“Cost of Capital” in Residual Income for Performance Evaluation
Bibliographic record
Abstract
We consider a setting in which a firm uses residual income to motivate a manager's investment decision. Textbooks often recommend adjusting the residual income capital charge for market risk, but not for firmspecific risk. We demonstrate two basic flaws in this recommendation. First, the capital charge should not be adjusted for market risk. Charging a market risk premium results in “double” counting because a risk-averse manager will personally consider this risk. Second, while investors can avoid firm-specific risk through diversification, a manager cannot. If the manager faces significant firm-specific risk at the time he makes his investment decision, then it is optimal to charge him less than the riskless return so as to partially offset his reluctance to undertake risky investments. On the other hand, the manager will vary his investment decisions with the pre-decision information he receives, which accentuates his compensation risk, and the firm must compensate him for bearing this additional risk. Hence, if the manager will receive relatively precise pre-decision information, then it is optimal to charge him more than the riskless return to reduce the variability of his investment decisions.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".