Communication, monetary policy, and financial markets in Mexico

This paper was produced as part of the Final Conference of the BIS-CCA Research Network on "Monetary policy frameworks and communication (2019-2022)".

Summary

Focus

All modern central banks have moved to a strategy that includes better communication tools, more transparency and accountability. Associated with this trend, in the last few decades the literature has substantially increased. The recent development of intensive computational tools that can transform text into data, provides additional ways to analyse and corroborate the benefits of greater communication and central banks' sentiment. Textual analysis has become itself a source of information and feedback for central banks.

Contribution

We use unsupervised Natural Language Processing (NLP) techniques to turn the text that private banks send to their clients about the Mexican economy into vectors of topics. Then, we use NLP to determine the issues that the economic analysts discuss the day before and the day after of the Monetary Policy Announcement (MPA), and also the day before and the day after of the Monetary Policy Minutes in Mexico.

Findings

We find that every time, private banks cover a large diversity of topics and words before the MPA with no evident consensus of topics, and that almost always the quantities of terms and topics are reduced and repeated by almost every bank after the MPA indicating some surprise, and that the topics vary depending on the date of the MPA. We also found weak evidence that a measure of the size of the changes in the private bank's communication with their clients is positively correlated to changes in the long-term yields but negatively correlated to the size of exchange rate movements.

Abstract

We determine if the communication of private banks to their clients with financial interests in Mexico changes or not after Mexico's Central Bank communicates its monetary policy decision (MPD) and also two weeks later, with the publication of the minutes of Mexico's Central Bank monetary policy decision (MMPD) between 2011 and 2019. We use unsupervised Natural Language Processing (NLP) techniques to turn the text that private banks send to their clients about the Mexican economy into vectors of topics. We find that every time, private banks cover a large diversity of topics and words before the MMPD with no evident consensus of topics, and that almost always the quantities of terms and topics are reduced and repeated by almost every bank after the MMPD indicating some surprise (notable exception: the liftoff in December 2015), and that the topics vary depending on the date of the MMPD. The fact that private banks discuss the same topics and write to their clients with sentences that contain the exact same words indicates that the private banks react to the MMPD, independent of their opinion about the Central Bank's statements. We also found weak evidence that a measure of the size of the changes in the private bank's communication with their clients is positively correlated to changes in the long-term yields but negatively correlated to the size of exchange rate movements.

JEL classification: C6, E5, E6.

Keywords: natural language processing, unsupervised sentence embedding, central bank communication, Mexico.