Making Exceptions to Foreign Direct Investment Regulation: How an Opaque Federal Regime May be Magnifying Abnormal Returns
Bibliographic record
Abstract
With the intention of protecting its national security interests, the United States federal government established the Committee on Foreign Investment in the U.S. (CFIUS). This regime has grown to harness vast authority to block certain foreign direct investments (FDI) which it deems to be threatening to national security. Prior literature has indicated that CFIUS can potentially create deadweight loss in the economy, have negative impacts on the merger arbitrage spreads for U.S.-domiciled companies, and deter FDI with its lengthy and costly review process. Some also criticize the fact that the expansion of CFIUS’s powers has been accompanied by little oversight or transparency for the American public. What has yet to be explored with empirical analysis in existing literature, however, are the effects of CFIUS making exceptions to its mandatory filing requirements for its Five Eyes friends – the United Kingdom, Canada, Australia, and New Zealand. With an Excepted Foreign State status, investors and acquirers domiciled in one of these states are afforded advantages in the American mergers-and-acquisitions (M&A) marketplace, the value of which has yet to be quantified until now. This paper utilizes M&A data (with 238 transactions after filtering), obtained through the SDC Platinum database. Through a difference-in-difference analysis, this paper finds that U.S. companies involved in M&A transactions with an investor domiciled in a CFIUS-excepted state experience an estimated 4.59% higher cumulative abnormal stock return post-announcement. However, there is enough uncertainty in this estimate that we cannot reject the null hypothesis of no effect at standard levels of statistical significance. In any case, it is possible that the CFIUS review process makes investors doubtful of a merger or acquisition being completed.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".