Bibliographic record
Abstract
The main goal of this paper is to introduce and discuss the temporal dimension and the subsequent (time-series) functionalities of two well-known technical market anomalies - the momentum anomaly and the trend-reversal anomaly. Our approach not only challenging the efficient-market hypothesis but also has a temporal dimension because it uses the “psychological time” at the beginning of a move, as a parameter in overnight post-market asset position trading strategies and daytime asset position strategies as well. Momentum and reversal have been documented extensively in financial literature and they are viewed, in the current paper, as temporal momentum and temporal trend-reversal anomalies because they are hard to fully explain within the standard static asset-pricing paradigm. A rational dynamic and temporal representative agent could explain and document better these anomalies and this is the case of this article. The presented research shows that momentum profit accumulates entirely overnight, while trend-reversal profit occurs entirely intraday. These findings reject classical theories of intraday and overnight returns. Hence, (i) a well designed overnight-position return strategy based on temporal momentum anomaly; and (ii) a well designed intraday-position return strategy based on temporal trend-reversal anomaly, could gain benefit at the expense of long-term investors and hedgers. After back-testing our research in available 16-year data, we found that overnight-position speculators profit from the proposed temporal momentum trading strategy approach at the expense of hedgers, and daytime swing traders profit from the proposed temporal trend-reversal trading strategy approach at the expense of long-term 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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".