From Data Acquisition to Lag Modeling: Quantitative Exploration of A-Share Market with Low-Coupling System Design

TL;DR

This paper introduces a two-stage framework for detecting lead-lag relationships in the Chinese A-share market, combining long-term coupling analysis with high-frequency pattern detection to enhance understanding of market microstructure.

Contribution

It presents a novel low-coupling, modular system that integrates multiple metrics and models for scalable, reproducible lead-lag relationship detection in stock markets.

Findings

Strong coupling correlates with lead-lag effects at fine time scales

High-frequency data reveals significant lead-lag patterns

System supports scalable and reproducible analysis

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.