Same Day, Same Story; One Day Ahead, a Different Signal: The Dual Validity of Financial Sentiment

Abstract: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 method-specific sampling, human agreement aligns more closely with graded same-day associations than with one-day leads. On a fixed-n panel, however, agreement has similar graded rank correlations at both horizons, while the coarse ordering remains weak. Benchmark agreement therefore establishes semantic validity but does not by itself determine predictive rankings. In a conversation that is 17.6% spam, message volume predicts neither market damage nor settlement size.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Social and Information Networks (cs.SI)

arXiv:2609.11144 [cs.AI]

(or arXiv:2609.11144v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.11144 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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