Stock Market Prediction from Sentiment and Financial Stock Data Using Machine Learning
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
Forecasting stock values is challenging due to market volatility and numerous financial variables, such as news, social media, political changes, investor emotions, and the general economy. Predicting stock value using financial data alone may be insufficient. By combining sentiment analysis from social media with financial stock data, more accurate predictions can be achieved. We use an ensemble-based model employing multi-layer perceptron, long short-term memory, and convolutional neural network models to estimate sentiment in social media posts. Our models are trained on AAPL, CSCO, IBM, and MSFT stocks, using financial data and sentiment from Twitter between 2015-2019. Results show that combining financial and sentiment information improves stock market prediction performance, achieving a next-hour prediction performance of 74.3%.
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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 | 0.009 | 0.014 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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