Automatic Stock Price Prediction and Classification Based on Hybrid with AI Feature Selection Method
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
In this research, we investigate the problem of automatically predicting and classifying stock prices, with an eye towards creating and testing a Hybrid AI Feature Selection Method. This work employs a fictitious dataset to offer a new method that integrates Genetic Algorithm (GA) and Recursive Feature Elimination (RFE) to isolate the most important characteristics for predicting stock prices and classifying market fluctuations. The findings demonstrate that the hybrid strategy is effective in reducing the complexity of features and greatly improving model performance over more conventional methods. Furthermore, a simulation of a trading strategy based on the categorization findings reveals its potential to produce more efficient and successful investment methods, highlighting the practical relevance of this study. This research contributes to the developing field of financial technology by laying the groundwork for a new way of thinking about financial prediction and decision making, giving professionals and investors access to cutting-edge resources that can help them make better, more profitable choices.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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