The Quality of Neuer Markt Quarterly Reports-a Reply to a Comment
Bibliographic record
Abstract
When compared with its prior performance, the year 2001 is not one of the best years for the Neuer Markt. The Neuer Markt's reputation has been marred by the practice of several companies on the exchange that have published misleading information in the form of incomplete annual and quarterly data. In this study, we examine the quality of Neuer Markt quarterly reports by concentrating on the disclosure level of 47 Neuer Markt companies' reports for the third quarter of 1999, 2000, and 2001. To enable making comparisons, we have established four disclosure indexes that measure each report's compliance with the Neuer Markt Rules and Regulations (NM Rules and Regulations) as well as with International Accounting Standards (IAS) and U.S. Generally Accepted Accounting Principles (U.S. GAAP) interim reporting standards. We then attempt to find typical attributes of Neuer Markt enterprises that provide high or low level of disclosure accounting information in their quarterly reports. The results demonstrate that the level of disclosure has increased over time, partly in response to additional enforcement. In this regard, the quarterly reports standardization project of Deutsche Boerse is an important landmark in satisfying investors' information needs.
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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 | 0.026 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".