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    "story_id": "mp-2026-09-13-007",
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    "headline": "A Sentiment Score That Matched People Did Not Necessarily Rank Tomorrow",
    "slug": "a-sentiment-score-that-matched-people-did-not-necessarily-rank-tomorrow",
    "dek": "Five instruments on 70,500 messages separated semantic agreement from predictive ordering around securities lawsuits.",
    "summary": "Five instruments on 70,500 messages separated semantic agreement from predictive ordering around securities lawsuits.",
    "body_text": "Researchers linked 70,500 X messages about securities class actions from 2002–2025 to abnormal returns, then ran VADER, Loughran–McDonald, FinBERT, Twitter-RoBERTa and an LLM annotator through one pipeline. Human-label agreement aligned more closely with graded same-day associations under conventional sampling, while a fixed-size panel produced similar graded rank correlations at same-day and one-day horizons. Coarse predictive ordering remained weak, and message volume predicted neither market damage nor settlement size in a corpus with 17.6% spam. This observational result is not trading guidance.",
    "why_it_matters": "Five instruments on 70,500 messages separated semantic agreement from predictive ordering around securities lawsuits.",
    "limitations": [
      "This observational result is not trading guidance."
    ],
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    "modified_at": "2026-09-13T09:00:00.000-04:00",
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        "text": "Five instruments on 70,500 messages separated semantic agreement from predictive ordering around securities lawsuits.",
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        "qualification": "This observational result is not trading guidance."
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    "tags": [
      "financial NLP",
      "sentiment analysis",
      "evaluation"
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  "sources": [
    {
      "source_id": "source-2026-09-13-007",
      "title": "arXiv preprint 2609.11144",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.11144",
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    "name": "The Machine Press",
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  "cite_this_report": {
    "title": "A Sentiment Score That Matched People Did Not Necessarily Rank Tomorrow",
    "publisher": "The Machine Press",
    "published_at": "2026-09-13T09:00:00.000-04:00",
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