Novel AutoCNN Model for Stock Market Index Prediction

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.