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

Talita Greyling (), Rangan Gupta () and Christian Pierdzioch ()
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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

No 202616, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Social media sentiment; Supply bottlenecks; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 C53 E23 E70 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2026-05
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