AEformer: Asymmetric Embedding Transformer for Stock Market Prediction based on Investor Sentiment
Bibliographic record
Abstract
Abstract Stock market prediction is an essential topic in economics. However, owing to the noise and volatility of the stock market, timely market prediction is generally considered one of the most challenging problems. Several researchers have introduced investor sentiment into stock prediction models and have achieved good results. Applying investor sentiment to high-frequency stock price forecasts can lead to risk aversion and improved returns. We have designed a model for high-frequency stock price prediction known as asymmetric embedding transformer (AEformer) that uses investor sentiment. We filtered stock comments using category information and enhanced the utilization of investor sentiment for stock prediction by incorporating an asymmetric embedding layer combined with a channel-wise independent self-attention mechanism. The experimental results show that AEformer outperforms the other models in high-frequency stock predictions using investor sentiment. Moreover, the asymmetric embedding layer is effective in improving the forecasting performance of transformer-based models.
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How this classification was reachedexpand
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 | 0.072 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".