AI analysis of central bank statements should answer three questions: what changed, what those changes imply for policy, and what remains uncertain. A polished summary is useful, but a traceable comparison with the previous announcement is more useful.
For traders and analysts, the practical goal is to turn policy documents into a reviewable research brief. Keep the announced decision, the model’s interpretation and any proposed market response separate. A confident paragraph should never become an order ticket by accident.
Start With the Full Policy Release
Build the analysis around official documents rather than headlines. For US monetary policy, the Federal Reserve’s FOMC meeting archive separates statements, implementation notes, press conferences, projection materials and later minutes. Those documents belong to the same policy discussion, but they do not all become available at the same time.
Store each document with its publication time, retrieval time, meeting date and document type. Preserve the original text alongside the cleaned version. When evaluating an announcement, allow the model to use only material available at the chosen decision time.
Use the same discipline for the European Central Bank, Bank of England and other institutions. Establish a separate document map for each bank rather than assuming their publication formats match. Keep committee decisions apart from individual officials’ speeches, and label prepared remarks separately from questions and answers.
Compare Changes, Not Just Hawkish or Dovish Words
Configure the model to evaluate policy stance rather than ordinary positive or negative sentiment. Strong employment language and easing inflation language may point in different directions. Ask for separate assessments of inflation, activity, employment, policy guidance and balance sheet policy before requesting a combined interpretation.
The comparison should distinguish the statement’s tone from its change since the previous meeting. The research preprint Mind the Shift, on measuring FOMC stance with language models explicitly models both absolute stance and changes between meetings. Treat that distinction as a research design choice, not evidence that a tone score guarantees profitable trades.
Consider a hypothetical change from “inflation remains elevated” to “inflation has eased but remains elevated.” A keyword count still finds “elevated.” A useful comparison identifies the added acknowledgement of progress without discarding the remaining concern.
Ask the system to show the previous passage, the replacement passage and a short explanation. Require it to identify added conditions and removed commitments. Do not let a summary turn “could reduce rates if conditions improve” into “will reduce rates.” The missing condition is the analysis.
Separate Policy Interpretation From Market Expectations
Set up three distinct fields: the decision announced, the suggested future policy path and the difference from a documented expectations baseline. Do not ask a language model to invent the third field from the statement alone.
Suppose, hypothetically, a central bank cuts rates by 25 basis points. If the recorded baseline anticipated a 50 basis point cut, label the decision as easing but smaller than expected. That description is more precise than forcing the entire announcement into one hawkish or dovish box.
Store the baseline’s timestamp and origin. If it comes from analyst forecasts, label it as survey expectations. If it comes from market prices, document the instrument and calculation. Broader real-time financial news analysis can provide context, but keep subsequent commentary out of the initial announcement assessment.
Design an Output That Analysts Can Check
Use a fixed output format rather than an open request to “analyse the announcement.” Keep factual extraction separate from interpretation, and give the system permission to return “not stated” or “ambiguous.” An empty field is preferable to a plausible invention.
| Output field | Required content |
|---|---|
| Announced action | Rate decision and any stated balance sheet changes, with supporting passages. |
| Language changes | Added, removed and revised wording compared with the previous release. |
| Policy interpretation | Topic-level assessment, conditions and alternative readings. |
| Expectation comparison | Difference from the timestamped baseline, or “baseline unavailable.” |
| Review status | Missing documents, extraction warnings and unresolved questions. |
Validate numbers independently of the prose assessment. Check percentage signs, rate ranges, dates and units against the original document. Keep projections separate from decisions, and do not convert a conditional forecast into a commitment.
Language models can organise text and assess context, but generated answers do not assure factual accuracy; prompt changes can also alter results. The BIS primer on language models for economists addresses these constraints and the need for human validation. Build that validation into the workflow rather than adding a disclaimer after the output.
Test the Analysis Without Borrowing From the Future
Evaluate document interpretation before evaluating trading performance. Create a review set containing straightforward decisions, mixed messages, negation and conditional guidance. Have reviewers label each case independently, then inspect disagreements instead of hiding them inside one accuracy figure.
Compare the AI workflow with simpler alternatives: a text comparison, a policy dictionary and a standard extraction template. Require the extra model complexity to justify itself through better evidence retrieval, fewer missed changes or more consistent classifications.
For historical tests, exclude later minutes, revised transcripts and commentary published after the decision window. Consider whether the model’s training material may already contain the event or its aftermath. The separate guide to data leakage and overfitting in AI trading backtests covers the broader evaluation problem.
Keep the Final Decision Reviewable
Before deploying the workflow, define what happens when documents are missing, sources conflict or the interpretation changes during the press conference. Preserve each version of the brief instead of overwriting the first assessment with a better-informed later one.
The strongest use case is disciplined research assistance: retrieve the official release, expose meaningful changes, preserve conditions and flag uncertainty. Judge the system by whether an analyst can verify its reasoning from the evidence—not by how decisively it sounds.