- Highlights
- Review comprehensive detail-level estimates and statistics from leading investment banks and research firms
- View estimate types for thousands of active and inactive global companies, including segment estimates and company-issued guidance and actuals, as well as commodities and sector-specific data
- Benefit from persistent analyst identification and tracking from broker to broker, including contact information where available
- Feed Coverage
Region Count Type Start Date Africa 1,350 Entities 2000 Asia 21,650 Entities 2000 Europe 15,120 Entities 1997 Latin America 740 Entities 2000 Middle East 1,330 Entities 2000 North America 16,540 Entities 2000 Pacific 2,340 Entities 2000 - Feed Details
- Updated intraday, the FactSet detail estimates feed provides individual broker-level estimates collected from over 800 sell-side analysts. This database contains 20+ years of broker history across more than 59,000 global companies. Data Frequency: Quarterly; Update Frequency: Intraday.
- Firm Information
- FactSet creates data and technology solutions for investment professionals around the world, providing instant access to financial data and analytics that investors use to make crucial decisions. We combine our unique proprietary datasets, your in-house data, and third-party unstructured data to help you see and seize opportunity sooner.
- Community Discussion
Today we would like to inform you that we have made a major update for our emotional data API and released version 3 in February 2019. Additionally, to general improvements of stability and redundancy we have added some new and very interesting data sets. For more information just visit our websi...
We conducted a survey on Alternative Data demand during the https://advantage.factset.com/factset-buy-side-forum-tokyo-2019 FactSet Buy-Side Forum Tokyo last week. Not surprisingly, ESG tops the list among 18 categories as Japanese financial institutions began integrating ESG investment strategie...
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Working with Japanese Earnings Call Transcripts: StanfordNLP What open-source packages are you using in the NLP space? I had always found Python’s NLTK package easy to use. A recent https://medium.com/analytics-vidhya/introduction-to-stanfordnlp-an-nlp-library-for-53-languages-with-python-code-d7...
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