Disclosure quantity and the efficiency of price discovery
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of the information disclosure quantity on the pricing efficiency of stocks. Design/methodology/approach Using a sample of large and actively traded Canadian companies listed on the Toronto Stock Exchange, the authors utilize annual reports filed on system for electronic document analysis and retrieval (SEDAR) between 2003 and 2013 to estimate the amount of publicly available information and find that the length and size of annual reports are important determinants of short-horizon return predictability from historical order flows, which is an inverse indicator of market efficiency. Findings The results show that longer and larger annual reports are associated with reduced information asymmetry, lower cost of immediacy, higher trading activity, and an overall improvement in the efficiency of price discovery. The results are robust to the inclusion of controls for various determinants of short-horizon return predictability, such as trading costs, volatility, informational effects and other firm-specific characteristics. Research Limitations/implications Collectively, the findings provide empirical support for the benefits of detailed corporate disclosure in Canada. Originality/value This is the first study to utilize the short-horizon return predictability approach to evaluate the efficiency of price discovery in relation to the amount of information disclosure.
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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 | 0.013 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".