The informational content of key audit matters: Evidence from using artificial intelligence in textual analysis
Bibliographic record
Abstract
Abstract This study provides empirical evidence that key audit matters (KAMs) are informative for future negative accounting outcomes. We employ FinBERT—a deep learning model designed for natural language processing that allows human‐like text comprehension—to demonstrate that goodwill‐related KAMs are predictive of firms' future impairments. Our findings reveal that utilizing KAMs as a stand‐alone predictor for future impairments provides meaningful predictive power. By exploring the semantic content of reported KAMs, we find that their predictive power is primarily driven by text passages covering how both the firm and the auditor exercise judgment in the accounting and auditing of goodwill. Furthermore, we show that KAMs are incrementally predictive beyond several firm‐level determinants and disclosures in annual reports. Finally, our additional analyses indicate that (1) KAM‐predicted impairment probabilities are relevant to capital markets, (2) KAMs are useful for predicting the magnitude of goodwill impairments, and (3) the predictive power extends to other KAM topics. Collectively, our findings enhance the understanding of the informational content of KAMs, which is a key rationale for their introduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".