A PREDICTIVE MODEL FOR STOCK PRICES BASED ON TRANSFORMER AND UTILIZING MULTIMODAL DATA
Bibliographic record
Abstract
Stock market prediction necessitates effective multimodal data integration and robust uncertainty quantification. This paper proposes a novel Transformer-based architecture addressing two critical limitations of existing approaches: static cross-modal interaction and deterministic output assumptions. Our framework introduces (1) a multimodal subspace attention mechanism that projects numerical and textual features into orthogonal subspaces, enabling disentangled learning of modality-specific interactions through multiple attention heads, and (2) a dynamic gated recalibration module that adaptively adjusts modality contributions using time-variant weights. Evaluated on Technology Select Sector SPDR Fund (XLK) data with market sentiment feeds, the model achieves higher directional accuracy than conventional Transformers while reducing volatility period prediction errors. The integrated uncertainty quantification module further provides statistically reliable confidence intervals, verified through backtesting.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".