Can Short Sellers Detect Internal Control Material Weaknesses? Evidence From Section 404 of the Sarbanes–Oxley Act
Bibliographic record
Abstract
We examine whether short sellers are interested in, and capable of, identifying firms with an upcoming revelation of internal control material weaknesses (ICMW). We show that short sellers accumulate positions in firms that are about to disclose ICMW under Section 404 of the Sarbanes–Oxley Act for the first time when internal control problems are severe. We find that the short-interest buildup is mainly due to the use of private rather than public information, which suggests that their trades contain incremental prediction power of the upcoming internal control failure. Furthermore, the ability of short sellers to predict ICMW is more pronounced in firms operating in poor information environment. Finally, we find no evidence that trades by short sellers prior to the ICMW disclosure create a cascade of selling that leads to an overreaction of ICMW. Overall, we present evidence that corporate governance information in the form of ICMW is part of the short sellers’ information set, and we establish a path through which ICMW impacts equity investors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".