Short-term Stock Market Price Trend Prediction Using a Customized Deep Learning System
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 big data era, deep learning solution for predicting stock market price trend becomes popular. We collected two years of Chinese stock market data according to the financial domain, proposed a fine-tuned stock market price trend prediction system with developing a web application as the use case, meanwhile, conducted a comprehensive evaluation on most frequently used machine learning models and concludes that our proposed solution outperforms leading models. The system achieves an overall trend predicting accuracy of 93%, also achieves significant high scores in other machine learning metrics score in the meantime. Thus, this work provides a solid foundation for further price prediction by classifying the price trend accurately. With the detail-designed evaluation on prediction term lengths, feature engineering and data pre-processing methods, this work also contributes to the stock analysis research community in both financial and technical domain.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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