S&P Global and Snowflake bring AI-driven financial analysis to qualitative 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 called Lazy Prices, published by Harvard professor Lauren Cohen, which found that changes in the risk sections of SEC 10-K and 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 Market Intelligence LLC., this was true even without reading the text. The result was a research framework capable of identifying precisely what changed, whether a new risk was material, and which companies in a short portfolio warranted the position — with large language models doing the reading.
“We went and rebuilt that research with the help of Snowflake,” Hynes said. “With all the Snowflake Cortex and Snowflake CoWork tools, we were able to augment the alpha in that short book by removing the companies that didn’t have any risk and just concentrating on the companies that did have the risk.”
