Analysts’ EPS-Decreasing Exclusions and Target Price Forecasts
Bibliographic record
Abstract
We examine the relation between individual analysts’ exclusions that result in lower street earnings per share (EPS) forecasts than their EPS forecasts based on generally accepted accounting principles (i.e., EPS-decreasing exclusions) and the optimism of their target price forecasts. We document that analysts’ EPS-decreasing exclusions from their annual forecasts mainly relate to positive nonrecurring items already reported by the firm. We find that analysts’ EPS-decreasing exclusions are associated with more optimistic target prices. Our results also suggest that analysts’ EPS-decreasing exclusions contribute to the optimism in their target prices by enabling analysts to project higher earnings growth. Further analyses reveal that the relation between analysts’ EPS-decreasing exclusions and target price optimism is attributable, at least in part, to analysts’ strategic incentives for issuing favorable valuations. This paper was accepted by Eric So, accounting. Funding: S. A. Larocque acknowledges the financial support of the KPMG Fellowship at the Mendoza College of Business. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03627 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".