Investor Sentiment, Disagreement, and the Breadth–Return Relationship
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
We study the cross-sectional breadth–return relation by assuming that investors subject to market sentiment hold a biased belief in the aggregate. With a dynamic multiasset model, we predict that the breadth–return relationship can be either positive or negative depending on the relative strength of two offsetting forces—disagreement and sentiment. We find evidence consistent with our predictions. The breadth–return relationship is positive when the sentiment effect is small. However, the relationship becomes negative when (i) the time-series variation of market-wide sentiment is high and (ii) the cross-sectional dispersion of firm-specific exposure to market-wide sentiment variation is large. Our unified framework reconciles a few seemingly inconsistent empirical studies in this literature and explains puzzling cross-sectional return patterns observed during the Internet bubble and the subprime crisis periods. This paper was accepted by Brad Barber, finance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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 it