Article’s

The rapid evolution of financial technology (fintech) has brought about revolutionary changes in investment management through robo-advisors and algorithmic trading. The current research seeks to examine the effect of these technologies on portfolio diversification and herding behavior among investors in the Indian retail stock market. Applying a quantitative deductive research methodology, the study uses secondary financial data collected from National Stock Exchange (NSE), Bombay Stock Exchange (BSE), and popular robo-advisory platforms in the time span 2019–2024. To assess portfolio diversification, the Herfindahl-Hirschman Index (HHI) and the correlation analysis among the assets held by investors will be used. On the other hand, the Cross- Sectional Absolute Deviation (CSAD) model and the Lakonishok-Shleifer-Vishny (LSV) index will be applied to measure herding behavior among investors. The research findings demonstrate that portfolios advised by robots have much lower HHI (0.21) than portfolios managed by traditional methods (0.39), which shows higher levels of diversification. At the same time, herding behavior was proved to exist based on the negative value of the CSAD model’s squared market return coefficient (β= -0.35; p=0.02), with LSV figures rising from 0.07 in relatively stable market periods to 0.19 when markets become highly volatile. Furthermore, regression analysis proves the existence of a significant negative relationship between algorithmic trading and portfolio concentration (β=-0.28; p=0.01). Thus, robo-advisors have been proved not only to lead to more effective diversification but also to generate herding risk due to their similarity. These findings have important implications for investors, fintech companies, government authorities, and financial regulation bodies.