From Statements to Insights: LDA-Based Analysis of Pakistan’s Monetary Policy Statements

This study explores the effectiveness of central bank communication by applying Natural Language Processing (NLP) to the Monetary Policy Statements (MPS) of the State Bank of Pakistan from 2005 to 2024. To quantify the textual features of the policy corpus, we construct a Document-Term Matrix (DTM) and deploy Latent Dirichlet Allocation (LDA), an advanced topic-modeling technique, to identify latent economic themes. By integrating these machine learning techniques with economic indicators, we analyze the complex interplay between monetary policy narratives and macroeconomic outcomes. Our findings uncover hidden textual trends that significantly influence economic decision-making, particularly regarding investment behavior. Ultimately, this research emphasizes that a transparent communication strategy coupled with an optimistic policy tone is critical for building public trust and fostering economic stability. This paper contributes a novel, data-driven perspective to the literature on monetary policy in emerging markets, offering actionable insights for policymakers to enhance communication efficacy.