The relevance of XBRL extensions for stock markets: evidence from cross-listed firms in the US
Bibliographic record
Abstract
Purpose The study investigates the relevance for stock markets of voluntary disclosure of eXtensible Business Reporting Language (XBRL) extensions [based on International Financial Reporting Standards (IFRS) or US-GAAP] for an international sample of US cross-listed firms. Design/methodology/approach The study examines if the disclosure of XBRL extensions by a firm provides relevant information to market participants. Towards that end, this paper investigates whether this type of disclosure affects the level of information asymmetry between insiders and investors and if it is value relevant. This study measures information asymmetry by bid-ask spread and value relevance by stock price or Tobin's Q. Findings After a certain level of disclosure of XBRL extensions, the impact on stock pricing is negative (creates noise on stock markets). Controlling for that phenomenon, both IFRS and US-GAAP XBRL extensions are value relevant. Second, results indicate that XBRL extensions are positively (negatively) related to stock market value for firms that exhibit positive (negative) earnings. This suggests a complementary effect between earnings and XBRL extensions on their relation with stock price or Tobin's Q. Finally, the results also indicate that both IFRS extensions and US-GAAP extensions are associated with lower information asymmetry (i.e. bid-ask spread). Originality/value To the best of the authors’ knowledge, this study is the first to investigate the relevance of XBRL extensions under IFRS for US cross-listed firms since the availability of the IFRS taxonomy for foreign private issuers that prepare financial statements under IFRS standards.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".