Modernizing the Stock Pricing Mechanism: An Effective Path to Improve the Stock Market Efficiency
Bibliographic record
Abstract
This study investigates whether regulating the stock pricing mechanism by adopting an agreed-upon efficient stock valuation model can enhance the stock market efficiency. The study involves a simulated stock market experiment with 65 traders who provide daily stock price predictions for a virtual company under unregulated and regulated scenarios. In the regulated scenario, traders agree on one of three valuation models to generate stock prices. Moreover, 20 evaluators acted as “Homo-Economicus” to determine a stock's fair value trend, serving as a benchmark to assess the information efficiency of the simulated market. The study finds that the NAPV-regulated market shows a strong linear relationship and high R-squared, indicating the highest level of information efficiency, while the DDM- and RIM-regulated markets show moderate correlations. The study suggests that modernizing the stock pricing mechanism, by regulating shareholders to mutually agree on one efficient stock valuation model to be used by the company to generate fair values alternative to market prices, could significantly enhance the stock market efficiency by focusing on fundamentals rather than irrational speculators. However, model choice matters, as NAPV explains more variation. The study suggests that appropriate regulation is crucial for realizing this potential. Although the results are promising, limitations like small evaluator samples, inability of models to always generate stock values, and trader biases should be considered. Future research with larger samples and more models could strengthen these insights.
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.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".