Hybrid Deep Learning Framework For Nse Market Prediction Using Technical Indicators And Time-Series Analysis
Keywords:
NSE Stock Market, Hybrid Deep Learning, LSTM-GRU, Technical Indicators, Time-Series AnalysisAbstract
The stock market is a volatile and dynamic environment, making stock market forecasting a difficult task. The ability to predict stock prices accurately is crucial for investors and financial institutions in making effective investment decisions and managing risks. The paper presents the hybrid deep learning system for National Stock Exchange (NSE) market predictions based on the technical indicator and time-series analysis. These machine learning and deep learning models such as SVM, RF, LSTM, and transformer-based models are compared. Data from the NSE India and Yahoo Finance were used for experiments with the historical stock market data. Technical indicators like MA, RSI, MACD, BB and EMA were added to enhance the prediction performance. Data preprocessing was applied prior to the model training and included in the process: normalization and handling missing values, sequence generation. The experimental results showed that the proposed Hybrid LSTM-GRU model achieved the highest forecasting accuracy of 94.1%, which was higher than the traditional machine learning and standalone deep learning models, and had the lowest RMSE, MAE, MAPE values. The proposed system also resulted in a lower RMSE, MAE and MAPE, indicating greater stability and reliability in the prediction. The study also demonstrates the use of technical indicators with Hybrid LSTM-GRU architecture has significantly enhanced the performance of the stock market forecasting.The study further validates that the integration of technical indicators and the Hybrid LSTM-GRU architecture has clearly resulted in better performance when predicting the stock market.
References
Adesina, O. S., & Obokoh, L. O. (2025). A hybrid framework of deep learning and traditional time series models for exchange rate prediction. Scientific African, 29, e02818.
Agrawal, M., Shukla, P. K., Nair, R., Nayyar, A., & Masud, M. (2022). Stock prediction based on technical indicators using deep learning model. Computers, Materials & Continua, 70(1).
Banoth, S., & Yadala, S. (2024, December). A hybrid deep learning framework for stock price forecasting with sentimental analysis. In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 467-471). IEEE.
Bhanja, S., & Das, A. (2024). A Black Swan event-based hybrid model for Indian stock markets’ trends prediction. Innovations in Systems and Software Engineering, 20(2), 121-135.
Cheng, C. H., Tsai, M. C., & Chang, C. (2022). A time series model based on deep learning and integrated indicator selection method for forecasting stock prices and evaluating trading profits. Systems, 10(6), 243.
Fozap, F. M. P. (2025). Hybrid machine learning models for long-term stock market forecasting: Integrating technical indicators. Journal of Risk and Financial Management, 18(4), 201.
Kamara, A. F., Chen, E., & Pan, Z. (2022). An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices. Information Sciences, 594, 1-19.
Magloire, P. F. F. (2025). Hybrid Machine Learning Models for Long-Term Stock Market Forecasting: Integrating Technical Indicators.
Maheshwari, R., & Kapoor, V. (2025). A deep approach for forecasting the NSE opening index employing CNN-LSTM hybrid framework. Procedia Computer Science, 258, 170-182.
Moodi, F., Rafsanjani, A. J., Zarifzadeh, S., & Chahooki, M. A. Z. (2024). Fusion of technical indicators and sentiment analysis in a hybrid framework of deep learning models for stock price movement prediction. IEEE Access, 12, 195696-195709.
Sekar, R. (2025). Hybrid Strategies: Enhancing Intra-Day Trading in India via Machine Learning, Signal Processing, and Market Indicators (Doctoral dissertation, American Business Management and Technology College (Switzerland)).
Singh, A., & Mishra, A. (2025). Stock Market Prediction Using Ensemble and Deep Learning Approaches: An Analysis on NSE Data. Available at SSRN 5866922.
Manjunath, C., Marimuthu, B., & Ghosh, B. (2023). Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance. International Journal of Electrical and Computer Engineering (IJECE), 13(3), 3549-3560.
Shah, J., Vaidya, D., & Shah, M. (2022). A comprehensive review on multiple hybrid deep learning approaches for stock prediction. Intelligent Systems with Applications, 16, 200111.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
