An Optimization based Stock Price Forecasting With Graph Neural Network
Bibliographic record
Abstract
Stock prices are naturally volatile and exposed to significant noise, making accurate forecasting a difficult task. Although classic approaches such as linear model ARIMA as well as non-linear methods LSTM have been effectively utilized, there is still plenty of room for designing sophisticated models that can further reduce forecasting errors. The main limitation of these methods is their inability to capture relationships between different stocks. Recently, Graph Neural Networks have been extensively applied to overcome this limitation but still there is a gap to further improve their performance. Hence, in this research study, optimization-assisted graph neural networks (GNNs) are proposed for stock price forecasting. The proposed model efficiently optimizes the parameters of configurations and parameters of GNN including graph structures. This optimization has been designed using the swarm-intelligence algorithms mainly the Particle swarm optimization (PSO) and Grey-Wolf Optimization (GWO). The findings of the study show that the proposed optimization-assisted GNN outperforms the existing models of stock price forecasting on the S&P500 stock dataset with the lowest MAE error of 0.01087.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".