Tracking central bank narratives with LLMs



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Large Language Models (LLMs) can be used to read and interpret every central banking speech in real time. We demonstrate our framework for understanding the different impact of three narrative shocks on the macroeconomic assessment of central bankers. We consider the recent AI and tariff shocks as well as the closure of the Strait of Hormuz.

These three narrative shocks have substantial differences in their interpretation. AI is currently the most discussed shock. But central bankers conclude little about the implications for monetary policy. Both tariffs and the recent energy shock are considered negative supply shocks. The closure of the Strait of Hormuz shows a strong inflationary impact and an unambiguously hawkish policy response which intensified during May before the US/Iran peace deal. Tariffs are fading in prominence but are still considered inflationary for the US.

Rather than reducing every speech to a single hawkish or dovish score, our framework decomposes the policy stance into multiple underlying forces. Every score is related to specific sentences which can be interrogated and, where needed, overridden by expert judgement. Our ambition is not to replace that judgement but to systematically quantify and track it.

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