Experimental Research on Stock Trend Analysis Based on News Sentiment Labeling
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 study, we aim to predict stock market trends based on news using a special type of AI model to confront with the chaos of the stock market and the limitations of traditional models that ignore public opinion. We developed a business-oriented sentiment labeling system based on a standard sentiment labeling system and real financial logic, which can achieve 89.0% classification accuracy. On this basis, we constructed an improved prediction model trained on multiple financial data (transaction, fundamental, news, etc.). The method is to directly integrate and test. The results show that the model has a good prediction effect, and the R^2 value on the test set is 0.80. The experimental results show that, compared with the model without news sentiment features, the model with news sentiment features is more likely to be improved, especially when the data scale is large. This work proves that the news-based artificial intelligence model with business logic can improve the prediction effect of finance and quantitative trading.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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