Applications of deep learning in stock market prediction: Recent progress

• Published in: Expert systems with applications Vol. 184; p. 115537
• Main Author: Jiang, Weiwei
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.12.2021; Elsevier BV
• Subjects: Convolutional neural network; Deep learning; Feedforward neural network; Machine learning; Neural networks; Prediction models; Recurrent neural network; Securities markets; Stock market prediction; Deep learning; Recurrent neural network; Feedforward neural network; Convolutional neural network; Stock market prediction; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115537

•The latest applications of deep learning in stock market prediction are presented.•The literature is reviewed with a general workflow for stock market prediction.•The often-ignored implementation and reproducibility in other surveys are examined.•The future directions along with the research frontiers are pointed out. Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Based on the summary, we also highlight some future research directions in this topic.