Global Stock Market Prediction Using Transformer-Based Deep Learning Techniques
Bibliographic record
Abstract
Deep learning approaches’ use in financial market forecasting has recently drawn a lot of attention from both investors and scholars. The Transformer framework, initially created for natural language processing, is used in this study to propose a revolutionary approach for forecasting the stock market indices of seven different economies: China, Canada, India, Japan, Russia, the United Kingdom, and the United States. The well-known Transformer architecture, which excels at capturing complex non-linear patterns, is modified to study the unique dynamics of each nation’s stock market. The model successfully determines the basic principles driving market behavior by using an encoder-decoder structure and a multi-head attention mechanism. The aforementioned indices are used in back-testing trials, which cover a variety of economic environments. The outcomes demonstrate that the Transformer performs better than conventional techniques, with Mean Squared Error (MSE) acting as a crucial evaluation criterion. Notably, the model shows promise for investors in these widely separated markets to receive excess profits. This study offers a thorough analysis of stock market forecasting, expanding its relevance to a wider global context. comprehension of how this strategy might adjust to various financial ecosystems is improved by the addition of seven unique economies. Future research may go more deeply into applications that are industry-specific and investigate potential extensions to additional international markets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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 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".