Language and Management Forecasts Around the World*
Bibliographic record
Abstract
ABSTRACT Speakers of weak future‐time reference (FTR) languages perceive the future as closer and more imminent. In this study, we examine the important question of whether the FTR properties of languages spoken by investors affect their demand for forward‐looking information, thereby influencing corporate management forecast practices in different countries. We predict that investors who speak weak‐FTR languages are more concerned about the future prospects of their investments and the ability of company management to respond to future changes, leading to a greater demand for management forecasts from these companies. We find that firms in weak‐FTR language countries exhibit a greater propensity for and frequency of issuing management forecasts and that they also issue more long‐horizon forecasts, compared to those in strong‐FTR language countries. Our results hold after controlling for other country‐level cultural factors. Within the same countries, firms with more foreign institutional ownership from weak‐FTR countries issue more (long‐horizon) management forecasts than their counterparts. Finally, firms from strong‐FTR countries significantly increase their issuance of (long‐horizon) management forecasts, after cross‐listing their stocks in Germany, a weak‐FTR country. This is the first study to examine language FTR as an antecedent to voluntary disclosures. We document a linguistic trait as a novel investor environment factor that shapes corporate voluntary disclosures and explains the cross‐country variations in management forecast practices.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".