Analisis respon harga saham terhadap pengumuman laba kuartal interim dan kuartal keempat
Bibliographic record
Abstract
This research examines cross-quarter differences in the response of stock prices to earnings announcements. The research focuses on whether fourth quarter earnings announcements contain more or less information than those of interim quarters. Like many prior studies, this paper provides information about regressions of unexpected announcement period returns on measure of unexpected earnings. It conducted with three different measures of measures of unexpected earnings; earnings expectations are based on seasonal random walk, random walk with trend and market-adjusted trend. Using 45 firms and 720 quarterly earnings for period 1996 through 1999 with weighted last square, this paper present evidence that stock prices respond less to fourth quarter earnings announcements that to intern announcements. Fourth quarter announcement are characterized by a smaller ERC (Mandenhall and Nichols,1988; Salamon and Stober, 1994 and Lee and Park, 2000) and a lower explanatory power of unexpected earnings than earlier quarter announcements (Hagerman, Zmijewski and Shah, 1984) because of lower predictability of fourth quarter earnings (Collins, Hopwood and McKeown, 1984). This results suggests that fourth quarter earnings are less persistent and less useful to investors than interim quarter earnings. Two explanations from previous studies are the existence of fiscal year-and discretionary accruals from earnings management or the phenomenon of fourth quarter settling up described by Collins, Hopwood and McKeown (1984). In addition, measurement error in earning expectations may be larger in fourth quarters than in interim quarters (Salamon and Stober,1994)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".