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SUMMARY:Measuring the Informativeness of Audit Reports: A Machine Learning
  Approach - Reining Petacchi (Georgetown)
DTSTART:20250313T130000Z
DTEND:20250313T140000Z
UID:TALK218386@talks.cam.ac.uk
CONTACT:Cerf Admin
DESCRIPTION:This paper studies the informational value of audit reports us
 ing computational linguistic tools based on FinBERT\, a cutting-edge large
  language model (LLM) designed for financial texts. We analyze the topics 
 within audit reports and classify them into 41 labels\, organized into sta
 ndard and expanded components. The standard components contain boilerplate
  language on audit scope\, opinion\, and basis for opinion. In contrast\, 
 the expanded components contain explanatory language\, audit matters\, and
  discussions of audit procedures that reflect auditor judgment. Contrary t
 o the perception that audit reports lack informational value\, we find tha
 t changes from the addition of new sentences in the expanded components ca
 rry strong implications for the client firms’ future performance and mis
 statement risk. Firms with larger changes in the expanded components exhib
 it poorer future returns\, less persistent operating performance\, and a h
 igher likelihood of future financial restatements. These changes trigger i
 nvestor trading\, reducing bid-ask spreads around the audit report release
 s. Both regulatory influences and litigation pressures drive these changes
 \, underscoring the role of both public and private oversight in enhancing
  audit report informativeness.
LOCATION:Castle Teaching Room\, CJBS
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