A Review of Enhanced Index Price Movement Prediction Using Ensemble Deep Learning Models

Authors

  • Anshul Singh Bhadoriya Sagar Institute of Research and Technology, Bhopal, India Author
  • Arun Japatey Sagar Institute of Research and Technology, Bhopal, India Author

Keywords:

Index price prediction, Ensemble deep learning, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Financial markets

Abstract

This review paper investigates and evaluates advancements in index price movement prediction through ensemble deep learning models. Focusing on the fusion of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), the study comprehensively analyses their collective impact on enhancing the precision and accuracy of index price forecasts in financial markets. The paper reviews and compares various ensemble strategies employed in deep learning, assessing their effectiveness in capturing complex patterns and dependencies within financial data. Special attention is given to the synergistic integration of CNN and DNN, highlighting how their combined capabilities contribute to improved predictive performance. Through an extensive literature review, the study examines the methodologies, architectures, and training strategies associated with ensemble deep learning models in the context of index price prediction. Additionally, the paper explores ensemble techniques’ implications for mitigating overfitting and enhancing model robustness in dynamic financial environments.

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Published

2024-04-05

How to Cite

A Review of Enhanced Index Price Movement Prediction Using Ensemble Deep Learning Models. (2024). International Journal of Innovative Research in Technology and Science, 12(2), 421-426. https://ijirts.org/index.php/ijirts/article/view/65

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