A New Measure of Market Inefficiency
Bibliographic record
Abstract
Financial crises, such as the Great Financial Crisis of 2007–2009 and the COVID-19 Crisis of 2020–2021, lead to high volatility in financial markets and highlight the importance of the debate on the Efficient Markets Hypothesis, a corollary of which is that in an efficient market it should not be possible to systematically make excess returns. In this paper, we discuss a new empirical measure—Excess Trading Returns—that distinguishes between market and trading returns and that can be used to measure inefficiency. We define an Inefficiency Matrix that can provide a complete, empirical characterization of the inefficiencies inherent in a market. We illustrate its use in the context of empirical data from a pair of model markets, where information asymmetries can be clearly understood, and discuss the challenges of applying it to market data from commercial exchanges.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".