Turn-of-the-month and Intramonth Anomalies and U.S. Macroeconomic News Announcements on the Thinly Traded Finnish Stock Market
Bibliographic record
Abstract
Evidence from the U.S. stock market as well as from major European stock markets has lately suggested that the turn-of-the-month (hereafter TOM) and intramonth anomalies occur because of major U.S. macroeconomic news announcements that are released around the TOM. World-wide markets are becoming more integrated and therefore in this study we hypothesize that major U.S. macroeconomic news announcements are also the cause for the TOM and intramonth effects on the thinly traded Finnish market. This study uses Finnish data to first identify significant TOM and intramonth effects and second to investigate whether these anomalies arise because of the clustered major U.S. macroeconomic news announcements. Both so-called calendar anomalies are evident, but after controlling for the effect of the major U.S. news releases the anomalies disappear, resulting in further evidence for the significance of the clustered U.S. macroeconomic news announcements. The results of this study are in line with earlier findings and claim that the TOM and intramonth anomalies are driven by the clustered release of major U.S. macroeconomic news announcements.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".