Mandatory Monthly Sales Disclosure and the Information Content of Earnings
Bibliographic record
Abstract
Synopsis The research problem Taiwanese stock markets practice a unique form of reporting frequency, combining quarterly interim financial reports with mandatory monthly sales announcements. This unique setting allows us to compare the information content of quarterly earnings that are announced before, bundled with, or announced after the monthly sales disclosures. Motivation Monthly sales disclosures likely contribute to the pricing of earnings announcements (EAs) by providing investors with valuable information that helps them revise their expectations when forecasting earnings and cash flows. We examine whether monthly sales disclosures help investors to process earnings information. The test hypotheses Our first hypothesis is that the immediate market response to earnings news is lower if the previous quarter’s EAs are disclosed after the reporting of monthly sales for the first month of the next quarter. Our second hypothesis is that the subsequent market response to earnings news is lower if the previous quarter’s EAs are disclosed after the reporting of monthly sales for the first month of the next quarter. Target population We studied a sample of Taiwanese listed firms from 2014 through 2018. Adopted methodology We used multivariate regressions to test the hypotheses. Analyses We examined the monthly sales disclosures for the first month of quarter [Formula: see text], which are released within the first 10 days of the second month of quarter [Formula: see text]. We analyze the pricing of the content of the earnings of quarter q, conditional on whether the earnings are released before, simultaneously with, or released after the monthly sales disclosures. Findings We found that earnings disclosed after the monthly sales news are associated with weaker EA returns and post-EA drift, relative to earnings disclosed before or bundled with the monthly sales news. These results suggest that monthly sales disclosures preempt the information content of late EAs. More importantly, we found that late EAs are associated with less positive correlation between EA returns and post-EA returns than early and bundled EAs. Overall, these results suggest that by providing timely sales information, monthly sales disclosures improve the information environment.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".