This thesis examines the intricate relationship between cognitive biases, risk perception, and market dynamics within the context of investor decision-making. It challenges the traditional notion of rational financial behavior by integrating insights from behavioral finance, highlighting how psychological and emotional factors systematically influence individual and institutional investment choices. Through an analysis of key cognitive biases - including overconfidence, anchoring, and herding—and their impact on risk perception, the study reveals how these biases contribute to suboptimal decisions, market inefficiencies, and systemic risks. The divergence between perceived and objective risk, often driven by emotional responses such as fear and greed, is shown to significantly affect investment behavior and heighten market volatility. A mixed-methods approach is used, combining empirical case studies (e.g., the 2008 financial crisis, COVID-19 shock) with statistical analysis to assess sentiment, volatility, and price dynamics. The findings align strongly with behavioral finance theories, suggesting that sentiment has only a weak and inconsistent influence on objective market indicators, reinforcing the need for deeper psychological modeling. The study also explores the integration of behavioral finance into financial models, regulatory frameworks, and technological tools such as AI and robo-advisors. Ultimately, it argues that incorporating behavioral insights into policy and practice is essential for enhancing market efficiency, improving investordecision-making, and creating a more stable financial ecosystem.
M. Paradiso (Sat,) studied this question.