Market timing using strategists’ and analysts’ forecasts of S&P 500 earnings per share
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract

 
 
 This paper examines the bias in and usefulness of top-down and bottom-up consensus forecasts of earnings per share for the S&P 500 Index provided by market strategists and analysts to I/B/E/S. These forecasts exhibit a significant optimism bias that decreases over the 12 months up to release of actual earnings per share. The bias is significantly more pronounced for the bottom-up forecasts of analysts. Unlike the findings for country timing, we demonstrate that a stock market timer using switching rules based on the consensus forecasts of S&P 500 earnings or the directional switch in the consensus or in the number of switchers cannot generate a free lunch. © 2000 Elsevier Science Inc. All rights reserved.
 
 
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it