A Deep Neural Network for Stock Price Prediction
Bibliographic record
Abstract
Abstract The stock market is a large, complex system that consists of many human factors. Due to the human action’s randomness to the market environment, the market behavior is usually erratic. In this case, many previous works were trying to find out an automatic prediction model that can capture relationships between stock market prices and the surroundings of the stock markets. Recently, a simple LSTM-attention mechanism was proposed to advance the prediction accuracy of previous works. However, the simple LSTM with attention mechanism only demonstrates how the past data influence the next. This forward-only relationship does not reflect a backward relationship, which is how the future data is related to the past data. To enforce the backward relationship on the attention-LSTM model, a multilayer bidirectional LSTM model with an attention mechanism is proposed. A bidirectional LSTM layer that can encode the data relationship in both directions is leveraged. Then attention mechanism is applied to introduce the model with the ability to focus on critical content and ignore the disturbance. Experimental result shows that the attention-BiLSTM model has a coefficient of the determinant of 0.9940, which is better than the baselines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".