Predicting Next Quarter Nifty 50 Price using Genetic Algorithm and Support Vector Regression
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
The stock market is difficult to predict due to uncertainty and complexity. Still, several methods are utilized to predict stock prices, such as fundamental analysis, technical analysis, and statistical and machine learning methods. Most of the studies, which use daily historical price, predicts next-day price. However, next-day price is not so useful for investors or short-term traders. In this study, genetic algorithm optimized support vector regression (GA-S VR) predicts next quarter's Nifty 50 index price using daily historical dose price. Further, the study compares the GA-SVR prediction with Support Vector Regression (SVR) based on Root Mean Square Error (RMSE). The result shows that GA-S VR improves the prediction of S VR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it