Behavioral Biases and Investment Decisions: Analyzing the Influence of Heuristics, Prospects and Planned Behavior on Retail Investors

Abstract

Background: Behavioural finance challenges the assumption of fully rational investment decisions by showing that cognitive biases can shape how retail investors evaluate risk, information and market opportunities. In this context, integrating behavioural biases with the Theory of Planned Behaviour provides a useful framework for examining the investment intentions of retail   investors. Aims: This study examines the influence of behavioral biases and TPB components such as attitude, subjective norms, and perceived behavioral control on investment intentions of retail investors based in Delhi-NCR region, where behavioral biases are treated as second order construct, consisting of anchoring, availability, loss aversion, mental accounting, overconfidence, representativeness, and regret aversion. Method: A structured questionnaire was administered to 379 retail investors using purposive and snowball sampling techniques. The data were analysed using PLS-SEM (SmartPLS 4.0) with bootstrapping based on 10,000 resamples. Findings: Behavioral biases emerged as the strongest predictor of intention to invest (β = 0.441, p < 0.001) followed by perceived behavioral control (β = 0.199, p < 0.001), attitude (β = 0.159, p = 0.001), while subjective norms were insignificant (β = 0.088, p = 0.113). It was further noted that attitude mediated the link between behavioral biases and the intention to invest (β = 0.149, p < 0.001). The model accounted for 58.6% of variance (R² = 0.586). Conclusion: This study provides a validated second-order model of behavioral biases and demonstrates that biases outperform TPB constructs, thus broadening TPB to behavioral finance theory. This mediation by attitude also suggests that there are two parallel decision routes for investment, one involves evaluation of attitudes, while the other route goes directly to making the decisions based on affects. This study advises on design of intervention strategies to mitigate bias and boost self-efficacy among advisors, fintech, regulators and educators.