Do Earnings Calls Help Predict Financial Restatements? Evaluating Text Beyond Public Firm Information

Title:Do Earnings Calls Help Predict Financial Restatements? Evaluating Text Beyond Public Firm Information

Abstract:Financial restatements correct errors in previously published financial reports. Could managers' language in earnings calls warn of them in advance? Comparing 109 calls that precede restatements with 218 matched control calls, we find that the text predicts restatements but adds no detectable ranking gain beyond public firm information. A text classifier beats chance, largely because of restatements tied to special-purpose acquisition companies, but a model using only public information scores slightly higher. With refitting, the 95% interval for the gain from adding text (-0.05 to 0.11 AUROC) still leaves room for a meaningful benefit. A large language model scores higher, but we show that it names the exact ticker in 66% of masked transcripts, and it was released after the restatements occurred. Our results suggest that finding new warning signs in call text requires a public-information baseline and a check of what a language model already knows.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2611.01123 [cs.CL]

(or arXiv:2611.01123v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2611.01123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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