Momentum, Commentary Tone, and Earnings Surprise in LLM Financial Forecasts
What makes AI language models change their financial predictions
When researchers fed large language models synthetic earnings announcements with deliberately altered stock momentum, commentary tone, and earnings surprises, the models' forecasts shifted—but not always in ways that matched reality. The tone of non-factual commentary changed the models' answers more often than the actual earnings data did, suggesting these AI systems may be picking up on linguistic cues rather than financial fundamentals.
Financial institutions increasingly rely on AI to help forecast stock movements and earnings. If these models respond more strongly to how something is written than to what the numbers actually say, their predictions could systematically mislead investors or traders. This research exposes a blind spot: we need to know what language models are actually using to make financial decisions before we trust them with real money.