Select High-Quality Stock with Random Forest Classifier
Bibliographic record
Abstract
This study explores the application of a Random Forest Classifier to predict stock performance among S&P 500 companies based on financial and stock performance data. Financial indicators such as Return on Equity (ROE), Return on Assets (ROA), Market Value (MV), Price-to-Book Ratio (PB), and Price/Earnings-to-Growth Ratio (PEG) were combined with stock performance data collected between September 2023 and June 2024. The data was merged into a unified dataset and split into training and testing sets. The model was trained using financial features from September 2023 to March 2024, while performance predictions were tested on data from March 2024 to June 2024. The Random Forest model achieved an accuracy of 63.92%, highlighting its effectiveness in identifying unsatisfied stocks but showing moderate accuracy for higher performance categories. The findings underscore the model's potential for data-driven stock selection, while also suggesting that further improvements, such as feature selection optimization and additional data integration, could enhance prediction accuracy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".