Does late 10K filing impact companies’ financial reporting strategy? Evidence from discretionary accruals and real transaction management
Bibliographic record
Abstract
Abstract The study investigates how late 10K filers adapt their financial reporting strategy in the post‐late filing period in their response to bad publicity, negative market sentiment, and higher stakeholders’ scrutiny resulting from reporting delays. Both the level and change regressions show that late 10K filers significantly reduce the use of discretionary accruals from pre‐ to post‐late filing year. However, they simultaneously increase real transaction management over the same time period. The trade‐offs between the two earnings management techniques are more prominent when the late filers have a strong incentive to meet or beat earnings benchmarks. Our primary results are robust when late filings are caused by accounting, auditing, and internal control issues, and when the late filers cited no meaningful reason for late 10K filings. It is further evident that late filers with material internal control weaknesses and late filers that subsequently restate their financial statements make relatively higher trade‐offs than the matched non‐late filers. Finally, the trade‐offs between reduced accruals and increased real transaction management are stronger for the accelerated filers, and for the late filers audited by Big 4 auditors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".