Quantitative signals from Corporate Calls, News & Social Media
NLP-derived signals from quarterly earnings call transcripts for quantitative trading strategies.
Monitors several language metrics for quarterly earnings call transcripts of 4,500+ US stocks. Provides additional building blocks for asset managers to build investment strategies based on alternative data.
Access information for over 1.7 million company corporate events. Transcript participants are mapped to FactSet symbology for historical analysis on firms, individuals, analysts, or brokers.
Actionable analytics derived from earnings calls and industry event transcripts. Pre-processed NLP signals ready for systematic trading strategies.
Sentiment scores derived from financial news sources using advanced NLP techniques.
Monitors public financial news for 6,000+ stocks from 2,000+ financial media sources in 33 languages. Measures financial sentiment, number of stories published, and buzz using advanced NLP.
Tickerized NLP sentiment scores from multiple news APIs for alpha generation and risk management. Designed specifically for quantitative trading applications.
Easy-to-use news for data analysis by analyzing materials disclosed by companies via TDnet and EDINET. Sentiment and event classification included.
Over 9,000 stories daily covering Latin America, Spain and Portugal. Includes sentiment analysis, trend detection, and predictive analytics capabilities.
Sentiment signals from Twitter, Reddit, stock forums and other social media platforms.
Sentiment analytics of news and social media generated by machine learning. Comprehensive coverage of social sentiment for equity markets.
Stay on top of retail investing trends. Valuable for risk management, avoiding overexposure to retail interest, and alpha generation from Reddit discussions.
500+ million comments from 12 million Chinese retail investors on the Guba stock forum. Unique insight into China A-share market sentiment.
Purchase intent and consumer sentiment data derived from social media for publicly-traded companies. Point-in-time historical data with daily updates.
Ready-to-query databases available in Kamba's Snowflake instance.
Social sentiment data with temporal data and quantitative measures for US equities. Daily time series format ready for backtesting.
Maps companies to securities (stocks, bonds) with identifiers from OpenFIGI and PermID. Essential for entity resolution and signal enrichment.
| Signal Type | Top Dataset | Provider | Coverage | Status |
|---|---|---|---|---|
| Earnings Calls | Brain Language Metrics on Earnings Calls | Brain | 4,500+ US Stocks | ✓ Quant-Ready |
| News Sentiment | Brain Sentiment Indicator on Stocks | Brain | 6,000+ Stocks / 33 Languages | ✓ Quant-Ready |
| Social Media | MarketPsych Analytics | LSEG | Global Equities | ✓ Quant-Ready |
| Reddit/Retail | Quiver WallStreetBets Feed | BattleFin | US Retail Stocks | ✓ Quant-Ready |
| Snowflake Native | SFACTOR Social Sentiment | S-Factor | US Equities | ✓ Quant-Ready |