Neuromarketing Predictors Of Retail Trading Surges: A Brain-Behavior Analysis Using Indian Stock App Users

Abstract

India’s rapid transition from conventional investing to app-mediated securities trading has created an environment in which investor attention, emotional arousal, behavioral bias, interface design and market information interact almost instantaneously. This study develops and tests a neuromarketing-based framework for explaining short-duration retail trading surges among Indian stock-app users. The proposed model integrates electroencephalographic measures of approach-related neural activity and cognitive engagement, eye-tracking indicators of visual attention, electrodermal arousal, self-reported behavioral biases and observed trading responses. The conceptual foundation combines consumer neuroscience, the Stimulus-Organism-Response framework, behavioral finance and investor-attention theory. Recent evidence demonstrates that EEG and multimodal physiological measures can predict consumer preferences and purchase decisions with meaningful out-of-sample accuracy, while experimental financial research shows that gamification, attention cues and digital nudges can increase trading volume and financial risk-taking. Recent Indian evidence further documents the importance of loss aversion, herding, anchoring, overconfidence, social-media exposure and financial literacy in retail investment decisions.

The proposed empirical design involves active Indian stock-app users exposed to controlled trading-interface conditions varying market momentum, social proof, urgency cues and gamification intensity. Brain-behavior indicators are linked to trading initiation, order frequency, response latency, position size and speculative-risk preference. The analytical architecture combines confirmatory factor analysis, reliability and validity testing, mixed-effects models, structural equation modelling, mediation and moderation analysis, effect-size estimation and predictive machine-learning validation. The evidence framework predicts that heightened neural approach motivation, visual fixation on salient market signals, physiological arousal, FOMO and overconfidence jointly increase trading intensity, whereas financial literacy and reflective decision friction weaken stimulus-to-trade pathways. The study contributes to behavioral finance by introducing neurophysiological mechanisms into the explanation of digital retail trading and contributes to management practice by identifying interface interventions capable of preserving engagement while reducing impulsive and potentially harmful trading.