Stock Price Prediction of Walmart Based on Combination of SVM and LS-SVM Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the most significant operations in the finance sector is stock trading. The stock market is an essential part in the economy of a country and serves as the indicators of the situation of a country’s economy as the stock prices go up or down. Therefore, stock price prediction, the behavior of attempting to predict the potential worth of a corporation or any financial instruments successfully, will maximize investor’s gain, enhance market’s confidence, and help government policymakers to make economic decisions. In order to forecast the price of a stock, a machine learning approach is constructed in this study. The suggested algorithm includes random forest, support vector machine (SVM), and least square support vector machine (LS-SVM). In particular, the random forest is employed to select the most important features from the technical indicators calculated for stock price prediction. The SVM and the LS-SVM models are employed to predict the daily stock prices. Besides, R-Squared (R²), mean squared error (MSE) and mean absolute error (MAE) are used for model evaluation. According to the results, both SVM and LS-SVM models can predict stock price well, but both algorithms are not suitable for large datasets, and overfitting problem exists. These results shed light on guiding further exploration of stock price predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it