Bibliographic record
Abstract
This research aims to analyze the impact of Sovereign Wealth Fund (SWF) investments on the values of companies in which they invest. We attempt to provide evidences of the effects of SWF investment activities by examining the short-term impact of SWF investments on the performance of the companies. In order to quantify the valuation effects of SWF investments, we collect data on equity investments for each SWF and we use the event study methodology to estimate abnormal returns to the shares around the times that news of the transactions of SWFs becomes publicly available. The sample consists of 61 investments by 11 important SWFs from around the world (China, Singapore, United Arab Emirates, Russia, France etc.) during the period 2003 to 2014. We find that the anouncement effect of SWF investments in the listed companies is positive and the level of transparency of SWFs influence the positive impact of SWF investments on the performance of those companies in which they invest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".