LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

ArXiv ID: 2308.01915 “View on arXiv”

Authors: Unknown

Abstract

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions.

Keywords: Limit Order Book (LOB), Stock Price Trend Prediction, Deep Learning Robustness, Model Generalization, Financial Market Prediction

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper involves advanced deep learning architectures and statistical evaluation (high math), and features a comprehensive benchmarking study with an open-source framework, multiple datasets, and profit analysis (high empirical rigor).
  flowchart TD
    A["Research Goal:<br>Assess DL Model Robustness &<br>Generalizability for LOB-based SPTP"] --> B["Methodology:<br>Develop LOBCAST Framework"]

    subgraph B ["Methodology: LOBCAST Framework"]
        B1["Data Preprocessing"] --> B2["DL Model Training"] --> B3["Evaluation &<br>Profit Analysis"]
    end

    C["Inputs:<br>Limit Order Book Data<br>Including Non-Stationary Regimes"] --> B

    B --> D{"Computational Process:<br>Train on Known vs.<br>Test on New Data Regimes"}

    D --> E["Key Findings:<br>1. Significant Performance Drop<br>on New Data<br>2. Limited Real-World<br>Market Applicability<br>3. Benchmark for Future<br>Innovative Solutions"]