Investing & Markets5.0 · 0 ratings

Central Bank Statement Parser

Decode a central-bank statement for hawkish/dovish shifts, forward guidance changes, and likely market reaction paths.

Role-BasedStructured-OutputChain-of-Thought

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. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique
Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

Learn this technique

Recommended models

claudegpt-4ogemini

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.

More in Investing & Markets