Adaboost-SVM Multi-Factor Stock Selection Model Based on Adaboost Enhancement
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, the applications of machine learning techniques to perfect traditional financial investment models has gained a widespread attention from the academic circle and the financial industry. This paper takes CSI300 stocks as the object of the research, uses Adaboost to enhance the classification ability of original linear support vector machine, and combines all major factors to build Adaboost-SVM multi-factor stock selection model based on Adaboost enhancement. In the backtesting analysis, the stock selection strategy of original linear support vector machine was compared with the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement. The result shows that the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement possesses stronger profitability and smaller income fluctuation than the original algorithm model.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it