The profitability of trading US stocks in Quarter 4 - evidence from trading signals emitted by SOI and RSI
Bibliographic record
Abstract
Since the yields of trading stocks are affected by numerous elements, this study aims to explore whether the profitability of trading stock with the use of technical trading rules, such as Stochastic Oscillator Indicators (SOI) and Relative Strength Index (RSI), would matter in the fourth quarter of the year. By employing the data of Dow Jones 30 (DJI 30) and NASDAQ 100 (NDX100) from 2011 to 2020 and from 1991 to 2020, this study reports that investors applying SOI and RSI would have higher cumulative abnormal returns (CARs) in Quarter 4 as compared with those in Quarters 1–3, respectively. The revealed findings indicate that the quarterly effect should be taken into account by investors when trading DJI 30 and NDX100, the representative indices for US stock markets. More importantly, this study may contribute to the existing literature due to the rare discussion of this interesting issue in the past research.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".