How Analysts and Whisperers Use Fundamental Accounting Signals to Make Quarterly EPS Forecasts
Bibliographic record
Abstract
We examine the relative efficiency of whisperers’ and analysts’ forecasts of one-quarter-ahead earnings per share (EPS) and identify commonalities and differences in their use of fundamentals to forecast earnings. Results suggest that (a) fundamentals that focus on sales and cost of sales are relevant in explaining one-quarter-ahead EPS changes; (b) whisperers focus on cash flow fundamentals and accrual-based earnings measures in their one-quarter-ahead forecasts, whereas analysts focus on only cash flow fundamentals; and (c) although neither analysts nor whisperers fully incorporate information contained in fundamentals and accrual-based earnings measures in their forecasts, whisperers’ earnings forecast model (forecast errors model) exhibits higher (lower) explanatory power than that of analysts. We also examine robustness of our results by reestimating the models using a two-way random-effects panel data estimator. Although our conclusions remain the same, more statistically significant fundamentals emerge in panel regression results. Evidence presented in this article is consistent with (a) whisperers being different from analysts and (b) whisper forecasts containing unique incremental information beyond that of analysts’ forecasts. Market participants may want to consider using both forecasts when making investment decisions.
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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 | 0.007 | 0.059 |
| 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 | 0.000 |
| 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 | 0.001 |
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".