LLM-extracted sentiment matters for trading, but how you separate firm-specific signals from macro-driven ones, and which allocation method you choose, matters just as much as which sentiment model you use.
This paper shows how large language models can improve small-cap stock trading by extracting sentiment from financial news and combining it with macro indicators and technical signals. Rather than treating portfolio risk as fixed, the authors feed model-predicted risk (split into two types of uncertainty) directly into portfolio construction.