Anomalous Cross-Border Capital Flow Patterns and Their Implications for National Economic Security: An Empirical Analysis

Abstract

This study analyzes anomalous cross-border capital flow patterns and their implications for national economic security through an empirical investigation spanning 42 economies from 2000 to 2021. Applying a multi-model detection framework combining statistical filtering techniques, machine learning algorithms, and network analysis, we identify 284 distinct anomalous episodes classified into six categories: surges, sudden stops, flight, retrenchment, structural shifts, and round-tripping. The research employs structural vector autoregression modeling to quantify economic security impacts, revealing pronounced heterogeneity in vulnerability profiles across economy types, with emerging markets experiencing peak external position impacts reaching 4.25 standard deviations and cumulative GDP costs of 8.37% following sudden stop episodes. Our analysis demonstrates that global risk factors significantly influence destabilizing outflows, while institutional quality exhibits protective effects against capital flight. The transmission mechanism analysis identifies financial market channels as dominant conduits in advanced economies, with banking sectors serving as critical vectors to strategic industries. These findings contribute to the theoretical understanding of capital flow security dynamics and provide evidence-based recommendations for regulatory frameworks incorporating big data analytics, blockchain-based tracking systems, and calibrated macroprudential measures to enhance national economic resilience while preserving beneficial aspects of financial openness.