Monetary-Intelligent Language Agent (MILA)

Central bank communication has become a crucial tool for steering the monetary policy stance and shaping the outlook of market participants. Traditionally, analyzing central bank communication required substantial human effort, expertise, and resources, making the process time-consuming. The recent introduction of artificial intelligence (AI) methods has streamlined and enhanced this analysis. While fine-tuned language models show promise, their reliance on large annotated datasets is a limitation that the use of large language models (LLMs) combined with prompt engineering overcomes. This paper introduces the Monetary-Intelligent Language Agent (MILA), a novel framework that leverages advanced prompt engineering techniques and LLMs to analyze and measure different semantic dimensions of monetary policy communication. MILA performs granular classifications of central bank statements conditional on the macroeconomic context. This approach enhances transparency, integrates expert knowledge, and ensures rigorous statistical calculations. For illustration, we apply MILA to the European Central Bank's (ECB) monetary policy statements to derive sentiment and hawkometer indicators. Our findings reveal changes in the ECB's communication tone over time, reflecting economic conditions and policy adaptions, and demonstrate MILA's effectiveness in providing nuanced insights into central bank communication. A model evaluation of MILA shows high accuracy, flexibility, and strong consistency of the results despite the stochastic nature of language models.