Anticipation Capability and Signal Mode That Analysts Use to Communicate Financial Fraud: Empirical Evidences from Punishment Bulletins by CSRC
Bibliographic record
Abstract
Using the sample of Chinese A-share listed companies punished by CSRC and its' matched companies in 2005- 2011,this paper examines w hether analysts can anticipate the fraud and w hat signals analysts may use to communicate financial fraud to security market. The results are:( 1) the analysts' recommendations for fraudulent companies at the end of one quarter before the first fraud behavior,and the end of quarter in first fraudulent behavior,or at the end of last quarter of fraud behavior,are more inferior to that for no- fraudulent companies;( 2) The analysts' coverage of fraudulent companies and non-fraudulent companies has no significant difference;( 3) The more analysts' coverage,the more inferior stock recommendations of fraudulent companies. The results above mean that analysts have capability of anticipate corporate financial fraud,and the efficient instrument used to signals corporate financial fraud information is not analysts' coverage but stock recommendations. The analysts' coverage number is not the result caused by analysts' self-selection in corporate fraud but the determinant of the capability of analysts' anticipate corporate financial fraud.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".