The Relation between Expected Returns, Realized Returns, and Firm Risk Characteristics*
Bibliographic record
Abstract
Existing literature employs two approaches to assess the validity of alternative proxies for firm-specific cost of equity capital (Et-1(rt)). One approach relies on the expected link between future realized returns and Et-1(rt), while the second approach relies on the expected relation between Et-1(rt) and firm-specific priced risk characteristics. The results of these two streams of literature are mixed with the first (second) approach providing no (strong) support for the construct validity of any (some) of the Et-1(rt) proxies examined. In this paper, we resolve the conflict between these two streams of research. In contrast to prior research, we document a strong positive association between the implied cost of capital estimates and future realized returns. We demonstrate that the negative or insignificant association between implied cost of capital estimates and future realized returns documented in prior research is due to empirical misspecification. Moreover, we show that although most of the estimates we examine are positively associated with future realized returns, only two, rDIV and rPEG, are also predictably related to firm-specific risk. Taken together our results suggest that rDIV and rPEG provide superior proxies for Et-1(rt).
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".