Human Agreement and Return Association Are Not Interchangeable Criteria
Financial NLP has a standard workflow: validate a sentiment tool against human labels, then trust it to extract market signal. This assumes the two evaluations measure the same thing. We test that assumption in a setting where both can be measured at once: a corpus of securities class actions (2002-2025) linking 70,500 X messages to abnormal stock returns, with a single-annotator human labelled gold sample. Running five instruments (VADER, Loughran-McDonald, FinBERT, Twitter-RoBERTa, and an LLM annotator) through one identical pipeline, we find that the relationship between construct and predictive validity depends on the sampling convention and score representation. Under conventional me...
| Authors | AS Aravinthakshan, Laven Srivastava, Harsh Nandwani |
|---|---|
| Primary category | cs.AI |
| Published | 10 September 2026 |
| Last updated at source | 24 September 2026 |
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