Empirical Analysis of Firm Attributes before and after the Sarbanes-Oxley Act
Bibliographic record
Abstract
This paper examines whether voluntary delisting from U. S. exchanges by international firms surged during the five years following the passage of Sarbanes-Oxley ACT of 2002 (SOX). Using 278 international firms, which include 139 delisted international firms from NYSE and NASDAQ and a matched pair of 139 non-delisted international firms, we document that the number of voluntary delisting increased significantly from 12.9% in the pre-SOX period (1997 – 2001) to 87.1% in the post-SOX period (2002 – 2007). This represents an increase of 74.2% in the number of international firms that delisted. In addition, using a predictive model advanced by Piotroski and Srinivasan (2008) and Doidge, Karolyi, and Stulz (2009), we find that yearly profitability ratio is negatively affected by ADR listing status and is the strongest predictor of delisting in the logistical regression model. Furthermore, firm size, corporate governance and leverage ratio are not statistically significant in predicting ADR listing status or associated with SOX legislation. This supports the documented evidence that the SOX legislation did not decrease or negatively affect firm size, corporate governance, or leverage ratio.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".