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Measuring firm-level supply chain risk using a generative large language model

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  • Fan, Siyu
  • Wu, Yifei
  • Yang, Ruochen

Abstract

Using site visit transcripts from Chinese-listed firms, we propose a generative large language model (LLM)-based approach to quantify firm-level supply chain risks. We validate our measure by showing that it varies intuitively over time and across industries, and it correlates with higher capital costs, greater stock return volatility, and larger inventory reserves. Furthermore, we identify distinct categories of supply chain risks at both the micro and macro levels, finding that supply chain concentration drives micro-level risks, while exposure to overseas markets and macroeconomic conditions drives macro-level risks. Our findings highlight the capability of LLMs to assess supply chain risk.

Suggested Citation

  • Fan, Siyu & Wu, Yifei & Yang, Ruochen, 2025. "Measuring firm-level supply chain risk using a generative large language model," Finance Research Letters, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:finlet:v:77:y:2025:i:c:s1544612325003745
    DOI: 10.1016/j.frl.2025.107111
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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