Accuracy And Risk: Drivers Of Strategic Trading Decisions

Abstract

This research paper examines the dual drivers of Perceived Accuracy and Perceived Risk in shaping strategic trading decisions within AI-augmented environments. As artificial intelligence transforms retail trading platforms, understanding how traders cognitively balance the promise of algorithmic precision against the perils of technological dependency becomes critical. Grounded in Prospect Theory and the Stimulus-Organism-Response framework, this study investigates how these competing perceptions influence decision-making processes and outcomes. Utilizing a mixed-methods approach combining experimental trading simulations, psychometric assessments, and eye-tracking analysis with 428 active traders, the research reveals a complex, non-linear relationship. Findings demonstrate that Perceived Accuracy and Perceived Risk operate not as independent factors but as dynamically interacting forces, with their relative influence shifting according to market conditions, trader experience, and decision context. High Perceived Accuracy significantly enhances decision confidence and strategic consistency (β=0.47, p<0.001) but can paradoxically increase risk exposure through overreliance. Perceived Risk functions as both an inhibitor and a calibratorexcessive risk perception degrades decision quality through anxiety-induced impairment, while moderate levels enhance vigilance and analytical depth. The study identifies critical thresholds and interaction effects, presenting a decision framework that balances accuracy-driven opportunity capture with risk-aware preservation. These insights offer significant implications for interface design, risk communication, trader education, and regulatory approaches in increasingly automated financial ecosystems.