Good morning. Analysts and investors increasingly are using generative AI to review earnings calls, but new research suggests large language models (LLMs) may soon become powerful tools for stock selections.
For many years, financial sentiment analysis relied on simple word lists. Take for instance, on an earnings call, that would mean counting a CEO or CFO’s positive phrases like “strong growth” and negative ones like “unexpected losses”—to assign a sentiment score. This rules-based system was transparent and easy to explain.
LLMs, in contrast, interpret context and language structure, allowing them to recognize that phrases such as “growth slowed less than expected” are positive, despite negative words, according to a study by S&P Global Market Intelligence. The findings demonstrate that LLMs can extract insights from earnings call transcripts and convert them into actionable trading signals. These AI-driven signals closely match those from traditional, rules-based sentiment models, showing both methods measure the same underlying reality.
While LLMs are more complex and costly, the study found their fine-tuned strategies could have delivered double the excess return compared to traditional approaches—particularly as market inefficiencies shrink. For example, a long-short strategy using LLM-based signals achieved 8.4% annual returns, twice the performance of traditional benchmarks (4.2%), according to S&P Global Market Intelligence.
“The real edge is precision,” said Mengmeng Ao, quantitative research analyst at S&P Global Market Intelligence. “Lexicons do well at the headline level, but LLMs separate what’s material from what’s noise. That context is what investors care about.”
Other key findings: When LLMs flagged highly important financial events, sentiment signals delivered 6.4% excess annual returns—double those for medium-importance events (3.2%) and nearly four times those for low-importance events (1.7%).
It seems that LLM-driven investment strategies have the potential to reshape how the markets move.
Sheryl Estrada
sheryl.estrada@fortune.com
