Behavioural Finance: Investor Biases and Decision-Making among Retail Investors in Delhi – An Empirical Analysis

Abstract

Traditional finance paradigms, rooted in the Efficient Market Hypothesis (EMH) and rational actor models (e.g., Markowitz portfolio theory), posit that investors process information optimally to maximize utility. However, persistent market anomalies—such as excessive volatility, bubbles, and under reaction—challenge these assumptions, paving the way for behavioural finance. This discipline integrates cognitive psychology to elucidate biases like overconfidence, loss aversion, and herding those systematically deviate decisions from rationality (Kahneman & Tversky, 1979; Shiller, 2000).

In Delhi-NCR, India's premier financial hub commanding over 20% of the nation's 10+ crore demat accounts (SEBI, 2025), retail participation surged post-2020 amid digital brokerage booms (e.g., Zerodha, Groww) and pandemic-induced market rallies. Yet, localized evidence on biases remains sparse, despite cultural factors amplifying herding. This study addresses the gap through a cross-sectional survey of 400 Delhi-NCR retail investors (stratified by age, income, and experience), employing validated Likert-scale instruments to measure 12 key biases. Data analysis via SPSS regression and SEM reveals overconfidence prevalence at 68% (M=3.9/5), significantly correlating with excessive trading (β=0.45, p<0.001), while herding explains 42% of variance in suboptimal decisions (R²=0.42). Demographic moderators show young males (<35 years) most affected, eroding annual returns by 2-3%. Findings underscore behavioral deviations' role in India's retail boom inefficiencies. Implications advocate SEBI-led policy nudges (e.g., bias-alert fintech apps, mandatory NISM behavioural modules) and investor education to foster sustainable participation. This work extends prospect theory to emerging-market contexts, urging longitudinal follow-ups post-2026 reforms.