Information from the abstract
Purpose This paper provides evidence on whether corporate investment in artificial intelligence (AI) predicts corporate default risk, using the full term structure of forward-looking probabilities of default (PD). Design/methodology/approach Validated firm-level AI-investment measures built from employee resumes and job postings are merged with NUS-CRI forward-intensity PDs for U.S. listed non-financial firms (outcomes 2011–2018). Because PD is a fraction in [0,1], estimation uses fractional logit with industry and year fixed effects; identification is probed with a university AI-talent-supply instrument in a control-function design with bootstrap inference for the generated regressor, matching and entropy balancing and an extensive robustness battery. Findings A one-standard-deviation increase in AI investment is associated with significantly lower default probability at 12- to 36-month horizons – about 8 basis points at 12 months, roughly one-quarter of the sample mean. The relative effect declines with horizon and is concentrated within the first two years. A control-function test does not reject exogeneity, so the fractional-logit estimates are preferred, with the instrument as corroboration; results are robust to the job-posting measure, alternative industry classifications and screens and R&D and capital-intensity controls. Originality/value To our knowledge, the first firm-level evidence linking validated AI-investment measures to the PD term structure for U.S. listed firms, showing that labor-market footprints of AI adoption carry default-relevant information and that the association is a short-to-medium-horizon phenomenon.
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Related topics: Financial Distress and Bankruptcy Prediction · Credit Risk and Financial Regulations · Firm Innovation and Growth
Thai researcher and institutional participation
Woraphon Wattanatorn · Thammasat University
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