Text-Based Measure of Supply Chain Risk Exposure

Di (Andrew) Wu ()
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Di (Andrew) Wu: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109

Management Science, 2024, vol. 70, issue 7, 4781-4801

Abstract: Using textual analysis techniques, including seeded word embedding and bag-of-words-based content analysis, I develop a firm-level measure of supply chain risk exposure from a novel source of unstructured data—the discussion between managers and equity analysts on supply chain-related topics during firms’ quarterly earnings conference calls. I validate the measure by showing that (1) the measure exhibits intuitive variations over time and across firms, successfully capturing both routine and systematic supply chain risk events; and (2) the measure is about risk exposure, as it significantly correlates with realized and options-implied stock return volatility, even after controlling for well-known aggregate risk measures. I then demonstrate that the measure is specifically indicative of the supply chain component of risk exposure. (3) Consistent with theoretical predictions, firms facing higher supply chain risks have higher inventory buffers, particularly in raw materials and intermediate inputs, increased cash holdings in lieu of investments, and significantly lower trade credit received from suppliers. Moreover, (4) during unexpected risk episodes, such as the Tohoku earthquake, firms with higher ex ante risk exposure have worse operating and financial performance. These results indicate that the text-based measure provides a credible quantification of firm-level exposure to supply chain risks and can thus be reliably utilized as outcome or explanatory variables in empirical supply chain research.

Keywords: natural language processing; supply chain risk; empirical operations management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:7:p:4781-4801

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