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

Silvia Albrizio, Allan Dizioli and Pedro Simon

No 2023/202, IMF Working Papers from International Monetary Fund

Abstract: 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.

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 (search for similar items in EconPapers)
Pages: 46
Date: 2023-10-04
New Economics Papers: this item is included in nep-big and nep-mon
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