Information content of option prices: Comparing analyst forecasts to option-based forecasts
Bibliographic record
Abstract
The asset pricing literature has been producing increasingly complex and computationally intensive models of stock returns. Separately, professional analysts’ forecast stock returns. Are the sophisticated methods found in the asset pricing literature achieving different forecasts to those of analysts?’ Do the two forecasts’ even capture the same information? In this paper, I hypothesize that analyst forecasts and forecasts constructed using option prices will be different because they place different weights on available information. Using hypothesis tests and quantile regressions, I find that option-based forecasts are statistically significantly different from analyst forecasts at every level of the forecast distribution. Using cross-sectional regressions, I find that the difference originates in the weighting structure of the information sets used to create the forecasts: option-based forecasts incorporate information about the probability of extreme events more heavily while analyst forecasts focus on information about firm and macroeconomic fundamentals. • We compare analyst and option-based forecasts to determine if (1) they are different and (2) if the information they contain is different. • Option-based forecasts and analyst forecasts are statistically significantly different at all levels of the forecast distribution. • Differences arise from the weighting structure of the information sets used to create forecasts. • Option-based forecasts heavily incorporate information about the probability of extreme events. • Analyst forecasts place more emphasis on firm and macroeconomic fundamentals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".