Bibliographic record
Abstract
This study investigates a large sample of financial statement restatements over the period 1986-2001, and compares restatements caused by changes in accounting principles to those caused by errors. Typically, investors perceive restatements as negative signals due to three potential reasons: (a) the restatement indicates problems with the accounting system that may be manifestations of broader operational (and managerial) problems, (b) the restatement causes downward revisions in future cash flows expectations, and (c) the restatement indicates managerial attempts to cover up income decline through “cooking the books.” We provide evidence that market reactions to restatements due to errors are generally negative. We show that these restatements come in periods of declining profits and lower profits than industry peers for the restating firms, consistent with both opportunistic managerial behavior and operational problems. However, investors9 reactions to income-increasing restatements due to errors are not different from zero, suggesting that the perceived failure of the accounting system is just offset by the upward revisions in future cash flow expectations in these cases of income-increasing errors. Thus, our combined results show that not all restatements are alike; users of the information need to carefully assess the existence and potential effects of the three factors that typically cause the downward revisions in stock prices on a case by case basis. TOPICS:Security analysis and valuation, accounting and ratio analysis
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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".