Bibliographic record
Abstract
Price discrepancies, although at odds with mainstream finance, are persistent phenomena in financial markets. These apparent mispricings lead to the presence of ‘arbitrageurs’, who aim to exploit the resulting profit opportunities, but whose role remains controversial. This article investigates the impact of the presence of arbitrageurs in rational financial markets. Arbitrage opportunities between redundant risky assets arise endogenously in an economy populated by rational, heterogeneous investors facing restrictions on leverage and short sales. An arbitrageur, indulging in costless, riskless arbitrage is shown to alleviate the effects of these restrictions and improve the transfer of risk amongst investors. When the arbitrageur lacks market power, they always take on the largest arbitrage position possible. When the arbitrageur behaves noncompetitively, in that they take into account the price impact of their trades, they optimally limit the size of their positions due to decreasing marginal profits. In the case when the arbitrageur is subject to margin requirements and is endowed with capital from outside investors, the size of the arbitrageur’s trades and the capital needed to implement these trades are endogenously solved for in equilibrium.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".