An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book

ArXiv ID: 2505.22678 “View on arXiv”

Authors: Jiahao Yang, Ran Fang, Ming Zhang, Jun Zhou

Abstract

In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to capture price movement patterns effectively, particularly without well-designed features. We observed that raw LOB data exhibits inherent symmetry between the ask and bid sides, and the bid-ask differences demonstrate greater stability and lower complexity compared to the original data. Building on this insight, we propose a novel approach in which leverages the Siamese architecture to enhance the performance of existing deep learning models. The core idea involves processing the ask and bid sides separately using the same module with shared parameters. We applied our Siamese-based methods to several widely used strong baselines and validated their effectiveness using data from 14 military industry stocks in the Chinese A-share market. Furthermore, we integrated multi-head attention (MHA) mechanisms with the Long Short-Term Memory (LSTM) module to investigate its role in modeling stock price movements. Our experiments used raw data and widely used Order Flow Imbalance (OFI) features as input with some strong baseline models. The results show that our method improves the performance of strong baselines in over 75$% of cases, excluding the Multi-Layer Perception (MLP) baseline, which performed poorly and is not considered practical. Furthermore, we found that Multi-Head Attention can enhance model performance, particularly over shorter forecasting horizons.

Keywords: Limit Order Book (LOB), Siamese Architecture, Multi-Head Attention, LSTM, High-Frequency Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper applies established deep learning architectures (Siamese networks, LSTM, Multi-Head Attention) with minimal novel mathematical derivations, but rigorously tests on 14 real stocks with specific data (level-II LOB), benchmarks against strong baselines, and reports performance metrics over multiple horizons.

  flowchart TD
    A["Research Goal: Improve LOB Prediction for HFT"] --> B["Input Data: Chinese A-Share Market LOB Data"]
    B --> C["Methodology: Siamese Architecture to process Ask/Bid symmetry"]
    C --> D["Enhancement: Integrate Multi-Head Attention with LSTM"]
    D --> E{"Computational Processing"}
    E -->|Raw Data| F["Model Training & Evaluation"]
    E -->|OFI Features| F
    F --> G["Key Findings: >75% Baseline Improvement & MHA Benefit in Short Horizons"]