The Effect of External Monitoring on Accrual‐Based and Real Earnings Management: Evidence from Venture‐Backed Initial Public Offerings*
Bibliographic record
Abstract
This paper investigates the effect of venture capitalist (VC) quality on earnings management in firms conducting initial public offerings of their equity stock, focusing on manipulation of both accruals and real activities. I develop a measure of VC quality based on a principal components factor analysis using data that are obtainable for virtually all VC firms. This metric is highly correlated with VC funds’ financial returns, and with the likelihood of successful exits through initial public offerings or trade sales. After going public, companies backed by higher quality VCs have lower abnormal accruals, lower earnings management through real activities manipulation, and a lower likelihood of financial restatement. Companies backed by top‐quartile VCs do not appear to engage in real activities manipulation as a substitute for accruals manipulation. Companies backed by lower‐tier VCs exhibit earnings management behaviors which are indistinguishable from those of non‐VC‐backed companies. The results continue to hold when controlling for endogeneity. Overall, the results suggest that higher quality VCs are better able to constrain opportunistic financial reporting by their portfolio companies going public.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".