Seasonality of Earnings Momentum in an Emerging Market: The Taiwan Experiences
Bibliographic record
Abstract
In Taiwan, firms are requested to announce earnings for the first and fourth quarters within one and four months, respectively, after the fiscal quarters’ end. I therefore conjecture that prior to formal announcement, private earnings information have longer time to dissiminate for the fourth quarter than the first quarter, based on the gradual-diffusion-information model developed by Hong and Stein (1999). Furthermore, given the impact of earnings information on stock price, I hypothesize that returns after quarterly earnings announcement are higher for quarters having less time to disseminate private information before formal announcement than returns for quarters with more time. I uncover a pronounced seasonal pattern for post-announcement cumulative returns for hedge portfolios buying stocks having positive earnings surprises and selling stocks with negative earnings surprises, in accordance with the hypothesis. Specifically, cumulative returns for these hedge portfolios are significantly larger following the first quarter than the fourth quarter during the six to 12 months after the earnings announcement. The evidence is robust to risk adjustment. Moreover, this seasonality can be attributed more to the differential performance of stocks having positive earnings surprises than that of stocks having negative surprises. However, the seasonal results need to be explained with caution because the corresponding third quarter stock returns post-announcement are not as strong as those for the first quarters, despite the third quarter announcement also being made within one month after the fiscal quarter’s end.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".