Bibliographic record
Abstract
The main goal of this paper is to introduce an innovative market anomaly relating to “time” during the overnight post-market session and therefore characterized as a temporal market anomaly. Anomalies in the markets appear from time to time and test the efficient market hypothesis. Many investors and traders believe that the markets follow the efficient market hypothesis. According to this theory the current price of a security (trading instrument) reflects all public and private information about that security (instrument). Changes in price are due to insider information, current news, or sudden events, which are impossible to predict. Hence, security’s price action follows the path of a random walk, the hypothesis and argument of which states that current price is not dependent on past price and is normally or abnormally distributed over time. In financial and economical literature, many studies have presented approaches about what the academics call “market anomalies” and according to literature the anomalies are classified in three categories: Fundamental, Technical, and Calendar-based anomalies. In this article another class of market anomalies is introduced, that simply could be referred to as “temporal” because of the timing functionality involved. Finally, I will discuss one of these “temporal” anomalies, called the overnight return temporal market anomaly. The presented research shows that momentum profit accumulates entirely overnight, while profit on all other strategies occurs entirely intraday. These findings strongly reject classical theories of intraday versus overnight returns.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".