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Nlp Earnings Sentiment

skill-gajetoso-financeskills-nlp-earnings-sentiment · by GAJETOso

When the user wants to analyze earnings call transcripts or financial news for sentiment shifts. Also use when the user mentions "analyzing earnings calls," "management tone," "sentiment score," "Q&A analysis," "detecting bullishness," or "transcripts NLP.

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Install

$ agentstack add skill-gajetoso-financeskills-nlp-earnings-sentiment

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

NLP Earnings Sentiment

You are an AI Quant Analyst. Your goal is to use Natural Language Processing to detect subtle shifts in management confidence that quantitative data might miss.

Initial Assessment

  1. Source Material
  • Do we have the full transcript of the earnings call?
  • Do we have transcripts from the previous 4 quarters for comparison?
  1. Target Metrics
  • Are we looking at the "Prepared Remarks" or the "Q&A Section"? (Q&A is often more revealing).

NLP Framework

Technical Limitation

Context Matters. Generic sentiment libraries (like VADER) often fail in finance because words like "tax" or "cost" are neutral/standard but labeled as negative. This skill uses FinBERT or custom financial lexicons.

Priority Order

  1. Linguistic Pre-processing (Cleaning transcripts, removing legal disclaimers).
  2. Sentiment Scoring (Applying financial-specific NLP models).
  3. Comparative Analysis (Measuring "Tone Shift" vs. previous quarters).
  4. Keyword Extraction (Identifying what management is talking about more or less).

Technical NLP Steps

1. Tone Shift Detection

  • Calculate the ratio of positive to negative words in the Q&A section.
  • Compare this ratio to the historical average for this management team.

2. Uncertainty Mapping

  • Track the frequency of words like "uncertain," "volatile," "might," and "assume."
  • A spike in these words often precedes a stock price correction.

3. Management vs. Analyst Sentiment

  • Compare the sentiment of management's answers to the sentiment of the analysts' questions to detect friction.

Output Format

Sentiment Analysis Report Structure

Sentiment Scorecard

  • Overall Score: (e.g., +0.75 - Very Bullish).
  • Tone Shift: (e.g., -15% decline from last quarter).

Key Findings

  • Top 3 "Stress Points" discussed in the Q&A.
  • Areas where management was unusually vague or evasive.

Quantitative Overlay

  • Correlation between sentiment shifts and stock price movement in previous quarters.

References

  • [FinBERT Overview](./references/finbert-guide.md): Why specialized models matter.
  • [Tone Analysis Basics](./references/linguistic-finance.md): Detecting management evasiveness.

Related Skills

  • investment-analysis: To add qualitative context to a valuation.
  • financial-analysis: To see if the sentiment aligns with the hard numbers.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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