The Role of “Other Information” in the Valuation of Foreign Income for U.S. Multinationals
Bibliographic record
Abstract
In this paper, we examine investors' valuation of the domestic and foreign components of total earnings after controlling for information beyond current earnings. Our sample consists of U.S. multinationals during the 1985-2002 period. In a prior study, Bodnar and Weintrop (1997) find that investors place a higher weight on foreign earnings than on domestic earnings in valuing securities, and that this finding can be explained in part by the higher growth opportunities in foreign markets. While this explanation is intuitively appealing, other possible explanations include the varying importance of information other than current accounting earnings in pricing securities and the possible misspecification of their model. One potentially important source of other information is information contained in revisions of analysts' forecasts of future (abnormal) earnings and terminal values. Excluding this information from the regression specification potentially leads to a correlated omitted variables problem. In this paper, we use the Liu and Thomas (2000) proxy for “other information,” which is derived from analysts' revisions of near-term and long-term earnings forecasts and discount rate changes. Including the “other information” variable greatly improves the explanatory power of the returns—earnings regression. Consistent with our predictions, we find that the bias resulting from excluding other value-relevant information has a greater effect on foreign earnings than on domestic earnings. Foreign earnings are no longer incrementally value relevant when we control for “other information.”
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".