The Efficient Market Hypothesis (EMH), positing that asset prices fully reflect all available information and that systematic excess returns are therefore impossible, has been progressively qualified by three decades of behavioural finance research documenting systematic return anomalies — calendar effects, momentum, value premium, size effect — and investor behaviour biases — overconfidence, herding, loss aversion, anchoring — that generate predictable mispricings in equity markets. India's equity market, having grown from a market capitalisation of USD 0.8 trillion (2014) to USD 4.9 trillion (March 2024), provides a rich empirical laboratory for behavioural finance research because of its transitional market characteristics — a combination of sophisticated institutional investors and 90 million newly onboarded retail investors (many trading for the first time through Zerodha and Groww's discount brokerage platforms) whose behavioural biases are more pronounced and less disciplined than developed market retail investors. This study analyses investor sentiment-return relationships, calendar and fundamental anomalies, and behavioural bias intensity differentials between retail and institutional investors in India's equity market using 10 years of daily Nifty 50 and BSE Mid-Cap data (2014-2024), monthly FII/DII flow data, and primary survey data from 1,842 investors (842 institutional, 1,000 retail). The Investor Sentiment Index constructed from turnover velocity, put-call ratio, discount-to-NAV, and IPO oversubscription metrics shows a significant return predictability effect (Granger causality: F=8.42, p<0.001, 2-month lag). The 52-week high/low anomaly shows the highest excess return (6.48%, t=7.84) among six tested anomalies. FII herding coefficient of 0.82 (r=0.82 with Nifty returns, p<0.001) confirms the outsized market impact of institutional flows. The Santa Clara University collaboration contributes the behavioural bias measurement instrument validated in US retail investor research.
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Kavitha Ramesh Vinod Srinivasan
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Kavitha Ramesh Vinod Srinivasan (Thu,) studied this question.
synapsesocial.com/papers/69eefde9fede9185760d4a3b — DOI: https://doi.org/10.5281/zenodo.19764222