Bibliographic record
Abstract
Predicting rare events in financial markets is a major challenge, and the traditional models of predicting extreme price Features haven't worked well for drastic price changes. The study presents a novel model for timing and forecasting unusual equity returns, particularly a 30% rise in ten trading days. Additionally, the methodology employs a two-stage pipeline that combines an advanced machine learning classification algorithm or model, such as Gradient-Boosting, Random Forest, or Support Vector Machine, with a Generalized Additive Model (GAM) to interpret nonlinear feature extraction. The most challenging issues in financial prediction are also taken into account by the framework, such as class imbalance using unique prediction metrics like PR-AUC (Area Under the Percision-Recall Curve) and Precision at K and dynamic risk control using quantile-based take-profit and stop-loss strategies. With a PR-AUC of 0.72—much higher than that of conventional techniques—the analysis shows that XGBoost produces better results. By providing a solid, logical framework for forecasting infrequent occurrences in erratic markets, the study advances our understanding and has applications for algorithmic trading patterns and risk management instruments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".