- Highlights
- Delve beyond simplistic company-level peer groups to reveal product-level competitors.
- Create a robust framework for NLP-based analysis by integrating unstructured or alternative content associated with a particular product, service or brand.
- Uncover the true market landscape exposed through the interactions of companies’ complete multi-sector profiles.
- Feed Coverage
Region Count Type Start Date Africa 1,200 Entities 2012 Asia 24,000 Entities 2011 Europe 14,500 Entities 2011 Latin America 2,000 Entities 2009 Middle East 2,900 Entities 2010 North America 17,800 Entities 2003 Pacific 2,700 Entities 2014 - Feed Details
- RBICS with Tradenames maps over 170,000 products, services and brands (a.k.a. Tradenames) to the granular sectors of the RBICS taxonomy, resulting in a multi-sector participation map for each company. Whereas RBICS with Revenue offers quantified multi-sector exposure based on a company’s segment disclosures, RBICS with Tradenames provides a comprehensive view of sector participation at the product level.
- 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.
- Data/Documents
- 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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