Behavioral Finance Models for Understanding Investor Decision Patterns

Abstract

Behavioral finance provides an important perspective for understanding why investors often deviate from rational decision-making assumptions. This study examines investor decision patterns by integrating cognitive, emotional, and social factors within a behavioral finance framework. The study considers overconfidence, anchoring, loss aversion, and herding as key behavioral influences and examines their effects on risk-taking, trading frequency, and investment selection. Prospect Theory, the Heuristic Model, the Overconfidence Model, and the Herding Model are incorporated to explain different dimensions of investor behavior. A conceptual regression model is formulated to examine the relationship between behavioral factors and investor decision patterns. The results indicate that cognitive biases influence information evaluation and trading activity, while emotional factors affect risk perception and responses to gains and losses. Social influence, particularly herding, encourages investors to follow the actions of other market participants and can affect investment selection. The findings further indicate that overconfidence is associated with increased trading and risk-taking, while loss aversion contributes to greater sensitivity toward investment losses. Overall, the study demonstrates that investor decision patterns are shaped by the combined effects of cognitive, emotional, and social factors. The findings provide useful implications for investors, financial institutions, and policymakers by emphasizing behavioral awareness, investor education, and improved decision-support mechanisms.