Behavioral Financial Analytics: A Conceptual Model for Explaining Enterprise Performance

Behavioral Financial Analytics (BFA) integrates behavioral finance theories with advanced data analytics to explain variations in enterprise performance beyond traditional financial indicators. This review conceptualizes a model that links cognitive biases, decision heuristics, and organizational learning with measurable firm outcomes. It explores how managerial behavior, investor sentiment, and market perceptions collectively shape corporate financial trajectories, particularly under conditions of uncertainty and bounded rationality. By combining behavioral insights with predictive analytics, the model demonstrates how latent psychological variables—such as overconfidence, herding tendencies, and loss aversion—can be quantified to forecast strategic and operational performance. The paper synthesizes empirical findings from behavioral economics, management science, and data-driven finance, emphasizing how integrating machine learning and sentiment analysis can uncover behavioral determinants of profitability, liquidity, and innovation capacity. Furthermore, the review discusses the implications of behavioral analytics for risk assessment, governance structures, and performance optimization in both established firms and startups. The proposed conceptual model provides a framework for analyzing the dynamic interplay between human behavior and enterprise systems, offering an actionable lens for improving decision-making, competitiveness, and resilience in modern organizations.