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Global Demand and Supply Sentiment: Evidence From Earnings Calls

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  • Franz Ulrich Ruch
  • Temel Taskin

Abstract

This paper quantifies global demand and supply conditions and compares two major global recessions: the 2009 Great Recession and the COVID‐19 pandemic. First, we compute demand and supply sentiment by applying Natural Language Processing techniques on earnings call transcripts. Second, we corroborate our sentiment measure by identifying demand and supply shocks using a structural Bayesian vector autoregression model. The results highlight sharp contrast in the size of supply and demand conditions over time and across sectors. While the Great Recession was characterized by weak demand, COVID‐19 caused sizable disruptions to both demand and supply, with varying relative importance across major sectors. Furthermore, certain sub‐sectors, such as professional and business services, internet retail, and grocery/department stores, fared better than others during the pandemic.

Suggested Citation

  • Franz Ulrich Ruch & Temel Taskin, 2024. "Global Demand and Supply Sentiment: Evidence From Earnings Calls," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(2), pages 314-334, April.
    • Handle: RePEc:bla:obuest:v:86:y:2024:i:2:p:314-334
    DOI: 10.1111/obes.12587
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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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