An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique


  • Jitendra Sisodia Department of Computer Science, Technocrats Institute of Technology, (Excellence), Bhopal, M.P. India
  • amar nayak Rajiv Gandhi Proudyogiki Vishwavidyalaya
  • Rajesh Boghey Department of Computer Science, Technocrats Institute of Technology, (Excellence), Bhopal, M.P. India


Share/ Index, CNN, DNN, Prediction, Ensemble model


As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.


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A. Gupta, Akansha, K. Joshi, M. Patel and V. Pratap (2023) “Stock Market Prediction using Machine Learning Techniques: A Systematic Review.” International Conference on Power, Instrumentation, Control and Computing (PICC: pp. 1-6.

K. V. Kumar and R. Anitha (2022) “A Detailed Survey to Forecast the Stock Prices by Applying Machine Learning Predictive Models and Artificial Intelligence Techniques.” International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS): pp. 1-6.

Zahra Fathali, Zahra Kodia&Lamjed Ben Said (2022) “Stock Market Prediction of Nifty 50 Index Applying Machine Learning Techniques.”, Applied Artificial Intelligence, Taylor & Francis 36(1): 3100-3123.

Baek, S., Mohanty, S.K., & Glambosky, M. (2020) “COVID-19 and Stock Market Volatility: An Industry Level Analysis.” Finance Research Letters: pp. 37-46.

Beckmann M. (2017) “Stock Price Change Prediction Using News Text Mining.” Federal University of Rio de Janeiro: pp. 156-162

Choudrie, J., Banerjee, S., Kotecha, K., Walambe, R., Karende, H., &Ameta, J. (2021) “Machine Learning Techniques and Older Adults Processing of online Information and Misinformation: A COVID-19 Study.” Computers in Human Behavior: pp. 119-121.

F. Ronaghi, M. Salimibeni, F. Naderkhani, and A. Mohammadi (2020) ‘‘ND-SMPF: A noisy deep neural network fusion framework for stock price movement prediction.’’ IEEE Int. Conf. Inf. Fusion (FUSION): pp. 1–7.

Pushpendra Singh Sisodia, Ashish Gupta, Yogesh Kumar, Gaurav Kumar Ameta (2022) “Stock Market Analysis and Prediction for Nifty50 using LSTM Deep Learning Approach.” International Conference on Innovative Practices in Technology and Management (ICIPTM): pp. 324-335.

O. O. Aalen (1989) “A linear regression model for the analysis of life times.” Statist. Med 8(8) : pp. 907-925.

T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang, and H. Cho (2021) ‘‘Xgboost: Extreme gradient boosting.’’ R Package 1(4): pp. 389-401

Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan (2015) “Deep Learning for Event-Driven Stock Prediction.”, IJCAI : pp. 2327-2333.

Sreelekshmy Selvin, R Vinaykumar, E.A. Gopalkrishnan, Vinay Krishna Menon, K.P. Soman (2017) “Stock Price Prediction using LSTM, RNN and CNN-sliding window model.”, IEEE : pp. 718-720.

Mehar Vijh, (2020) “Stock closing price prediction using machine learning techniques.” International Conference on Computational Intelligence and Data Science: pp. 599– 606

Sheng Chen (2018) “Stock Prediction Using Convolutional Neural Network.” IOP Conf. Series: Materials Science and Engineering: pp. 435-445.

. Jiaqi Pan, Yan Zhuang, Simon Fong (2016) “The Impact of Data Normalization on Stock Market Prediction: Using SVM and Technical Indicators.” ResearchGate: pp. 216-225.

K.K Suresh Kumar (2012) “Performance analysis of stock market prediction using Artificial Neural Network.” Global Journal of Computer science and Technology 12(1): pp. 735-742.

Abadi, M.v(2016) “Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems” IEEE: pp. 76-82.



How to Cite

Sisodia, J. ., nayak, amar, & Boghey, R. . (2024). An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique. Journal of Applied Artificial Intelligence, 5(1), 41–53.