MétaCan
Menu
Back to cohort
Record W1517573887 · doi:10.1108/raf-07-2014-0071

Do changes in gross margin percentage provide complementary information to revenue and earnings surprises?

2015· article· en· W1517573887 on OpenAlexaffabout
Camillo Lento, Naqi Sayed

Bibliographic record

VenueReview of Accounting and Finance · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsLakehead University
Fundersnot available
KeywordsGross marginEarningsRevenueMargin (machine learning)Gross profitQuarter (Canadian coin)Profit marginEconomicsGross outputEconometricsMonetary economicsProfit (economics)AccountingFinanceMacroeconomicsProfitability index

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.241
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueReview of Accounting and FinanceSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207