AI-Powered Stock Forecasting: A Graph-Based Approach for NEPSE
Bibliographic record
Abstract
The stock market is a cornerstone of the financial ecosystem, yet forecasting price movements remains a formidable challenge due to the dynamic and interconnected nature of influencing factors. While conventional prediction models often fail to adequately represent these complex relationships, Graph Neural Networks (GNNs) have emerged as a promising alternative, offering superior accuracy by modeling financial data as interconnected graphs. In this study, we introduce a visibility-based graph transformation technique to convert stock market features into a structured network, capturing long-memory dependencies. We then apply Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to analyze trends and predict market behavior. Our experiments reveal that GCN outperforms GAT in modeling financial graph structures, demonstrating its robustness in deciphering intricate market relationships. These results underscore the potential of GNN-driven approaches in stock market forecasting, providing actionable insights for investors and advancing predictive analytics in the Nepalese stock market (NEPSE).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".