Behavioral Finance in Dynamic Risk Management: Understanding Investor Psychology and Market Implications

Abstract

Behavioral finance has fundamentally reshaped the understanding of financial decision-making
by demonstrating that investors and market participants are not always rational actors. Cognitive
biases, heuristics, emotional responses, and social influences often drive decisions, leading to
market anomalies, mispricing, and deviations from traditional risk-return paradigms. Integrating
behavioral insights into financial risk management provides a more nuanced and realistic
framework for understanding and mitigating these effects.
This review examines the role of behavioral factors in dynamic risk management, emphasizing
applications across portfolio optimization, hedging strategies, risk assessment, and regulatory
compliance. It explores how behavioral biases—such as overconfidence, loss aversion, herding,
and framing effects—can impact investment outcomes and risk exposure, and how these insights
can be leveraged to design more effective risk mitigation strategies. Additionally, the paper
highlights emerging computational approaches, including agent-based modeling, sentiment

analysis, and machine learning, which enable the quantification and simulation of behavioral
effects at scale.
Key frameworks, empirical evidence, and practical applications are discussed, demonstrating
how behavioral finance complements traditional financial models by incorporating psychological
and social dimensions of decision-making. The review underscores the importance of integrating
behavioral factors into modern investment strategies to enhance portfolio resilience, improve
risk-adjusted returns, and support evidence-based regulatory and policy interventions. By
combining behavioral insights with advanced analytical tools, financial institutions can better
anticipate market dynamics, adapt to investor behavior, and build more robust, adaptive risk
management systems.