Stock Market Prediction via Deep Learning Techniques: A Survey

Paper: arXiv 2212.12717

Authors: Jinan Zou, Qingying Zhao, Yang Jiao, Haiyao Cao, Yanxi Liu, Qingsen Yan, Ehsan Abbasnejad, Lingqiao Liu, Javen Qinfeng Shi

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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: As a survey paper, this work excels in completeness and clarity by offering a structured taxonomy and detailed statistics of existing deep learning models in stock market prediction. However, it lacks original empirical backtesting, mathematical proofs, or new methodology implementation, leading to lower math complexity and empirical rigor scores.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Survey Deep Learning for Stock Market Prediction"] --> B["Identify 4 Core Subtasks &<br>Collect Benchmark Datasets/Metrics"]
    B --> C["Data Inputs:<br>Prices, Text, Sentiment & Macro Metrics"]
    C --> D["Propose Novel Taxonomy for<br>Deep Neural Network Models"]
    D --> E["Analyze & Categorize<br>State-of-the-Art Architectures"]
    E --> F["Outcomes:<br>Comprehensive Survey & Future Directions"]