Should managers estimate cost of equity using a two‐factor international CAPM?
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess how much difference it makes for US firms to use the two‐factor ICAPM to estimate their cost of equity instead of a single‐factor CAPM. Design/methodology/approach For a large sample of US companies, the authors compare the empirical cost of equity estimates of a two‐factor international CAPM with those of the single‐factor domestic CAPM and the single‐factor global CAPM. Findings The authors find that the cost of equity estimates of the two‐factor ICAPM are reasonably close to those of either single‐factor model for US firms with low‐to‐moderate foreign exchange exposure; and second, perhaps surprisingly, for US firms with extreme foreign exchange exposure, that the cost of equity estimates of the two‐factor ICAPM tend to be very close to those of the domestic CAPM, and even closer than to those of the single‐factor global CAPM. Research limitations/implications The paper's findings might prove useful to academic researchers wanting to resolve the seemingly contradictory empirical results on the pricing of FX risk. Practical implications The findings will hopefully help managers decide whether they should go to the trouble of estimating a US firm's cost of equity with the two‐factor international CAPM instead of a traditional single‐factor CAPM. Originality/value The paper extends the existing literature by focusing on the two‐factor ICAPM, and finds some new and surprising empirical results.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".