A reformulated asset pricing model based on contrarian strategies
Bibliographic record
Abstract
Purpose Researchers have proposed characteristics‐based pricing models as an alternative to risk‐based pricing models. While supported empirically, these characteristic‐based models lack theoretical support. This paper seeks to reformulate an asset‐pricing model (RAPM) to demonstrate why firm characteristics help to explain stock returns. Design/methodology/approach The RAPM is grounded in an economic setting where two groups of agents hold different beliefs about firm fundamental values, and the more sophisticated group (rationals) adopts contrarian strategies against the naïve group (quasis). The model is derived in a static equilibrium within the consumption‐investment framework with heterogeneous agents. Findings The key theoretical result is a parsimonious equation of cross‐sectional expected returns that not only are specified by the traditional risk‐return relation, but also are determined by contrarian adjustments at both market‐wide and firm‐specific levels. When the model is taken to empirical specifications, it leads to consistent explanations for the behaviors of growth and value stocks, and for size and book‐to‐market effects. Research limitations/implications The RAPM is a one‐period model that assumes that “rationals” have perfect knowledge about “quasis” sentiment parameter and their relative market weights. In future research, it is planned to extend this static model to multiple periods to incorporate a learning process by which “rationals” learn these parameters over time. Practical implications The RAPM clearly identifies four criteria for implementing arbitrage opportunities in investments. These criteria formalize the common practices in the mutual/hedge fund industry. Originality/value The paper develops an original framework that formally supports the characteristics‐based models. It offers insights for researchers in behavioral finance and guidelines for investment practitioners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".