Stock Market Price Prediction Using Deep Learning and Ensemble Methods | IJCT Volume 13 – Issue 2 | IJCT-V13I2P81
International Journal of Computer Techniques
ISSN 2394-2231
Author
Varikallu Praveen kumar, Sunkara Pavan Naresh, Bandaru Teja Murthy, Mrs.v. Elavenil
Abstract
Stock market price prediction is one of the most challenging and critical problems in the domain of financial forecasting. The inherently volatile and nonlinear nature of stock market data makes accurate prediction a formidable task. This paper presents a comprehensive deep learning and ensemble-based framework for stock market price prediction by integrating Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and traditional machine learning algorithms including Random Forest, Support Vector Machine (SVM), and ARIMA models. Technical indicators such as Moving Averages (MA), Relative Strength Index (RSI), MACD, and Bollinger Bands are extracted as features. Experiments are conducted on historical data from major stocks including Apple (AAPL), Google (GOOGL), and Amazon (AMZN). The proposed ensemble model achieves a Root Mean Square Error (RMSE) of 2.11 and Mean Absolute Error (MAE) of 1.65, outperforming all individual baseline models. Results demonstrate the superiority of the hybrid approach for real-world financial time-series forecasting.
Keywords
Stock Market Prediction, Deep Learning, LSTM, CNN, Random Forest, Ensemble Methods, Financial Forecasting, Time-Series Analysis, Technical Indicators, Neural Networks.
Conclusion
This paper presented a comprehensive deep learning ensemble framework for stock market price prediction. By integrating LSTM, CNN-LSTM, and Random Forest models with technical indicator-based feature engineering, the proposed approach achieved superior predictive performance compared to traditional statistical and standalone ML/DL methods. The CNN-LSTM ensemble attained an RMSE of 2.11 and MAE of 1.65 on the AAPL test set, representing a 56.8% improvement over linear regression and a 9.8% improvement over standalone LSTM.
The results confirm that hybrid deep learning ensembles are highly effective for financial time-series forecasting. Future directions include incorporating real-time news sentiment via transformer models, integrating macroeconomic indicators, and extending the framework to cryptocurrency and forex markets. Explainability methods such as SHAP and LIME will also be explored to improve model transparency for practical deployment in financial institutions.
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How to Cite This Paper
Varikallu Praveen kumar, Sunkara Pavan Naresh, Bandaru Teja Murthy, Mrs.v. Elavenil (2026). Stock Market Price Prediction Using Deep Learning and
Ensemble Methods. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
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