Discussion of ADRs, Analysts, and Accuracy: Does Cross‐Listing in the United States Improve a Firm's Information Environment and Increase Market Value?
Bibliographic record
Abstract
Abstract Lang, Lins, and Miller [2002] investigate the relation between cross‐listing in the United States and information intermediation by analysts. The results suggest that cross‐listing in the United States increases analyst following and forecast accuracy and that both variables are associated with Tobin's Q. These findings are interesting and advance the cross‐listing literature in several ways. This discussion raises two issues. First, I highlight that the sources of cross‐listing effects are not obvious and are difficult to disentangle. To illustrate this point, I replicate the analysis using cross‐listed Canadian firms, for which mandated disclosures are held constant. Thus, if disclosure effects are important for documented cross‐listing effects, I expect to find no relation in the Canadian sample. The findings for forecast accuracy are consistent with this hypothesis. However, analyst following continues to be significantly higher for cross‐listed Canadian firms. These findings suggest that the sources of cross‐listing effects differ for analyst coverage and forecast accuracy. Second, I discuss the link between analyst variables, firm value, and cost of capital. As they are only tenuously related, I draw attention to some unresolved questions and areas for future research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".