Do SEC Disclosures Reduce Investors' Disagreements about Firms' Exposures To Market Risk?: A Trading Volume Analysis
Bibliographic record
Abstract
This paper uses a trading volume analysis to examine the extent to which SEC-mandated disclosures make firms' market risk exposures more transparent to investors. We hypothesize that if the SEC's quantitative market risk disclosures reduce investor disagreements about firms' risk exposures, trading volume associated with market rate or price changes should decline after the disclosures are made public. We test for this relationship across three samples of firms that provide the mandated market risk disclosures for the first time in the 10-K reports. We find that the trading volume associated with changes in market rates or prices consistently declines after the 10-K filing for firms exposed to commodity price changes, . We find limited evidence of a decline in trading volume associated with changes in energy commodity prices, and no evidence of a decline in trading volume associated with change in non-energy commodity prices. We explore several explanations for the weaker commodity price results, some relating to potential deficiencies in the reported commodity information and others to research design issues. In general, we interpret the results as providing evidence suggesting that the SEC's quantitative market risk disclosures reduce investor disagreements about firms' exposure to market risks.
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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 | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".