Text-Based Measure of Supply Chain Risk Exposure

Supply chain risks, despite being a well-developed theoretical concept, are difficult to quantify, limiting the scope of empirical research. I develop a firm-level measure of supply chain risk exposure from a novel source of unstructured data---managers' discussions of supply chain-related topics during earnings conference calls and Q&A sessions---using textual analysis techniques including seeded word embedding and bag-of-words-based content analysis. I validate the measure by showing that (1) it exhibits intuitive variations over time and across firms, and successfully captures both systematic supply chain risk events and supply risks in firms' routine operations; (2) the measure indeed captures exposure to risks as it significantly correlates with contemporaneous and next-quarter stock return volatility, and (3) this relation remains significant after controlling for well-known aggregate risk measures such as the VIX and economic uncertainty indices. I then conduct a series of tests to demonstrate that the measure is indicative of risk that is specifically related to supply chains. (4) Consistent with theoretical predictions, firms facing higher supply chain risks have higher inventory buffers, with the majority of the inventory buildup concentrated in raw materials and intermediate inputs. High-risk firms also significantly increase their cash holding in lieu of investments and receive significantly lower trade credit from suppliers. (5) A placebo measure that only tabulates risk words does not correlate with any supply chain risk buffers. Moreover, (6) during well-known risk episodes such as the Tohoku earthquake, firms with higher ex-ante risk exposure have worse 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