Introduction:

Herding behavior is a key phenomenon in behavioral finance that contradicts classical assumptions of market efficiency and investor rationality. In emerging markets with high uncertainty and information asymmetry, investors often imitate others rather than making independent decisions. This behavior can amplify price volatility, create speculative bubbles, and reduce market efficiency.

This study examines herding behavior in the Tehran Stock Exchange (TSE) across bullish and bearish trends, as well as trading booms and recessions, and investigates the role of investor sentiment in influencing collective decision-making. The research is innovative in combining behavioral and financial approaches while focusing on an emerging market with low informational transparency.



Methodology:

The sample includes firms listed on the TSE between 2007 and 2023, comprising approximately 350,000 daily observations from 129 companies.

Herding is measured using the Cross-Sectional Absolute Deviation (CSAD) model by Chang et al. (2000), where a negative coefficient of the market return squared term (RM²) indicates herding.

Investor sentiment (SENT) and its interaction with market volatility (SENT*RM²) are included to assess its effect on herding. Quantile regression is applied to test robustness across different return levels.



Findings:

Results confirm significant herding in the TSE. The negative RM² coefficient indicates convergence of individual returns toward the market average during volatile periods.

Herding occurs in both bullish and bearish markets, but is stronger in downturns, reflecting fear, uncertainty, and imitation of larger investors. In uptrends, greed and fear of missing out reinforce collective behavior.

Herding intensifies during high-volume trading (booms) and weakens in low-volume periods (recessions). Investor sentiment does not have a significant direct effect, suggesting that collective market forces dominate individual attitudes.



Conclusion:

This study provides evidence of persistent and multifaceted herding behavior in the TSE. Such behavior strengthens in extreme market conditions and during trading booms, but diminishes in recessions. Even after accounting for investor sentiment, collective patterns remain dominant, highlighting the influence of social and psychological factors over individual decision-making.

From a policy perspective, results underscore the importance of enhancing transparency, improving regulatory monitoring, and developing financial literacy programs to mitigate irrational collective behavior. Future research could use survey-based sentiment indices or social media analysis to capture the emotional dimensions of investor behavior more precisely