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What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada?

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  • Marc-André Gosselin
  • Temel Taskin

Abstract

We construct new indicators of the imbalance between demand and supply for the Canadian economy by using natural language processing techniques to analyze earnings calls of publicly listed firms. The results show that the text-based indicators are highly correlated with official inflation data and estimates of the output gap and improve the accuracy of inflation forecasts. This suggests that these indicators could help central banks foresee inflationary pressures in the economy. Our examination of other topics in earnings calls, such as supply chain disruptions and capacity constraints, points to the potential benefits of using textual data to quickly draw insights on a range of relevant topics. We conclude that text-based measures of economic slack should be included in central banks’ monitoring and forecasting toolkits.

Suggested Citation

  • Marc-André Gosselin & Temel Taskin, 2023. "What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada?," Discussion Papers 2023-13, Bank of Canada.
    • Handle: RePEc:bca:bocadp:23-13
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    References listed on IDEAS

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    1. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    2. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
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    4. Guyllaume Faucher & Christopher Hajzler & Martin Kuncl & Dmitry Matveev & Youngmin Park & Temel Taskin, 2022. "Potential output and the neutral rate in Canada: 2022 reassessment," Staff Analytical Notes 2022-3, Bank of Canada.
    5. Sharon Kozicki, 2004. "How do data revisions affect the evaluation and conduct of monetary policy?," Economic Review, Federal Reserve Bank of Kansas City, vol. 89(Q I), pages 5-38.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022. "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, vol. 228(2), pages 259-277.
    8. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Staff Working Papers 01-18, Bank of Canada.
    9. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    10. Olivier Gervais & Marc-André Gosselin, 2014. "Analyzing and Forecasting the Canadian Economy through the LENS Model," Technical Reports 102, Bank of Canada.
    11. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    12. Paul Corrigan & Hélène Desgagnés & José Dorich & Vadym Lepetyuk & Wataru Miyamoto & Yang Zhang, 2021. "ToTEM III: The Bank of Canada’s Main DSGE Model for Projection and Policy Analysis," Technical Reports 119, Bank of Canada.
    13. Rafael Cusinato & André Minella & Sabino Silva Pôrto Júnior, 2013. "Output gap in Brazil: a real-time data analysis," Empirical Economics, Springer, vol. 44(3), pages 1113-1127, June.
    14. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
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    Cited by:

    1. 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.
    2. Tarek Alexander Hassan & Stephan Hollander & Aakash Kalyani & Laurence van Lent & Markus Schwedeler & Ahmed Tahoun, 2024. "Economic Surveillance using Corporate Text," NBER Working Papers 33158, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Central bank research; Domestic demand and components; Econometric and statistical methods; Inflation and prices; Potential output;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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