Do changes in gross margin percentage provide complementary information to revenue and earnings surprises?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the association between gross profit percentage, abnormal market returns, revenue surprises and earnings surprises. Gross margin is relied upon by various market participants, as its predictive power is incremental and distinct from revenue and earnings signals; however, gross margin has received little researcher attention. Design/methodology/approach – General regression specifications found in the prior literature are extended to assess the informational content of changes in gross margin percentage. In addition, various portfolios are created based around the nature of the signals (positive or negative), provided by each income statement metrics (revenue, gross margin and earnings). A sample of 5,582 quarterly observations of S & P 500 firms is compiled. The main regressions are exposed to three robustness tests that focus on industry sub-groupings, institutional ownership and fourth-quarter observations. Findings – The main findings reveal that gross margin percentage changes and earnings surprises are significantly related to abnormal market returns in the short window around the earnings announcement date and persist into a wider window measured as the quarter after the earnings announcement date. The relationship between gross margin percentage changes and abnormal returns is more pronounced when positive (negative) changes in gross margin percentage are accompanied by positive (negative) revenue and earnings surprises. Research limitations/implications – This study relies upon S & P 500 firms which are all relatively large firms. Therefore, the results may not be generalizable to smaller firms. In addition, the gross margin change is measured as the quarter-over-quarter percentage change because there is no analyst expectation for gross margin. Originality/value – This paper extends the prior literature by developing three testable hypotheses that investigate the linkages between abnormal market returns, gross margin and revenue and earnings surprises. This is the first known study to investigate the informational content of changes in gross margin percentage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".