Stock Market Prediction Using Deep Learning Approach

Abstract

Stock market prediction is a challenging task that has attracted a lot of attention from both academic and industrial communities. In recent years, deep learning has emerged as a powerful tool for stock prediction due to its ability to handle large amounts of complex data. In this article, we review the state-of-the-art deep learning techniques for stock prediction and provide insights into their strengths and limitations. Specifically, we focus on the application of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in stock prediction, and discuss the challenges and opportunities for future research in this area.