Stock Price Prediction and Investment Strategy via Machine Learning Model Fusion
Bibliographic record
Abstract
This study explores stock price prediction using multiple machine learning models and enhances accuracy via model fusion with XGBoost as the meta-model and Linear Regression, Support Vector Machine, and Random Forest as the base models. We trained and compared various models (e.g. linear regression, random forests, and support vector machine) using historical stock data to predict future stock prices based on daily closing prices and various technical indicators. The experimental data presented a clear discrepancy in the performance of the various models. Such unification of the models through composite technology offered us a chance to increase not only the accuracy but also the stability of the forecasting process. Results of this technique gave us the opportunity to design profit-making strategies, which were evaluated through a forward-looking simulation framework to ensure out-of-sample validity. The primary goal of this study is to propose a new method for enhancing financial forecast accuracy and machine learning-driven investment decision making.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".