Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the restatement information disclosed in the Form 8K and the Press Release. It examines the relationship between manipulating the quantity, quality, manner and timing of restatement information and the probability of committing fraud. Design/methodology/approach The authors used 18 informational indicators developed by BenYoussef and Breton (2016), and applied the prediction methodology based on F-scores, developed by Dechow et al. (2011). Findings Results indicate that the information content of restatement announcements provides significant insights into the likelihood of fraud occurrence. A firm that manipulated previous earnings will continue to do so, and will try to mislead investors by releasing inaccurate and incomplete information in the Form 8K and the Press Release. The model helps identify this manipulation and hence can be used as a tool for fraud detection. Research implications/limitations This paper applies the constructs drawn from Information Manipulation Theory to restatement contexts to detect fraud. Practical implications The paper is of use to regulators, investors and financial crime experts, as it provides insights to better fraud detection. Originality/value The paper is based on proprietary data that were hand collected, and is being used first time to predict fraud.
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 | 0.010 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".