The cost of capital effects of overseas listings: Market sequencing and selection
Bibliographic record
Abstract
Sarkissian acknowledges financial support from FCAR and IFM2. The cost of capital effects of overseas listings: Market sequencing and selection Using a broad sample of 1298 overseas listings spanning most world markets and an extended event window of ten years before and after the listing we examine the transitory and permanent cost of capital effects associated with different home and host markets, listing sequencing, and firm characteristics. Controlling for the transitory effects, we find that the cost of capital gains to overseas listings are more modest than those reported in earlier studies: the average cost of capital decline is 2.5 percentage points for the entire sample of cross-listings. We find that although gains from increasing the size of the investor pool, liquidity, disclosure, and shareholder protection maintain some role, firms generate the greatest cost of capital gain when listing in markets with which there is large cross product market trade. The evidence emphasizes the importance of investor familiarity and information in cross-listing behavior. JEL classification: G15; G32 1
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".