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Spillover dynamics and determinants between FinTech institutions and commercial banks based on the complex network and random forest fusion

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  • Sun, Jiaojiao
  • Zhang, Chen
  • Zhang, Rongrong
  • Ji, Yuanpu
  • Ding, Jiajun

Abstract

FinTech is transforming the financial system by enhancing efficiency for commercial banks while introducing new risks. This paper examines the direct and indirect risk spillovers between FinTechs and commercial banks, focusing on the determinants of these spillovers from a micro perspective. We create a high-dimensional risk spillover network to analyze the characteristics of spillovers and the roles of different institutions. Considering institutional operational characteristics and investor attention, we identify eight key indicators influencing risk spillovers. We construct a multivariate correlation network through the random forest fusion method, assessing the impact of various factors during the full sample period and crises. Our findings indicate: (1) Risk spillovers exhibit localized centrality, with commercial banks serving as primary receivers and contributors to systemic risk, while FinTechs amplify the risk. (2) Over the full sample period, institution size and debt risk are critical determinants of spillovers. Investor attention is vital for commercial banks' risk absorption, whereas future development capacity significantly affects FinTechs' risk dynamics. (3) During COVID-19, the significance of debt risk diminishes, with operational performance taking precedence. During the Russian-Ukrainian conflict, long-term solvency emerges as the key determinant. Notably, during both crises, the influence of investor attention on spillovers of banks weakens while it increases for FinTechs. This study provides evidence to assist regulatory agencies in refining policies for effective financial innovation risk management.

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  • Sun, Jiaojiao & Zhang, Chen & Zhang, Rongrong & Ji, Yuanpu & Ding, Jiajun, 2025. "Spillover dynamics and determinants between FinTech institutions and commercial banks based on the complex network and random forest fusion," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:pacfin:v:91:y:2025:i:c:s0927538x25000502
    DOI: 10.1016/j.pacfin.2025.102713
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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