How Emotional Bias Effect The Investment Decision Making Of Retail Investors In India: Using Pls-Sem, Ann And Fsqca

Abstract

According to the expected utility theory, investors are assumed to be logical decision-makers in conventional finance theory. On the other hand, behavioural finance strongly challenges this rational viewpoint, claiming that investors frequently stray from reason when making financial decisions. Investing decisions these days are frequently made irrationally by investors. Their judgement, which is very different from logical assumption, is frequently the basis for the decision. Investors' decision-making is typically influenced by a variety of objectivities, emotions, and psychological elements when they are faced with risky situations. The objective of the this study is to identify the impact of emotional biases (Herding, Loss aversion, Regret aversion and status quo) on investment decision making and identify the most influential behavioural bias. Data are gathered from 379 Indian retail investors, who represent the majority of Indian states and union territories (UTs), using a multi-stage stratified random sampling technique. The study utilised the PLS- SEM approach in conjunction with artificial neural network (ANN) analysis and fuzzy set qualitative comparative analysis (fsQCA) to examine the proposed associations, verify the validity of the results, and extract significant practical knowledge. The findings of the research indicates that herding bias, loss aversion bias, regret aversion bias and status quo have a significant positive relation with investment decision making (IDM). And as per the outcome of ANN sensitivity analysis and fsQCA, loss aversion bias (LAB) is the most crucial determinant of investment decision making. It is followed by regret aversion bias (RAB), status quo bias (SQB), herding bias (HB). This study holds significance as it offers crucial implications for researchers, market participants, policymakers, and regulators involved in the development of Indian stock market.