An examination of herding behavior among tech stock investors in ASEAN-6 countries: A BiLSTM machine learning approach

The purpose of this study is to investigate whether herding behavior persists in conventional and sustainable stock indices in ASEAN-5 nations during times of economic uncertainty, such as the COVID-19 pandemic and international political unrest. The study uses augmented regression models and the Cross-Sectional Absolute Deviation (CSAD) approach to identify herding tendencies and asymmetries under different market conditions using daily closing price data from 2019 to 2023. The results show that herding behavior is evident in both conventional and sustainable indices, especially during times of high volatility. Herding, on the other hand, is more noticeable in sustainable indices, indicating that during crises, investors may act collectively motivated by risk aversion and ESG sentiments. It is also confirmed that asymmetric herding exists, with downward markets producing more powerful herding effects than upward ones. These findings demonstrate how behavioral dynamics play a crucial part in determining the performance of sustainable investments. They also imply that irrational investor behavior can still occur in sustainable markets. This necessitates a more thorough incorporation of behavioral finance principles into the planning and oversight of financial products that are associated with ESG. The study has two implications: first, it calls on financial institutions and policymakers to increase investor education and transparency in sustainable finance; second, it suggests that portfolio managers take behavioral risks into account when distributing assets among sustainability-focused investments, especially in lean economic times.