A Tool based on ML-driven Graphical Model for Stock Price Prediction by Leading Indicators
Bibliographic record
Abstract
Stock prediction has become an emerging issue in recent decades and many studies have incorporated it with social systems to provide a better accuracy for the prediction results. Machine learning (ML) model is widely studied and developed to show better performance in data analytics and prediction, which can be also applied in the stock markets for the price prediction.To be better applied in the stock market for price predication, it is necessary to finalize a ML-driven toolbox that can be easily adopted into the stock market. In this paper, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network to improve the prediction accuracy of stock trading is proposed. The proposed model is called SSACNN, short form of stock sequence array convolutional neural network that collects data including historical data of prices and its leading indicators (options / futures) for a stock to take an array as the input graph of CNN framework. In our experimental results, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".