Did the Sarbanes-Oxley Act result in a strategic shift in M&A motives?
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether or not the seminal legislation called the Sarbanes-Oxley Act (SOX) influenced a strategic shift in the merger and acquisition (M&A) market. Design/methodology/approach The sample consists of 4,839 completed deals undertaken by US acquirers from the Securities Data Corporation’s US M&As database from January 1, 1996 to December 31, 2009. The authors used the standard event study methodology for short-term performance analysis and the Berkovitch and Narayanan (1993) method to identify merger motives. Findings By following the same acquirers who participated during both pre- and post-SOX periods, the authors find that these acquirers generate 1-1.5 percent more returns for their stockholders around M&A announcement dates and that the motivation has shifted to value maximization (synergy), a notable strategic shift. Research limitations/implications All acquirers and targets are public. Originality/value This paper adds to SOX-related literature as well as to M&A literature. By analyzing M&A deals, often the largest capital investments for acquirers, this paper shows that, despite criticism of SOX, this legislation fundamentally contributed to a strategic shift in the M&A market.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".