When to Use
Use this skill when developing quantitative macro strategies, interest rate models, currency algorithms, or equity index overlays that react to central bank communications. Specifically, use this to:
- Quantify the Hawkish-Dovish stance of monetary policy statements, minutes, and press conference transcripts (Federal Reserve FOMC, ECB Governing Council, Bank of England MPC, Bank of Japan, Reserve Bank of India).
- Calculate Policy Surprise Deltas ($\Delta \text{Stance} = \text{Stance}t - \text{Stance}{t-1}$) and statement diffs (redlines) between consecutive meetings.
- Extract Monetary Policy Uncertainty and data-dependence hedging metrics to gauge policy trajectory ambiguity.
- Prevent negation leakage across sentence boundaries and accurately capture multi-word monetary policy collocations (e.g., "quantitative tightening", "downward pressure", "labor market slack").
When NOT to Use
- Corporate Earnings or 10-K Analysis: Corporate filings require broader corporate financial lexicons (Loughran-McDonald), where terms like "liability" or "restructuring" carry operational rather than monetary policy meaning (use
earnings-call-transcript-nlp-signal-research). - Informal Speeches Without Timestamps: Non-policy public commentary lacking synchronized release timestamps, which risks look-ahead bias and noise.
- Microsecond Macroeconomic Arbitrage: For trading immediate rate releases within sub-millisecond windows, hardware-accelerated direct data feeds (e.g. Bloomberg/Refinitiv machine-readable calendar feeds) should be used rather than raw unstructured text parsing.
- Languages Other Than English: The base engine is calibrated to English central bank releases; non-English central bank releases (e.g., BOJ Japanese text or PBOC Chinese text) require localized translation pipelines.
Prerequisites
- Parsed text of central bank communications stripped of website navigation boilerplate.
- Exact embargo release timestamps (e.g., 14:00:00 ET for FOMC Statement, 14:30:00 ET for Chair Press Conference).
- Historical archive of previous policy statements for calculating sequential deltas and statement redlines.
Workflow
- Text Ingestion & Embargo Verification: Ingest raw communication text and verify release timestamp matches the official exchange embargo time.
- Text Cleaning & Sentence Segmentation: Split text on sentence boundaries (
.,!,?,;,\n) to ensure negation scope is strictly isolated to individual sentences. Mask decimals ("0.25 percentage point", "2.5 percent") and dotted abbreviations ("U.S.", "e.g.") before splitting — their periods are not sentence boundaries. - Multi-Word Phrase Extraction (Collocations): Match atomic multi-word policy phrases (e.g., "quantitative tightening", "labor market slack", "rate hike", "price pressures") before evaluating single words to prevent term fragmentation.
- Sentence-Bounded Negation Resolution:
- For each phrase or word match, inspect preceding tokens within a local lookback window (default 3 tokens) strictly within the same sentence.
- Invert matched polarity when negated (e.g., "not ease" $\to$ Hawkish, "no further firming" $\to$ Dovish).
- Multi-Tier Stance Scoring:
- Compute Normalized Stance Score: $$\text{Net Score} = \frac{\text{Hawkish} - \text{Dovish}}{\text{Hawkish} + \text{Dovish}} \in [-1.0, 1.0]$$
- Compute Rhetoric Density per 1,000 words for Hawkish, Dovish, and Uncertainty terms.
- Sequential Statement Diffing (FOMC Redline):
- Compare current statement against the previous meeting's statement.
- Compute Policy Surprise Delta: $$\Delta \text{Score} = \text{NetScore}t - \text{NetScore}{t-1}$$
- Calculate lexical similarity (Jaccard and Cosine similarity) and identify newly added or dropped hawkish/dovish policy phrases.
- Signal Generation: Transmit the net score, surprise delta, and uncertainty index to macro execution and portfolio rebalancing engines.
Full procedure: see
references/workflows.md. Standards reference: seereferences/standards.md. Printable pre-flight checklist: seeassets/checklist.md.
Common Pitfalls
- Sentence Boundary Negation Leakage: Stripping punctuation and scanning tokens globally. A negation at the end of sentence 1 ("We will not ease.") must never negate a word at the beginning of sentence 2 ("Growth remains strong."). Sentence segmentation must precede tokenization.
- Trading Absolute Score Instead of Policy Surprise Delta: Central bank statements often exhibit persistent baseline tone (e.g., structurally hawkish during inflation shocks). Markets price in known stances; the true alpha signal is the delta ($\Delta \text{Score}$) from the previous statement.
- Timestamp Mismatch & Look-Ahead Bias: Aligning press conference remarks (which occur 30 minutes after statement release) or post-meeting minutes (released 3 weeks later) to the initial 14:00 statement bar.
- Generic Sentiment Dictionary Misclassification: Standard NLP dictionaries (e.g., VADER, general Loughran-McDonald) misclassify monetary terms—scoring "cut" or "slowdown" as generic negative sentiment rather than monetary accommodation (dovishness).
- Splitting Sentences on Decimal Points and Abbreviations: Central bank text is dense with figures ("0.25 percentage point", "2 percent objective") and abbreviations ("U.S."). A naive split on
.fragments the sentence mid-clause, truncates the negation lookback window, and can invert the stance: "the Committee sees no 0.25 percentage point hike" scores hawkish once "no" is stranded in a preceding fragment. - Ignoring Uncertainty / Hedging Language: Overlooking qualifying terms ("data-dependent", "highly uncertain", "conditional") which temper the conviction of forward guidance.
Verification
- Run test suite:
python -m unittest discover -s skills/central-bank-communication-nlp-analysis/scripts. - Validate repository compliance:
python tools/validate_skills.py(validates all skills; the script takes no per-skill flag). - Test sentence-boundary isolation with mock consecutive sentences, including sentences containing decimals and dotted abbreviations.
- Confirm non-text input raises rather than returning a neutral
0.0stance. - Verify statement diffing and policy surprise delta calculation against mock FOMC redlines.