Agency Problem and Stock Returns: Combining Measures of Asset Growth and Gross Profit
Bibliographic record
Abstract
In this paper, we propose a new factor in predicting stock returns, after taking agency problems into account. Although intensive studies have focused on asset growth and profitability as factors in predicting future returns, very limited attention has been given to their interaction. We construct a measure that combines both asset growth and scaled gross profit in a single measure (defined as AGGP, hereafter), by excluding the change in capital expenditures from gross profit. We demonstrate that our measure of profitability controls for the agency problem from managerial decisions in investment. Our results are also robust to the scaling issues raised by recent studies. Further, consistent with prior literature, our measure produces superior results in the full universe of CRSP stocks, but inferior results when applied to a subset of the 500 largest nonfinancial firms. This is consistent with the fact that those largest firms are less affected by the agency problem, leading to the failure of our new measure in predicting future returns among this subsample. In sum, our new measure sheds new lights on how to price agency issues, by providing a “cleaner” profitability measure free of agency costs and also lending supportive evidence to the mispricing explanation of the asset-growth effect.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".