The impact of COVID-19 on investor herding in Indonesia: Evidence from LQ-45 index before, during, and after the pandemic

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Type of the article: Research Article

Abstract
Herding behavior, in which investors follow overall market trends rather than conducting independent analysis, has significant implications for market efficiency, volatility, and liquidity conditions, particularly in emerging markets like Indonesia. This study aims to investigate the presence and dynamics of herding behavior in Indonesia’s LQ-45 index during three distinct periods: pre-pandemic (2019), pandemic (2020–2021), and post-pandemic (2023). The sample comprises 22 firms consistently listed on the LQ-45 index, with daily data collected from 2019 to 2023. A time-series regression based on the Cross-Sectional Absolute Deviation (CSAD) model measured herding intensity, while a Granger causality test assessed the relationship between herding behavior and market liquidity. The results indicate that herding behavior intensified significantly during the pandemic, evidenced by a strong negative γ₂ coefficient (–0.0124, p = 0.0026) and an adjusted R² of 0.1902, the highest across all periods. In contrast, the pre-pandemic period showed relatively weak herding behavior under more stable market conditions, while the post-pandemic phase demonstrated a return to more independent decision-making. The Granger causality test confirmed a bidirectional relationship between market liquidity and herding during the crisis, while such causality was absent after the pandemic. In the pre-pandemic period, herding influenced liquidity (p = 0.014), while no significant causal relationships were found afterward. Overall, herding behavior increased during the pandemic but returned to more independent decision-making in the post-pandemic phase.

Acknowledgments
The authors thank to Universitas Syiah Kuala for supporting this research. We also thank the reviewer for the thorough review of this manuscript and for the guidance on this research article. We sincerely appreciate the time and effort you have dedicated to providing valuable feedback.

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    • Figure 1. Framework analysis
    • Table 1. Descriptive statistics
    • Table 2. Regression result of herding
    • Table 3. Regression of market liquidity (Pre-pandemic and Post-pandemic)
    • Table 4. Regression of market liquidity (Pandemic and Full sample, part I)
    • Table 5. Regression of market liquidity (Crisis and Full sample, part II)
    • Table 6. Granger causality (p-value)
    • Data curation
      Ashraf Afif
    • Formal Analysis
      Ashraf Afif, Said Musnadi, Suci Ismadyaliana
    • Investigation
      Ashraf Afif
    • Methodology
      Ashraf Afif, Said Musnadi
    • Software
      Ashraf Afif, Suci Ismadyaliana
    • Supervision
      Ashraf Afif, Suci Ismadyaliana
    • Validation
      Ashraf Afif, Said Musnadi
    • Visualization
      Ashraf Afif, Said Musnadi
    • Writing – original draft
      Ashraf Afif, Said Musnadi, Suci Ismadyaliana
    • Writing – review & editing
      Ashraf Afif, Said Musnadi, Suci Ismadyaliana
    • Conceptualization
      Said Musnadi, Suci Ismadyaliana
    • Project administration
      Said Musnadi