From Data Acquisition to Lag Modeling: Quantitative Exploration of A-Share Market with Low-Coupling System Design
ArXiv ID: 2506.19255 “View on arXiv”
Authors: Jianyong Fang, Sitong Wu, Junfan Tong
Abstract
We propose a novel two-stage framework to detect lead-lag relationships in the Chinese A-share market. First, long-term coupling between stocks is measured via daily data using correlation, dynamic time warping, and rank-based metrics. Then, high-frequency data (1-, 5-, and 15-minute) is used to detect statistically significant lead-lag patterns via cross-correlation, Granger causality, and regression models. Our low-coupling modular system supports scalable data processing and improves reproducibility. Results show that strongly coupled stock pairs often exhibit lead-lag effects, especially at finer time scales. These findings provide insights into market microstructure and quantitative trading opportunities.
Keywords: lead-lag relationships, Granger causality, market microstructure, high-frequency trading, Chinese A-share market
Complexity vs Empirical Score
- Math Complexity: 4.5/10
- Empirical Rigor: 6.0/10
- Quadrant: Street Traders
- Why: The paper introduces a practical two-stage screening method with a low-coupling system design, featuring empirical testing on real A-share data across multiple time scales, but relies on standard statistical metrics (correlation, Granger causality) rather than advanced mathematics.
flowchart TD
A["Research Goal: Detect Lead-Lag<br>Relationships in Chinese A-Share Market"] --> B["Stage 1: Long-Term Coupling<br>(Daily Data)"]
A --> C["Stage 2: High-Frequency<br>Lead-Lag Detection<br>(1/5/15-min Data)"]
B --> D{"Coupling Analysis:<br>Corr, DTW, Rank Metrics"}
C --> E{"Statistical Analysis:<br>CCF, Granger Causality, Reg"}
D --> F["Identify Strongly<br>Coupled Stock Pairs"]
E --> G["Detect Significant<br>Lead-Lag Patterns"]
F --> G
G --> H["Key Outcomes:<br>Strong Coupling -> Lead-Lag Effects<br>Finer Scales = Stronger Signals<br>Low-Coupling System Design"]