Fast and effective predictability filters for stock price series using linear genetic programming
Bibliographic record
Abstract
A handful of researchers who apply genetic programming (GP) to the analysis of financial markets have devised predictability pretests to determine whether the time series that is being supplied to GP contains patterns that can be predicted, but most studies apply no such pretests. By applying predictability pretests, researchers can have greater confidence that the GP system is solving a problem which is actually there and that it will be less likely to make questionable investment decisions based on non-existent patterns. Previous work in this area has applied regression to randomized versions of time series training data to create a functional model that is applied over a future window of time. This work presents two types of predictability filters with low computational overhead, namely frequency-based and information theoretic, that complement the previous function-based continuous output predictability models. Results indicate that either filter can be beneficial for particular trend types, but the information-based filter involves a greater chance of missing opportunities for profit. In contrast, the frequency-based filter always outperforms, or is competitive with, the filterless implementation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".