Earnings Call Transcript NLP Signal Research

When to Use

Use this skill in quantitative equity research, event-driven funds, and post-earnings announcement drift (PEAD) strategies. Prepared management remarks are scripted and reviewed by IR/legal; the analyst Q&A is spontaneous, and the tone gap between the two is the informative part. Price, Doran, Peterson & Bliss (2012, Journal of Banking & Finance 36, 992–1011) find that conference call tone predicts abnormal returns and that the Q&A portion carries incremental explanatory power for PEAD beyond the presentation section — that is the empirical basis for scoring the two sections separately rather than as one blob.

The module scores each section with the Loughran-McDonald (LM) financial lexicon, applies the LM negation rule, and reports Q&A Tone Divergence ($\Delta_{\text{tone}}$) and an Uncertainty Ratio.

When NOT to Use

• As a standalone trading signal. This is a research feature generator. It has no return model, no position sizing, and no calibration; combine it with an earnings-surprise and liquidity model before it sizes anything.
• On non-English transcripts, machine translations, or auto-generated ASR captions. LM is an English list built on US SEC filings; translation and transcription noise destroy the word counts.
• On any text that is not a segmented transcript — press releases, 8-Ks, prepared-only excerpts. Divergence is undefined without both sections.
• On very short or partially transcribed sections. The engine returns INSUFFICIENT_DATA rather than a tradable score; do not override that by lowering the sample floor to make a signal appear.
• For commercial use without an LM licence. The LM dictionary is free for academic research only (see Prerequisites).

Prerequisites

• Transcript already segmented into prepared_remarks_text and qa_session_text. This skill does not parse vendor transcript layouts.
• The Loughran-McDonald Master Dictionary (Positive, Negative, Uncertainty lists), loaded and passed to the constructor. The bundled DEFAULT_LM_* sets are a small verified subset (~40 words per category vs ~2,355 Negative / ~354 Positive / ~297 Uncertainty in the full lists) — they exist so the engine runs out of the box, and are not adequate for research.
  • Source: https://sraf.nd.edu/loughranmcdonald-master-dictionary/
  • Licensing: "The dictionary/sentiment lists are free for use in academic research." Commercial licences must be obtained from the authors.
• A timezone-aware transcript publication timestamp (not the call date) if the output feeds a backtest.
• Python 3.10+ standard library only. No NLP framework, model download, or third-party dependency is required.

Workflow

1. Transcript Section Parsing: Supply PREPARED_REMARKS and QA_SESSION text separately. If your vendor only delivers a single blob, segment it before calling — a combined score masks exactly the tone drop this skill measures.
2. Tokenization: Lower-case; contractions and hyphenated terms stay single tokens ("don't", "year-over-year") so the ratio denominator is not inflated by fragments.
3. Loughran-McDonald Matching with Negation: Count $N_{\text{pos}}, N_{\text{neg}}, N_{\text{uncert}}$. A positive word preceded within three tokens by a negator (no, not, none, neither, never, nobody) is reclassified as negative, per Loughran & McDonald (2011). The rule is applied to positive words only — double negation of negative words is rare in disclosure. LM categories overlap by design ("volatility" is both Negative and Uncertainty), so uncertainty is counted independently of polarity.
4. Sentiment & Divergence Computation:
   • $\text{Net Sentiment} = \dfrac{N_{\text{pos}} - N_{\text{neg}}}{N_{\text{pos}} + N_{\text{neg}}}$, defined as $0.0$ when the denominator is zero (no polarity evidence — not a neutral tone).
   • $\Delta_{\text{tone}} = \text{Net Sentiment}{\text{QA}} - \text{Net Sentiment}{\text{Prepared}}$.
   • $\text{Uncertainty Ratio} = \dfrac{N_{\text{uncert}}}{N_{\text{total words}}} \times 100%$.
   • overall_net_sentiment pools the raw counts of both sections; it is not the mean of the two section scores.
5. Sample-Sufficiency Gate: If either section falls below min_section_words (default 50) or min_polarity_terms (default 5), emit INSUFFICIENT_DATA and stop. A two-word "section" can otherwise produce a full-strength $\Delta_{\text{tone}} = -2.0$ off two lexicon hits.
6. Signal Classification (precedence order, first match wins):
   • INSUFFICIENT_DATA — sample gate failed.
   • BEARISH_QA_DIVERGENCE — $\Delta_{\text{tone}} <$ bearish_divergence_threshold (default $-0.15$).
   • BULLISH_EARNINGS_TONE — pooled net sentiment $>$ bullish_sentiment_threshold (default $0.40$) and pooled uncertainty ratio $<$ max_uncertainty_ratio_pct (default $1.5%$).
   • NEUTRAL — otherwise. Bearish divergence deliberately outranks bullish tone: glowing prepared remarks followed by a collapsing Q&A is the case this signal exists to catch.
7. Threshold Calibration: The four defaults above are plausible starting points, not empirically validated constants. Re-estimate them per universe (sector, market cap, transcript vendor) on out-of-sample data before trading.
8. Audit Report Generation: Output EarningsTranscriptAuditReport, carrying the timezone-aware publication timestamp for point-in-time alignment.

Full procedure: see references/workflows.md. Standards reference: see references/standards.md. Printable pre-flight checklist: see assets/checklist.md.

Common Pitfalls

• Using General-Purpose Dictionaries: General English lexicons (e.g. VADER, Harvard IV) score "liability", "cost", "capital" and "tax" as negative. Loughran & McDonald (2011) built the financial lists precisely because, in 10-Ks filed 1994–2008, "almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts."
• Hand-Rolling a "Loughran-McDonald-like" Word List: Plausible-sounding earnings vocabulary is often not in LM at all — growth, record, momentum, robust, expansion, profit and exceed are not LM Positive; headwind is not LM Negative. Worse, risk/risks are LM Uncertainty, not Negative: putting them in the negative list is the same general-purpose error, just self-inflicted. Verify every word against the Master Dictionary flags.
• Skipping the Negation Rule: Without it, "margins did not improve" and "we are not confident" score as positive statements. LM prescribe a three-word look-back window for negated positives.
• Ignoring Q&A vs Prepared Remarks Separation: Scoring the whole transcript as one document averages away the divergence and discards the section shown to carry the incremental PEAD information.
• Trading Tiny Samples: A truncated or partially-transcribed section yields extreme $\pm 1.0$ scores from one or two matches. Sentiment on a handful of polarity terms is noise, not tone.
• Treating Zero Sentiment as Neutral: A section with no lexicon hits scores $0.0$, which is identical to a perfectly balanced section. Check has_sufficient_sample before interpreting a zero.
• Transplanting Thresholds Across Universes: The $-0.15$ / $0.40$ / $1.5%$ defaults are illustrative. Tone distributions differ by sector, market cap, and transcript vendor; an uncalibrated threshold produces a signal that fires on style, not information.
• Look-Ahead Bias in Transcript Availability: Align on the exact transcript publication timestamp, not the call date, the earnings date, or a naive local datetime. Vendors typically publish a full transcript minutes-to-hours after the call ends; scoring it as of the call start trades on text that did not exist yet. The engine rejects timezone-naive timestamps for this reason.

Verification

• Instantiate EarningsTranscriptNlpEngine with the full LM lists. Submit a call with optimistic prepared remarks and a defensive Q&A: with prepared remarks at $+1.00$ (6 LM positives, 0 negatives) and Q&A at $-0.78$ (1 positive, 8 negatives), $\Delta_{\text{tone}} = -1.78$ and the engine emits BEARISH_QA_DIVERGENCE.
• Confirm analyze_text_section("QA_SESSION", "The quarter was not strong and demand did not improve.") returns positive_count == 0, negative_count == 2, negated_positive_count == 2.
• Confirm a one-word section pair returns INSUFFICIENT_DATA, not a bearish trade.
• Confirm a timezone-naive transcript_published_at raises ValueError.
• Run python -m unittest discover -s skills/earnings-call-transcript-nlp-signal-research/scripts.

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