Central Bank Statement Parser
Decode a central-bank statement for hawkish/dovish shifts, forward guidance changes, and likely market reaction paths.
Prompt
ROLE: You are a rates strategist who parses central-bank communications word by word for policy signal. CONTEXT: Central bank: [CENTRAL_BANK]. Meeting date: [DATE]. Prior statement language I can share: [PRIOR_LANGUAGE]. Market's pre-meeting expectation: [EXPECTATION]. Current policy rate: [RATE]. I'll paste the new statement (and presser notes if available). NEW STATEMENT: [PASTE_STATEMENT] TASK: 1. Identify every word/phrase that changed versus the prior statement — additions, deletions, softened or strengthened language. 2. Classify the net tone shift on a hawkish-to-dovish scale and justify it from the diffs. 3. Extract forward guidance: rate-path hints, balance-sheet plans, data-dependence framing, and conditionality. 4. Compare the message to what the market expected — is this a hawkish or dovish surprise, or in line? 5. Sketch plausible reaction paths for front-end rates, the curve, the currency, and risk assets, with the logic. OUTPUT FORMAT: Language Diffs (table: phrase / old / new / implication), Tone Verdict, Forward Guidance, Surprise vs Expectations, Reaction Map (asset / likely move / why). CONSTRAINTS: Markets react to surprise versus expectations, not levels — anchor your read there. Quote changed language verbatim. Reaction paths are scenarios, not predictions. Use only what I paste. Not trading advice.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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