Representativeness Heuristic Can Cause Asset Price Underreaction to New Information in a Competitive Securities Market
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Bibliographic record
Abstract
In the literature, representativeness heuristic is commonly viewed as a cause of asset price overreaction to new information. This paper proves that representativeness heuristic can cause asset price under-reaction to new information in a competitive securities market. Specifically, there is one risk-free asset and one risky asset. Both rational and heuristic traders trade can against each other or against noise traders whose demand is random. The payoff of the risky asset is unknown but all traders receive an informational signal about the risky asset’s payoff before any trading takes place. Due to the representativeness heuristic, the updated mean of the risky asset’s payoff for heuristic traders is higher (lower) than that for rational traders when the realization of the informational signal is above (below) the expected payoff of the risky asset. The results of the paper suggest that regardless of noise traders being net buyers or sellers, the representativeness heuristic causes the asset price to overreact to new information close to the expected payoff of the risky asset and causes the asset price to underreact to new information far above or below the expected payoff of the risky asset.
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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) | 0.000 | 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 it