Do Foreign Cash Holdings Generate Uncertainty for Analysts?
Bibliographic record
Abstract
This study examines whether foreign cash holdings increase the complexity of analysts’ forecasting tasks, thereby affecting their earnings forecasts’ properties. In forecasting future earnings, analysts face uncertainty in understanding the firm’s economic situation, especially if cash is held by foreign subsidiaries – given that it may be subject to investment inefficiencies. The lack of disclosure about foreign cash makes it difficult for analysts to anticipate and fully incorporate in their estimates the negative performance consequences of foreign cash holdings. Using a sample of U.S. multinational corporations and estimating their foreign cash holdings, we show that a firm with an average level of estimated foreign cash to total assets has a 12.5% higher forecast error and a 13.7% higher forecast dispersion (relative to firms without foreign cash). Moreover, we document that estimated foreign cash is negatively associated with future performance and that, in the presence of large amounts of foreign cash, financial analysts issue more optimistic forecasts. Cross-section analyses show that estimated foreign cash holdings affect analysts’ forecasts to a larger extent in the absence of disclosure, consistent with the idea that the lack of disclosure restricts financial analysts’ ability to fully incorporate in their estimates the effect of foreign cash.
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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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".