1Research Scholar, Faculty of Computational Sciences, GNA University, Phagwara-144401, Punjab. Contact no: 8360857556. Email: raghav.gupta680@gmail.com. ORCID: 0000-0001-9791-4287
2Associate Professor, School of Engineering, Design & Automation, GNA University, Phagwara-144401, Punjab. Email: nipun.chhabra@gnauniversity.edu.in. ORCID: 0000-0001-5318-5043
Background: This study comes up with a hybrid deep learning model to forecast the prices of stocks of Nifty LargeMidcap 250 Index for five years. It makes use of CNN-LSTM hybrid structure. Sentiment analysis combines News on the social media. Wavelet denoising, PCA dimensionality reduction and walk-forward validation are also methodology. Stock Data represents NSE archives 2010-2024.
Results: Results indicate that CNN-LSTM is better in metrics than LSTM and GRU baselines. RMSE is 5.37 in the case of BPCL and 15.58 in the case of TATA Motors in CNN-LSTM. All stocks show the same patterns of low-error in MAE. MAPE shows outstanding 0.03 values of INFY and TATA Motors. The maximum R² is 0.9976 of INFY and 0.995 of CIPLA. The model fills the long-horizon forecasting gaps in the case of blended indices.
Conclusion: Findings justify practical usefulness of CNN-LSTM as it provides the best results.
Keywords: CNN-LSTM, RMSE, Nifty LargeMidcap 250, LSTM, MAPE, LSTM, TATA MOTORS, sentiment, technical indicators, SHAP.
How to cite this article: Gupta R, Chhabra N. Hybrid Deep Learning Technique for Stock Price Prediction. Int J Drug Deliv Technol. 2026;16(2): 662-668. DOI: 10.25258/ijddt.16.2.70
Source of support: Nil.
Conflict of interest: None