The Valuation‐relevance of Earnings and Cash Flows: an International Perspective
Bibliographic record
Abstract
We investigate which variable, earnings or cash flows, provides greater information for equity valuation within the United States, the United Kingdom, Canada, Germany, and Japan. We regress returns on earnings and cash flow metrics. We generally find earnings developed in three Anglo‐Saxon countries—where capital is traditionally raised in public markets and reporting rules are unencumbered by taxation requirements—to have greater explanatory power for stock returns than cash flow metrics. Conversely, in two non‐Anglo‐Saxon countries—where capital is traditionally raised from private sources—earnings are generally not superior to cash flows for equity valuation, except in Japan, non‐consolidated sample. While sensitivity analyses generally support the conclusions of our primary tests, in some of the additional analyses, earnings were superior to cash flows for samples from all countries. As expected, in all countries earnings have incremental information content over cash flows in explaining returns. Collectively, our findings provide two contributions. First, we generalize the findings of prior US research by showing that earnings are more important than cash flows for equity valuation in other Anglo‐Saxon countries. Second and more importantly, our findings demonstrate that the superiority of earnings over cash flows is not universal. Rather, it depends on the national reporting regime and attendant institutional factors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".