and neutral mentions. Request ->
Influencers
This dataset analyzes the impact of key opinion-makers on retail investors and sentiment. It gathers data from over 10 million social media accounts, offering insights into topics covered by influencers, including topic popularity within specific influencer groups. Request ->
News and Reactions
This dataset focuses on trending financial articles among influencers and professional investors, capturing social media momentum and providing early market signals. With a 99% data accuracy rate and specialized financial asset focus, it offers comprehensive insights through news sentiment and social media reactions. The dataset includes news text, source, sentiment, and labels like virality and popularity among professionals. Request ->
Oracle
This dataset provides detailed sentiment analysis and classifying mentions into facts, opinions, or predictions. It offers comprehensive insights including sentiment scores (neutral, bullish, bearish), asset codes, and fact_level labels indicating mention nature (fact, opinion, prediction) from different data sources. Request ->
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