An Empirical Analysis of Herding Behavior in the Stock Market: Evidence from Various Economic and Social Conditions in Iran - Majid Aghaei (Journal of Asset Management and Financing)
The stock exchange serves as a critical source of corporate financing and as a platform for individuals to invest their savings, attracting a substantial amount of domestic capital in recent years and playing a pivotal role in the country's economic growth and development. This study investigates herding behavior in the Tehran Stock Exchange (TSE) under various economic and social conditions, including periods before and after exchange rate fluctuations, prior to and following the COVID-19 pandemic, and during both bullish and bearish market phases from April 2015 to March 2023. The analysis is based on the overall stock price index and the price index of the top 50 companies, employing Ordinary Least Squares (OLS) regression and quantile regression methodologies. The findings indicate that herding behavior is significantly evident in the TSE throughout the entire study period and across most quantiles (from the 0.05 to the 0.75 quantile) for both indices. Specifically, herding behavior is prominently observed prior to significant exchange rate fluctuations; however, it becomes unconfirmed afterward due to the insignificance of the resulting coefficients. Furthermore, herding behavior was found to be present in the TSE before the COVID-19 outbreak, with a notable decline after the pandemic, and in some cases, a reversal was observed. The analyses also demonstrate that herding behavior persists in both bullish and bearish market conditions, especially within the lower market quantiles.Keywords: Herding Behavior, Quantile Regression, COVID-19 Pandemic, Exchange Rate Fluctuations, Bullish and Bearish MarketsJEL Classification: F65, C32, E44 IntroductionThe capital market serves as one of the fundamental pillars of the economy, playing a vital role in fostering economic growth and development. In recent years, Iran’s capital market has attracted significant attention from both traders and policymakers, owing to its financial appeal and investment opportunities. However, irrational and emotional behaviors among investors within this market have presented substantial challenges. A major issue is herding behavior, which refers to the innate human tendency to mimic others. In financial markets, such behavior can drive investors to make irrational decisions and engage in high-risk transactions, as participants often base their choices not on the intrinsic value of stocks, but rather on the perceived actions and expectations of others regarding future price movements (Bikhchandani & Sharma, 2000). Given the critical role of the capital market and its investors as key players in the economy, it is essential to examine their behavior to promote optimal decision-making and ensure proper market functioning. This study utilizes behavioral finance theories and analyzes data related to TSE to investigate investor behavior through the lens of herding behavior. Specifically, the research explores herding behavior under various macroeconomic conditions, including currency fluctuations, both bearish and bullish market environments, and the COVID-19 pandemic as a unique social context. Materials & Methods To evaluate herding behavior, Christie and Huang (1995) and Chang et al. (2000) employed modeling approaches based on the cross-sectional dispersion of stock returns. The methodologies in both studies are grounded in the principle that, when herding behavior is present, individual stock returns tend to converge toward the overall market return. As a result, herding behavior leads to minimal differences between individual stock returns and the market return index. These minor discrepancies are quantified using the cross-sectional standard deviation (CSSD) and the cross-sectional absolute deviation (CSAD). Given the limitations of the CSSD model—such as the necessity of estimating excess returns and its inability to account for potential herding behavior during stable periods—this study adopts the CSAD model to address these shortcomings. The CSAD model enhances the CSSD framework by incorporating cross-sectional absolute deviations, providing a more robust analysis of herding behavior.Additionally, in light of the nature of the research problem, this study employs quantile regression (QR), introduced by Koenker and Bassett (1978), to conduct a more nuanced analysis of herding behavior. Quantile regression provides a more precise methodology by capturing variations across the entire distribution of the dependent variable and addressing the limitations of ordinary least squares (OLS) estimators (Barnes & Hughes, 2002; Zhou & Anderson, 2013). This approach models the response of the dependent variable to the independent variable at various quantiles, denoted as "τ," making it an effective tool for analyzing non-normal distributions. Moreover, quantile regression is particularly adept at handling outliers, extreme values, and non-normal deviations (Xiao, 2012; Allen et al., 2013; Alexander, 2008). FindingsThe results of the OLS along w indicate the presence of herding behavior in the Tehran Stock Exchange (TSE) throughout the study period. This finding reflects a tendency among investors to engage in collective behavior in the TSE across various time intervals. Further confirmation of herding behavior is provided by the quantile regression (QR) results, which reveal its presence in the lower and middle quantiles of the stock market during the study period, particularly under normal market conditions. However, in the higher quantiles, herding behavior diminishes and, in some cases, even reverses. This suggests that investors in the upper quantiles are more inclined to make independent decisions and are less likely to follow the crowd.During periods of exchange rate fluctuations, herding behavior was notably observed prior to sharp increases in exchange rates. These findings imply that in the lead-up to significant economic volatility, investors, driven by uncertainty regarding future conditions, are more likely to engage in herding behavior. However, after experiencing intense currency fluctuations, while some indications of herding behavior persisted, they were not statistically significant. This outcome may suggest that following substantial volatility, investors tend to adopt more individualized and potentially more conservative strategies. The QR analysis also indicated that herding behavior was more pronounced in the lower quantiles of the market, which may reflect the influence of exchange rate fluctuations on less risk-tolerant investors.During the COVID-19 pandemic, the study results revealed the presence of herding behavior in both the overall index and the top 50 companies’ index prior to the pandemic. This finding is noteworthy, as it indicates that herding behavior was evident in the TSE even before the onset of a global crisis. However, following the pandemic, herding behavior significantly declined and, in some instances, reversed. These changes underscore the impact of crisis conditions and increased volatility on investor behavior, suggesting that, in critical situations, investors tend to rely more on independent decision-making and individual assessments. The results from both the OLS and QR analyses further indicated the presence of herding behavior in TSE during both bullish and bearish market conditions, particularly in the lower quantiles of the market. In bullish markets, investors typically gravitate toward purchasing stocks with positive returns, while in bearish markets, they are inclined to sell stocks with negative returns. These behaviors, especially prevalent in the lower quantiles, clearly illustrate the sensitivity of investors to market conditions and their propensity to follow the crowd. Conclusion and DiscussionThis study analyzed herding behavior in the Tehran Stock Exchange (TSE) from 2015 to 2022 using Ordinary Least Squares (OLS) and quantile regression methods, examining both the overall index and the index of the top 50 companies. The results revealed the presence of herding behavior throughout the study period and under various economic and social conditions, including exchange rate fluctuations, the COVID-19 pandemic, and both bullish and bearish markets. However, the intensity of herding behavior varied depending on the circumstances, with a stronger presence observed in the lower quantiles of the market. Following periods of severe exchange rate volatility and the pandemic, a noticeable shift toward independent behavior and individual decision-making emerged. Furthermore, herding behavior was identified as a contributing factor to market volatility. To enhance the efficiency of the stock market and mitigate the negative effects of herding behavior, it is recommended to improve information transparency and provide investors with access to independent analyses. Effective solutions include offering education in technical and fundamental analysis, strengthening oversight and regulations, diversifying financial instruments such as exchange-traded funds (ETFs), and adopting stable monetary and fiscal policies. Additionally, developing IT infrastructure, introducing tax incentives for long-term investments, and fostering collaboration among related institutions can further contribute to the sustainability and stability of the market.