The Effect of Changing the Listing Level on the Information Environment of ADRs
Bibliographic record
Abstract
This study investigates the impact of changing the listing level of American Depositary Receipts (ADRs) on the information environment of ADRs. Specifically I examine four main listing levels of ADRs and analyze whether ADRs that change (upgrade/downgrade) their listing level have greater/less analyst coverage, increased/decreased forecast accuracy, and increased/decreased forecast dispersion. In addition, since analyst forecast accuracy differs depending on ADRs’ home country legal institutions, this study also investigates whether analyst forecast accuracy differs depending on ADRs’ home country legal institutions. Specifically, I examine whether the impact on information environment is different for ADRs from countries with different legal systems and disclosure regulations. The SEC has segmented ADRs into four listing levels which have different reporting and regulatory requirements. The SEC and disclosure requirements vary across the four ADR programs. Level II and Level III are exchange listed ADRs, Level I is traded OTC, and Level IV private placement. ADRs that trade in the U.S. market (exchange listed) have more stringent requirements and must adhere to stronger enforcement of accounting standards. Thus, their regulatory and hence quality of information environment is higher. If ADRs that trade on Level II and Level III must follow a more rigorous regulatory requirement, then do analysts and investors in the U.S. markets adjust the pricing of securities to reflect this difference in ADR listing levels? The sample consists of 448 ADR firms from emerging and developed markets around the world that cross-listed on U.S. markets and eventually changed their listing level between 1999 and 2010. I classify the firms based on their listing level which symbolizes the degree of regulatory adherence. To proxy for the information environment, I examine analyst forecast. I analyze the level of significant difference in forecast accuracy, number of analyst forecast, and forecast dispersion when ADRs change their listing level. I present empirical evidence consistent with the hypotheses that an upgrade (downgrade) of ADR listing level is associated with a decrease (increase) in analysts’ forecast error, and number of analyst following. These results indicate that a change in the information environment around U.S. cross-listing is a combination of both the bonding hypothesis effect and the ADR listing level effect.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".