Visualizing the Periods of Stock Prices Using Non-Harmonic Analysis of the NASDAQ Composite Index Since 1985
Bibliographic record
Abstract
Abstract: The prediction of stock prices is studied extensively, because of the demand from private investors and financial institutions. However, long-term prediction is difficult due to the large number of factors that affect the real market. Previous research has focused on the fluctuation patterns and fluctuation periodicity of stock prices. We have likewise focused on the periodicity of stock prices. We have used a new high-resolution frequency analysis (non-harmonic analysis) method can solve the previous problem of the frequency resolution being low. As a consequence, we have succeeded in visualizing the various periodicities of stock prices. The periodicity fluctuates gently in many periods, but we have confirmed that it fluctuated violently in periods when a sudden event occurred. We expect that this experimental result in combination with previous research will help increase predictive accuracy and will aid long-term prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".