Corporate Fundamentals and Stock Price Co-Movement

ArXiv ID: 2411.03922 “View on arXiv”

Authors: Unknown

Abstract

We introduce an innovative framework that leverages advanced big data techniques to analyze dynamic co-movement between stocks and their underlying fundamentals using high-frequency stock market data. Our method identifies leading co-movement stocks through four distinct regression models: Forecast Error Variance Decomposition, transaction volume-normalized FEVD, Granger causality test frequency, and Granger causality test days. Validated using Chinese banking sector stocks, our framework uncovers complex relationships between stock price co-movements and fundamental characteristics, demonstrating its robustness and wide applicability across various sectors and markets. This approach not only enhances our understanding of market dynamics but also provides actionable insights for investors and policymakers, helping to mitigate broader market volatilities and improve financial stability. Our model indicates that banks’ influence on their peers is significantly affected by their wealth management business, interbank activities, equity multiplier, non-performing loans, regulatory requirements, and reserve requirement ratios. This aids in mitigating the impact of broader market volatilities and provides deep insights into the unique influence of banks within the financial ecosystem.

Keywords: High-frequency Data, Granger Causality, Variance Decomposition, Co-movement Analysis, Big Data Techniques, Equity (Banking Sector)

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced econometric techniques (FEVD, Granger causality tests) with substantial mathematical underpinnings, while its validation on real Chinese banking data with high-frequency timestamps and multiple regression models demonstrates strong empirical rigor.

  flowchart TD
    A["Research Goal: Analyze dynamic stock co-movement with fundamentals using high-frequency data"] --> B["Data Collection"]
    B --> C["Key Inputs: Chinese Banking Sector Stocks<br/>High-frequency Market Data & Fundamental Metrics"]
    C --> D["Computational Methods (4 Models)"]
    D --> E1["Forecast Error Variance Decomposition"]
    D --> E2["FEVD normalized by Volume"]
    D --> E3["Granger Causality Frequency"]
    D --> E4["Granger Causality Days"]
    E1 & E2 & E3 & E4 --> F["Identify Leading Co-movement Stocks"]
    F --> G["Key Findings & Outcomes"]
    G --> G1["Wealth management & interbank activities impact peer influence"]
    G --> G2["Fundamental drivers: Equity multiplier, NPLs, Regulatory ratios"]
    G --> G3["Framework robust across sectors for financial stability insights"]