S&P Global | Data Science Solutions

We've made a name for ourselves with high quality, unique data sets for many use cases.

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S&P Global Ratings and Research covering more than 1 million government, corporate, financial sector and structured finance entities and securities.
Variety of data sets ranging from SEC filings and transcripts, to transactions and key executives.
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Compelling alternative data previously vetted and linked by our internal data experts.
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Link to a variety of company-level datasets including Corporate Relationships, Transactions, Transcripts, Financials, and Company Fundamentals.
Use multiple sector classifications and business criteria to screen, assemble and monitor entity sets.

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Our API Solutions enable you to integrate high-quality data with your systems, portals, and business applications.
Xpressfeed^TM, our powerful data feed management solution, injects unrivaled insight directly into your workflow.
Information delivered in packages, so you can structure and receive only the data you need.
Xpressfeed^TM Loader application has automatic schema generation to simplify maintenance.
Xpressfeed^TM Loader runs on your side, giving you greater control over when and how the data is processed.
Supports on premises and cloud instance of Microsoft SQL Server, PostgreSQL, MySQL and Oracle databases.

Transcripts + Artificial Intelligence
Research Team Harnesses the Power of Natural Language Process (NLP) to Create New Alternative Dataset

Given the growing interest in NLP among data scientists, the S&P Global Market Intelligence Quantamental Research team created this report to demystify many aspects of NLP and provide three illustrations, with accompanying Python code, of how NLP can be used in a unique way to create a new alternative dataset. For this specific work, NLP was used to quantify the sentiment of earnings calls. In the first example, sector-level sentiment trends were generated providing insights around inflection points and accelerations. The other two illustrations are: i) stock-level sentiment changes and forward returns, and ii) language complexity of earnings calls.

What is NLP? – We demystify common NLP terms and provide an overview of general steps in NLP.
Why is NLP important? – Forty zettabytes (10^21 bytes) of data are projected to be on the internet by 2020, out of which more than eighty percent of the data are unstructured in nature, requiring NLP to process and understand.
How can NLP help me? – We derive insights from earnings call transcripts via NLP measuring industry-level sentiment trends or language complexity of earnings calls, and much more.
Where do I start? – Code for each use case is enclosed, enabling users to replicate the sentiment analysis.

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