2009, The Impact of Management Earnings Forecasts on Firm Risk and Firm Value, University of Western Ontario, working paper
Bibliographic record
Abstract
This study investigates whether voluntary management disclosure of earnings forecasts influences investors ’ long-term assessment of firm risk and firm value. We control for possible endogeneity between various firm-specific characteristics and the voluntary issuing of different types of management earnings forecasts by utilizing a two-stage Heckman treatment analysis. We find a significant negative relationship between the issuance of management earnings forecasts and a variety of risk measures including idiosyncratic risk, stock return volatility, beta, and bid-ask spreads suggesting the issuance of management earings forecasts reduces information asymmetry and thus reduces risk. We find a significant positive relationship between management earnings forecasts and firm value as captured by Tobin’s Q when the issuance of earnings is more precise (i.e., point estimates versus ranges or qualitative guidance), and a stronger positive relationship when the precise forecasts are also credible, i.e., when actual earnings meet or exceed management forecasts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".