Stock Prediction using Deep Learning and Sentiment Analysis
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
Stock prediction has been a popular research topic and researchers have done a lot of work in this field. Due to its stochastic nature, predicting the future stock market remains a very difficult problem. This paper studies the application of attention-based LSTM deep neural network in future stock market movement prediction. We also build stock aggregate dataset and individual dataset including stock history data, financial tweets sentiment and technical indicators in the US stock market. The experiment studies the time sensitivity of finance tweet sentiment and methods of collective sentiment calculation. This paper also experiments on conventional LSTM and attention-based LSTM for performance comparison. We find the finance tweets that are posted from market closure to market open in the next day has more predictive power on next day stock movement. The weighted sentiment on max follower on StockTwits also outperforms other methods. In our experiment, the result on our individual stock dataset shows a similar pattern like normal distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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