XGBoost-LSTM for Feature Selection and Predictions for the S&P 500 Financial Sector
Bibliographic record
Abstract
Financial industry researchers have long been committed to identifying factors that can predict trends in the financial sector of S&P 500, despite these factors often being difficult to discover. This article, through the combination of the Xgboost regressor and the shap summary plot, has mined and continuously optimized a potential excellent factor combination. Also, by utilizing the Xgboost regressor and LSTM models, it has achieved good prediction accuracy on the test set. This research gets the following results: First, the Xgboost regressor, in combination with Shap, has identified the seven most excellent factors from an initial combination of nine factors. Second, after imparting the final seven features to LSTM, the MSEs of the predictions made by Xgboost regressor and LSTM are 0.0003 and 0.0004, while the running times for Xgboost regressor and LSTM are 27 minutes and 16 minutes. Consequently, these results indicate that in the future predictions of finance sector index, investors may use the Xgboost-LSTM model for selecting effective factors and making accurate predictions efficiently.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".