Narrative evidence is not ground truth
News, filings and commentary reveal expectations and framing. NLP can group topics, score changes and surface contradictions, but it cannot make an unverified statement true.
Cross-validation, not automation
Narrative signals are compared with prices, volatility, option chains, event timing and source quality. A claim that lacks numerical or primary-source support remains a hypothesis.
Auditability matters
Prompts, source timestamps, transformations, model versions and human overrides should be reproducible. This reduces silent drift and makes it possible to explain why a research conclusion changed.
Research method
- Collect dated, attributable sources and preserve the original text or reference.
- Use NLP to extract entities, topics, sentiment shifts and conflicting statements.
- Cross-check material claims against filings, releases, market data and option-chain evidence.
- Log confidence, unresolved contradictions and human review before publication.
Boundaries and limitations
Language models can hallucinate, inherit source bias and change behavior across versions. Sentiment labels are context-sensitive, and access to more text does not eliminate missing or strategically framed information.
Research takeaway
The highest-value role for AI is disciplined evidence organization and contradiction detection, with traceable sources and human accountability at the decision boundary.
Related research
Research and technology implementation only. This is not financial, investment, or trading advice.