Behavioral Biases and Market Fluctuations: An Empirical Study of Herding and Volatility in Vietnam
DOI:
https://doi.org/10.47654/v30y2026i3p27-62Keywords:
Herding behavior, Trading volume, Market volatility, GARCH(1,1), Behavioral finance, Vietnam stock market, COVID-19Abstract
Purpose: This research assesses the intensity and presence of the herding behavior in the Vietnamese stock market and its influence on the trading activity and price volatility. Given the dominance of retail investors and limited market transparency, the study examines how collective investor sentiment impacts volatility before, during, and after the COVID-19 pandemic.
Design/methodology/approach: Using intraday transaction data from the Ho Chi Minh Stock Exchange (HOSE) between March 2019 and January 2025, the herding intensity is assessed through Patterson and Sharma's (2006) run-based statistic. The GARCH(1,1) approach is utilized to explore how herding and trading volume influence market volatility, with the dataset divided into three sub-periods: pre-COVID, during COVID, and post-COVID. Stationarity and approach robustness are confirmed by utilizing the Augmented Dickey-Fuller (ADF) test.
Findings: The results confirm statistically strong herding behavior, particularly during the COVID‑19 period, as both buyer (Hu) and seller (Hd) herding intensities frequently fall below the −1.96 threshold (p < 0.01), indicating significant directional clustering. Trading volume and herding intensity both significantly increase conditional market volatility (p < 0.01), with volatility remaining highest during the pandemic and moderating but still elevated in the post‑COVID period. These patterns show that intraday herding measures can serve as early‑warning indicators of volatility spikes in a retail‑dominated emerging market.
Originality/Value: This research is the first to integrate Patterson–Sharma intraday herding metrics with a GARCH(1,1) volatility model to study the Vietnamese stock market across pre‑crisis, crisis, and post‑crisis regimes. It provides rare intraday evidence from a developing market dominated by retail investors and quantifies how directional and zero‑return herding affect volatility dynamics. By treating Hu, Hd, and Hz as behavioral risk indicators, the study contributes to Decision Sciences by offering empirically grounded analytics that support decision‑making under ambiguity for market makers, regulators, and investors.
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