Stock Market Prediction via Deep Learning Techniques: A Survey

TL;DR

This survey comprehensively reviews deep learning methods for stock market prediction, highlighting recent models, datasets, evaluation metrics, and future research directions in this rapidly evolving field.

Contribution

It introduces a new taxonomy for deep neural network models in stock prediction and provides detailed analysis of datasets and evaluation metrics used.

Findings

Deep learning models outperform traditional methods in stock prediction.

A new taxonomy categorizes state-of-the-art deep learning models.

Identifies key datasets and evaluation metrics in the field.

Abstract

Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we point out several future directions by sharing some new perspectives on stock market prediction.