BEHAVIOURAL BIAS IN INDIAN EQUITIES: A 20-YEAR STUDY OF HERDING PATTERNS ACROSS MARKET CONDITIONS

Abstract

his research investigates the incidence and dynamics of herding behavior — one of the main behavioral finance biases — in the Indian equity market for a complete 20- year period from 2005 to 2025. Herding bias is an inclination of investors to imitate the action of a large crowd of others rather than their personal judgment, particularly during uncertainty or big market moves. The research tests whether group behavior of such a kind can be empirically detected in the Indian stock market, how it varies across varying market conditions, and whether it varies across economic or external shocks- driven time periods. The research employs secondary market data, and the focus is on daily stock prices of 39 frequently traded stocks of the Nifty 50 index and the Nifty 50 index. The sample for 20 years creates a strong foundation to examine hypotheses about behavior and detect structural changes in investor behavior over time. To capture volatility in sentiment in the market and other macroeconomic events outside, the dataset has been split into five various time periods: 1. Pre-Crisis Era (2005–2007): A period of economic growth and upbeat investor sentiment before the global financial crisis. 2. Global Financial Crisis (2008–2009): A time of crisis with heightened volatility, uncertainty, and global market downturns. 3. Post-Crisis Recovery (2010–2019): Ten years of economic stabilization and market normalization. 4. COVID-19 Era (2020–June 2021): Defined by swift market dislocations, high investor anxiety, and quick recoveries attributed to the pandemic. 5. Post-COVID Era (July 2021–2025): A period of resurgence through economic reconstruction, change of investor sentiment, and emerging trends in equity participation. vi In order to measure herding behavior, the research utilizes the Cross-Sectional Absolute Deviation (CSAD) approach used by Chang, Cheng, and Khorana (2000). CSAD captures the average absolute deviation of individual stock returns from the market return for one day. Under conventional asset pricing theory, this deviation is assumed to rise linearly with the absolute market return because individual stock returns would stray farther in bad times in turbulent market conditions. However, when herding exists, investors will lean towards imitating the prevailing market consensus in trading behavior, and hence there will be a non-linear (concave) association between CSAD and market return. This implies that dispersion really falls when market activity is high — contrary to the assumption held by rational asset pricing — hence empirical evidence in favor of herding behavior. The research fortifies its investigation by splitting each of the five time periods into bull and bear phases, so it is feasible to investigate the extent to which herding behavior is heightened in either a rising or declining market. Moreover, herding is investigated under conditions of extreme market states, i.e., on days of extreme positive returns and on days of abnormally large negative returns, to ascertain whether investor behavior is more collective when there is extreme stress or euphoria. This research adds to the expanding literature of behavioral finance by employing a qualitative, in-depth quantitative method to measure non-rational investor behavior in a prominent emerging market. It offers useful insights for regulators, institutional investors, and fund managers to allow them to appreciate the subtleties of collective behavior and to identify possible inefficiencies in the Indian equity market. Moreover, by spanning two decades and addressing salient global and domestic events, the study provides a longitudinal analysis of how investor psychology changes and evolves as a function of changing market conditions. While the findings of this study will be presented in the subsequent sections, this paper provides a structured, evidence-based approach to the study of behavioral biases in financial markets, with particular reference to the Indian setting.