2024

Journal

Journal of Telecommunications and the Digital Economy, 12(1), 612-636.

Stock markets have an important impact on economic growth of countries. The prediction
of stock market indexes has been a complex task for last years. Indeed, many researches and financial analysts are highly interested in the research area of stock market prediction. In this paper, we propose a novel framework, titled AutoCNN based on artificial intelligence techniques, to predict future stock market indexes. AutoCNN is composed mainly of three stages: (1) CNN for Automatic Feature Extraction, (2) The Halving Grid Search algorithm is combined to CNN model for stock indexes prediction and (3) Evaluation and recommendation. To validate our AutoCNN, we conduct experiments on two financial datasets that are extracted in the period between 2018 and 2023, which includes several events such as economic, health and geopolitical international crises. The performance of AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves strong prediction abilities. AutoCNN contributes to emerging technologies and innovation in the financial sector by automating decision-making, leveraging advanced pattern recognition, and enhancing the overall decision support system for investors in the digital economy.

@article{zouaghia2024novel,
  title={A novel AutoCNN model for stock market index prediction},
  author={Zouaghia, Zakia and Kodia, Zahra and Ben Said, Lamjed},
  journal={Journal of Telecommunications and the Digital Economy},
  volume={12},
  number={1},
  pages={612--636},
  year={2024},
  publisher={Telecommunications Association [South Melbourne, Vic.]}
}