Exploration of the Interaction Mechanism between Market Pricing Bias and Stock Returns from a Behavioral Finance Perspective
Bibliographic record
Abstract
Traditional financial theory, grounded in the "rational agent hypothesis" and the "efficient market hypothesis," posits that market prices fully reflect all available information and that stock returns are solely determined by systemic risk. However, frequent occurrences of asset price bubbles, crashes, and anomalies (such as momentum effects and value effects) in real markets indicate that market pricing biases are ubiquitous and difficult to eliminate entirely through arbitrage. Behavioral finance, by incorporating psychological theories, uncovers the driving role of investors' irrational behavior in pricing biases and further delves into the dynamic feedback mechanism between pricing biases and stock returns. From a behavioral finance perspective, this paper systematically reviews the causes and manifestations of market pricing biases, as well as their impact pathways on stock returns, and analyzes how the interaction between the two contributes to market dynamic imbalances. The research finds that the combined effects of investors' cognitive biases, emotional contagion, and limited arbitrage create a complex mechanism of "self-reinforcement" or "mean reversion" between pricing biases and returns. This has significant implications for the optimization of asset pricing models and the design of market regulatory policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".