Hybrid Machine Learning Models for Long-Term Stock Market Forecasting: Integrating Technical Indicators
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
Stock market forecasting is a critical area in financial research, yet the inherent volatility and non-linearity of financial markets pose significant challenges for traditional predictive models. This study proposes a hybrid deep learning model, integrating Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) with technical indicators to enhance the predictive accuracy of stock price movements. The model is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R2 score on the S&P 500 index over a 14-year period. Results indicate that the LSTM-CNN hybrid model achieves superior predictive performance compared to traditional models, including Support Vector Machines (SVMs), Random Forest (RF), and ARIMAs, by effectively capturing both long-term trends and short-term fluctuations. While Random Forest demonstrated the highest raw accuracy with the lowest RMSE (0.0859) and highest R2 (0.5655), it lacked sequential learning capabilities. The LSTM-CNN model, with an RMSE of 0.1012, MAE of 0.0800, MAPE of 10.22%, and R2 score of 0.4199, proved to be highly competitive and robust in financial time series forecasting. The study highlights the effectiveness of hybrid deep learning architectures in financial forecasting and suggests further enhancements through macroeconomic indicators, sentiment analysis, and reinforcement learning for dynamic market adaptation. It also improves risk-aware decision-making frameworks in volatile financial markets.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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