IMF Working Papers

Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls

By Silvia Albrizio, Allan Dizioli, Pedro Vitale Simon

October 4, 2023

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Silvia Albrizio, Allan Dizioli, and Pedro Vitale Simon. Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls, (USA: International Monetary Fund, 2023) accessed November 21, 2024

Disclaimer: 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.

Summary

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.

Subject: Artificial intelligence, Consumer price indexes, Financial institutions, Futures, Inflation, Labor, Prices, Technology, Wages

Keywords: Artificial intelligence, Consumer price indexes, Expectation index, Expectations disagreement, Firms attention, Firms' earnings calls transcripts, Futures, Global, GPT3.5, Inflation, Inflation expectation, Monetary policy, Monetary policy effectiveness, Natural Language processing, Rms' inflation expectations, Wages

Publication Details

  • Pages:

    46

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2023/202

  • Stock No:

    WPIEA2023202

  • ISBN:

    9798400253522

  • ISSN:

    1018-5941

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