Optimization of Random Forest Algorithm and Research on the Effectiveness of its Application in Stock Index Forecasting
Bibliographic record
Abstract
Random forest algorithm is a kind of integrated learning algorithm with strong universality, high prediction accuracy and not easy to overfitting, and strong stability in stock index prediction application.This study constructs a stock index prediction model based on the random forest algorithm, and predicts the stock index futures price state according to the iteration of the decision tree in the random forest algorithm.Then we propose to use the regular term and ARMA-GARCH time series forecasting model to optimize the overfitting and large forecasting errors in the Random Forest model to achieve the construction of stock index forecasting optimization model.It is verified that the average absolute error of the random forest optimization model proposed in this paper is only 0.0316 in stock index forecasting, and the robustness in stock index forecasting is excellent.The empirical application results of stock index forecasting show that the accuracy of this paper's model for CSI 300 and CSI 500 indexes is above 90%, and the total return of the strategy during the backtesting period is relatively high.The practical application of the stock index forecasting model proposed in this study has the value of further research, which can provide reference and guidance for investors.
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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 | 0.052 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".