Understanding Cross‐Country Differences in Valuation Ratios: A Variance Decomposition Approach
Bibliographic record
Abstract
Abstract We use a variance decomposition approach to examine why aggregate valuation ratios differ across countries. In a cross section of 22 developed countries from 1980 to 2009, we find that 50 percent of all cross‐country differences in the aggregate price‐to‐book ratio (P/B) can be explained by cross‐country differences in expected future five‐year profitability. In the second half of our sample period, this percentage exceeds that of the first half, rising to almost 64 percent. Although international differences in accounting standards and conventions may have made earnings from different countries more difficult to compare relative to dividends, we find that it is still cross‐country differences in expected future profitability, rather than dividend growth rates, that are more closely related to international differences in valuation ratios. Even among 25 emerging markets, we find that expected future profitability at the five‐year horizon can account for 29 percent of all cross‐country P/B variations. Our results show that international investors are able to identify substantial cross‐country differences in future‐earnings prospects and incorporate them into stock market valuations.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".