Stock market prediction is a challenging task, as stock prices are influenced by a variety of factors. However, deep learning has emerged as a promising tool for stock market prediction. Deep learning models can learn complex patterns from large amounts of data, which can be used to predict future stock prices. In this paper, we review the application of deep learning in stock market prediction. We discuss the different deep learning models that have been used for stock market prediction, and we evaluate their performance. We also discuss the challenges of using deep learning for stock market prediction, and we propose some directions for future research. Stock market prediction is a multidisciplinary problem that concerns economists, statisticians, and computer scientists. The proliferation of machine learning methodologies, especially deep learning techniques, has led to the adoption of these techniques in time-series forecasting, such as stock prices. A systematic literature review (SLR) of 12 papers was conducted to identify primary studies that deal with the prediction of stock markets in the European Union (EU) using deep learning techniques. The SLR indicates that there is not yet intense activity in this field, which thus appears open for further research.
