Hybrid LSTM/GRU and Support Vector Regression Models for Stock Index Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forecasting stock market indices is a challenging task due to the inherent complexity, non-linearity, and stochastic nature of financial time series. Although deep learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, outperform traditional econometric methods in capturing temporal dependencies, their standalone implementations often lack robustness and generalizability. This study presents a novel hybrid framework combining recurrent neural networks with Support Vector Regression (SVR) to address these limitations. The proposed approach integrates the sequential learning capabilities of LSTM and GRU with the nonlinear regression strengths of SVR, achieving superior predictive performance across diverse global indices. Empirical results highlight significant improvements over baseline models in both accuracy and adaptability to varying market conditions. The hybrid framework demonstrates its effectiveness in merging the advantages of its components, providing robust, generalizable predictions with reduced susceptibility to overfitting. These findings pave the way for future research in hybrid financial forecasting methods and their practical applications.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it