Society & Economicsarticle2026-10-16

Investor herding under extreme uncertainty: evidence from Asia-Pacific equity markets during COVID-19

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Abstract

Purpose This study examines whether COVID-19-related extreme uncertainty coincided with time-varying herding in six Asia-Pacific equity markets and the United States, whether the pattern was pandemic-specific, and whether herding intensity was associated with US monetary conditions and foreign-market turbulence. Design/methodology/approach Using index- and constituent-level data from 2018 to 2024, the study combines cross-sectional absolute deviation regressions, 30-day rolling estimates, a 2022 stress-episode comparison, weekly monetary-condition regressions and cross-market models with HAC-robust Wald tests and adaptive LASSO. Findings The pandemic period is associated with stronger but episodic herding in China, Hong Kong, Japan and Malaysia, a pattern not reproduced uniformly in 2022. US monetary conditions have limited explanatory power outside Malaysia. Foreign-market turbulence is more broadly associated with domestic dispersion, but most significant associations widen dispersion; herding-consistent channels are selective, directional and horizon-dependent. Research limitations/implications The analysis identifies conditional associations rather than causal effects. The seven-market sample also limits formal identification of institutional sources of heterogeneity. Practical implications The findings caution regulators and investors against interpreting all cross-market transmission as herding. Market structure, trading restrictions and transmission horizons should be considered when evaluating behavioural synchronization during severe uncertainty. Originality/value The study integrates the formation, crisis specificity and cross-border correlates of herding. It distinguishes herding-consistent associations from directional return spillovers and compares market-information and external monetary-policy channels.

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View paper (DOI)OpenAlexReview of Behavioral FinancePublished 2026-10-16

Authors: Yining Bian, Chenguang Zhang