The Differential Role of R&D and <scp>SG</scp>&A for Earnings Management and Stock Price Manipulation*
Bibliographic record
Abstract
ABSTRACT This paper documents a differential role of R&D versus selling, general, and administrative expenses (SG&A) for real earnings management. The distinction of these two components is important because prior studies mostly examine their combined use, but firms could manipulate them differently given the differing valuation implications. Reduced SG&A is viewed positively by investors as evidence of cost reduction, while reduced R&D is viewed negatively by investors as such expenditures are critical signals of expected growth. I examine their use in the context of seasoned equity offerings (SEOs) as well as firms receiving accounting and auditing enforcement releases (AAERs). Although both groups face strong incentives to manage earnings upward by reducing expenses, I predict and find that firms will reduce SG&A but increase R&D. During the manipulation period, SEO and AAER firms exhibit lower discretionary SG&A and higher discretionary R&D, relative to control firms, and investors positively value low discretionary SG&A and high discretionary R&D. Overall, this study confirms the importance of distinguishing between R&D and SG&A in real earnings management contexts and suggests a complementary (substitutive) relation between cutting SG&A (R&D) and accruals management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".