Market Reactions and Information Spillovers in Bank Mergers: A Multi-Method Analysis of the Japanese Banking Sector

ArXiv ID: 2512.06550 “View on arXiv”

Authors: Haibo Wang, Takeshi Tsuyuguchi

Abstract

Major bank mergers and acquisitions (M&A) transform the financial market structure, but their valuation and spillover effects remain open to question. This study examines the market reaction to two M&A events: the 2005 creation of Mitsubishi UFJ Financial Group following the Financial Big Bang in Japan, and the 2018 merger involving Resona Holdings after the global financial crisis. The multi-method analysis in this research combines several distinct methods to explore these M&A events. An event study using the market model, the capital asset pricing model (CAPM), and the Fama-French three-factor model is implemented to estimate cumulative abnormal returns (CAR) for valuation purposes. Vector autoregression (VAR) models are used to test for Granger causality and map dynamic effects using impulse response functions (IRFs) to investigate spillovers. Propensity score matching (PSM) helps provide a causal estimate of the average treatment effect on the treated (ATT). The analysis detected a significant positive market reaction to the mergers. The findings also suggest the presence of prolonged positive spillovers to other banks, which may indicate a synergistic effect among Japanese banks. Combining these methods provides a unique perspective on M&A events in the Japanese banking sector, offering valuable insights for investors, managers, and regulators concerned with market efficiency and systemic stability

Keywords: Event Study, Fama-French Three-Factor Model, Vector Autoregression (VAR), Propensity Score Matching (PSM), Mergers and Acquisitions (M&A), Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper uses established empirical methods (event study, VAR, PSM) with real financial data and multiple robustness checks, but relies on standard econometric models without advanced mathematical derivations.

  flowchart TD
    A["Research Goal:<br>Assess M&A Market Reactions<br>& Information Spillovers"] --> B["Input Data<br>2 M&A Events in Japanese Banking:<br>2005 (Mitsubishi UFJ) & 2018 (Resona)"]

    B --> C{"Multi-Method Analysis"}

    C --> D["Method 1: Event Study<br>Market/CAPM/Fama-French Models"]
    C --> E["Method 2: VAR Analysis<br>Granger Causality & IRFs"]
    C --> F["Method 3: Propensity Score Matching<br>Causal ATT Estimation"]

    D --> G["Key Findings & Outcomes"]
    E --> G
    F --> G

    G --> H["Positive Market Reaction<br>(Significant CAR)"]
    G --> I["Prolonged Positive Spillovers<br>Synergistic Effects"]
    G --> J["Managerial/Regulatory Insights<br>Market Efficiency & Stability"]