Abstract
Stock markets have a significant impact on the economic growth of countries. Predicting stock market indices has been a complex task in recent years. Indeed, many researchers and financial analysts are keenly 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 indices. AutoCNN is composed mainly of three stages: (1) A Convolutional Neural Network (CNN) for Automatic Feature Extraction; (2) The Halving Grid Search algorithm combined with a second CNN model for prediction of stock indices; 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 the AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves to have 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Telecommunications and the Digital Economy
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
