Can Stock Price Manipulation be Prevented by Granting More Freedom to Manipulators
Bibliographic record
Abstract
Allen and Gale (1992) construct a model to show that stock price manipulation is possible. The time structure of their model allows manipulators to pretend as “informed” traders, so that the local investors cannot distinguish what type of entrant they are facing. When the type of the entrant becomes known to the local investors it is already too late to make any use of that information. This paper shows an institution can be designed in a very natural fashion which induces different behaviors on the part of manipulators and “informed” traders at the beginning of the process. The institution designed roughly consists of entitling the entrants to resell stocks at a later date as well if they wish to do so. As this reasoning is also accessible to manipulators, the designed institution deters them from entering the market. Regarding the informed traders, their expected gain from entering the stock market may or may not be positive contingent on the basic parameters of the model. There are cases, however, when there is an improvement in the expected total gain of the local investors.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".