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Whether you’re looking to generate alpha, conduct unique academic or investment research, or perform robust analysis — we have the right fundamentals for you.
Go deep with detailed industry data using SNL Sector Financials, broad leveraging wide-ranging, historical coverage from S&P Capital IQ, and comprehensive with point-in-time financials from CompustatⓇ.
Access financial statements, ratios, and multiples, plus press releases available within hours of publication, for timely analysis. Leverage click-through functionality and review detailed footnotes for each line item.
View true point-in-time analysis that helps you understand available data in a given point-in-time for more reliable multiples and valuation analysis.
Different industries report financial items in various ways. Leverage our standardized, industry-specific financial statement templates for in-depth comparisons.
We’ve won 8 Inside Market Data awards in 5 years, including Best Analytics Provider, Best Research Provider, and Best Counterparty Data Provider.
Whether your looking to generate alpha, conduct unique academic or investment research, or perform robust analysis — we have the right fundamentals for you.
Accessing the right information at the right time can mean the difference between success and failure. Our comprehensive data and powerful screening capabilities let you access the insights you need.
You can't make the right pitch without the right data. We deliver the data and insights you need to help grow your clients' business in a timely and cost-efficient manner.
Harness essential intelligence. Deliver with conviction. Develop strategies for strong alpha potential and unlock hidden value through a robust suite of data and analytics for your fundamental, quantitative, or quantamental approach.
This study from The University of Chicago Booth School of Business, investigates whether LLMs like GPT4 can analyze financial statements as effectively as professional analysts. Utilizing S&P Global's Compustat ® data, the study provided GPT4 with standardized, anonymous financial statements and tasked it with predicting future earnings directions. Impressively, without any further contextual information about the company other than the financial statement data, the LLM outperformed human analysts, even in situations where human analysts struggled. The research also found that the LLM's prediction accuracy rivals that of narrowly trained state-of-the-art machine learning models.
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