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Supply Bottlenecks and Sentiment in Europe: Some Evidence using Machine Learning

Author

Listed:
  • Talita Greyling

    (Centre for Well-being, Artificial Intelligence and Social Impact (C.WAIS) and School of Economics, University of Johannesburg, Johannesburg, South Africa)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Hamburg, Germany)

Abstract

We develop a social media sentiment index based on tweets extracted from Twitter and use a newspapers-based supply bottlenecks index to study by means of random forests, a machine-learning technique, how the latter affects the former for six European countries, after controlling for a wide array of other macro-finance predictors. We find that the predictive relationship between supply bottlenecks and sentiment is generally negative, but in a nonlinear manner. Supply chain constraints emerge as an important predictor of sentiment relative to the other control variables, and its predictive effect increases over the predictive horizon.

Suggested Citation

  • Talita Greyling & Rangan Gupta & Christian Pierdzioch, 2026. "Supply Bottlenecks and Sentiment in Europe: Some Evidence using Machine Learning," Working Papers 202616, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202616
    as

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    References listed on IDEAS

    as
    1. Pablo Burriel & Iván Kataryniuk & Carlos Moreno Pérez & Francesca Viani, 2024. "A New Supply Bottlenecks Index Based on Newspaper Data," International Journal of Central Banking, International Journal of Central Banking, vol. 20(2), pages 17-67, April.
    2. Nicholas Apergis & Arusha Cooray & Mobeen Ur Rehman, 2018. "Do Energy Prices Affect U.S. Investor Sentiment?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(2), pages 125-140, April.
    3. Vladimir Smirnyagin & Aleh Tsyvinski, 2022. "Macroeconomic and Asset Pricing Effects of Supply Chain Disasters," NBER Working Papers 30503, National Bureau of Economic Research, Inc.
    4. Caporin, Massimiliano & Gupta, Rangan & Subramaniam, Sowmya & Torrent, Hudson S., 2026. "Supply Constraints and Conditional Distribution Predictability of Inflation and its Volatility: A Nonparametric Mixed-Frequency Causality-in-Quantiles Approach," Research in Economics, Elsevier, vol. 80(2).
    5. Bouri, Elie & Cepni, Oguzhan & Gupta, Rangan & Liu, Ruipeng, 2025. "Supply chain constraints and the predictability of the conditional distribution of international stock market returns and volatility," Economics Letters, Elsevier, vol. 247(C).
    6. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021. "COVID-19 and the Costs of Deadly Disasters," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 366-370, May.
    7. Leo Krippner, 2020. "A Note of Caution on Shadow Rate Estimates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 951-962, June.
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    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

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