Executive Compensation, Investment Opportunities, and Earnings Management: High-Tech Firms versus Low-Tech Firms
Bibliographic record
Abstract
This paper examines the systematic differences between high-tech and low-tech firms in compensation policies, the sensitivity of compensation to market and accounting performance, and earnings management in the presence of investment opportunities. We find that the level of industry participation (i.e., high-tech versus low-tech) has incremental contracting value beyond the investment opportunity set (IOS) in determining executive compensation. When we control for the IOS factor, we find that high-tech firms generally pay higher levels of total compensation by granting larger amounts of stock options than low-tech firms, even though they typically offer lower cash salaries and bonuses than their low-tech counterparts. The relationship between compensation and stock return is higher for high-tech firms, and there appears to be no difference in the association between compensation and accounting return in both groups. More importantly, we find that the association between bonus and discretionary accruals is higher for high-tech firms than for low-tech firms, especially when earnings before discretionary accruals are lower than analyst-forecasted earnings. Furthermore, even in cases where premanaged earnings exceed earnings expectations, or in cases where the probability of meeting analysts' forecasts is low, high-tech firms are more likely to reward managers who use discretionary accruals to meet earnings forecasts. This is consistent with the practice of compensation committees of hightech firms rewarding CEOs for using discretionary accruals to signal private information to reduce information asymmetry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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".