S&P Global and Snowflake bring AI-driven financial analysis to investment research
Historically, investment research involved human analysts spending days reading and evaluating massive financial reports. Now, AI-driven financial analysis is covering all of that work in just a few minutes.
The catalyst was a 2020 academic paper titled “Lazy Prices,” published by Harvard professor Lauren Cohen, which found that changes in the risk sections of SEC Form 10-K and Form 10-Q filings reliably predicted negative stock performance. According to Liam Hynes (pictured, right), Global Head of New Product Development for Public Markets at S&P Global, this was true even without reading the text. The resulting AI-driven financial analysis framework, built with Snowflake’s Cortex and CoWork tools, used large language models to identify precisely what changed.
“Anytime I’m engaging with the database, the large language model knows exactly where to go,” Hynes told theCUBE. “This is a question about revenue revisions for the energy sector. Well, that means that I come along and I can use one of the tools that Snowflake CoWork has, which is setting up a function, which is really like a stored procedure. So that means anytime I’m engaging with the database, the large language model knows.”
