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Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls
Author:
Silvia Albrizio https://isni.org/isni/0000000404811396 International Monetary Fund

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,
Allan Dizioli
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Pedro Vitale Simon
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Series:
IMF Working Papers
Volume/Issue:
Volume 2023: Issue 202
Language:
English
Publication Date:
04 Oct 2023
DOI:
https://doi.org/10.5089/9798400253522.001
ISBN:
9798400253522
ISSN:
1018-5941
Pages:
46
Keywords:
rms' inflation expectations; Firms' earnings calls transcripts; Natural Language processing; GPT3.5; Monetary policy; inflation expectation; expectation index; monetary policy effectiveness; firms attention; expectations disagreement; Inflation; Artificial intelligence; Wages; Futures; Consumer price indexes; Global
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IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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Using a novel approach involving natural language processing (NLP) algorithms, we construct a new cross-country index of firms' inflation expectations from earnings call transcripts. Our index has a high correlation with existing survey-based measures of firms' inflation expectations, it is robust to external validation tests and is built using a new method that outperforms other NLP algorithms. In an application of our index to United States, we uncover some facts related to firm's inflation expectations. We show that higher expected inflation translates into future inflation. Going into the firms level dimension of our index, we show departures from a rational framework in firms' inflation expectations and that firms' attention to the central enhances monetary policy effectiveness.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund