Bibliographic record
Abstract
ABSTRACT Sovereign wealth funds (SWFs) are government‐owned institutional investors pursuing political and financial investment objectives. With $8 trillion in assets, SWFs are geopolitical powerbrokers actively participating in global capital markets, yet we know little about the financial reporting consequences of SWF investment. I document evidence supporting the hypothesis that the simultaneous pursuit of political and financial investment objectives renders SWFs weak monitors. Using a staggered difference‐in‐differences research design, I document economically significant increases in discretionary accruals for SWF target firms after SWF investment, relative to an entropy‐balanced control group of non‐SWF target firms. Corroborating tests document that the effect of SWF investment on discretionary accruals strengthens with SWFs' equity stake and SWF target firms' earnings management incentives and weakens when regulators curb SWFs' pursuit of political objectives. I highlight SWFs' distinct monitoring effect by replicating my analyses after replacing SWF investment with conventional institutional investment, and document that conventional institutional investment instead reduces discretionary accruals. I further corroborate SWFs' distinct monitoring role among conventional institutional investors using a wide variety of robustness tests employing alternate specifications, samples, and financial reporting proxies. Overall, this study introduces an economically important and fundamentally distinct but little‐studied institutional investor to the accounting literature.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".