(untitled) (Journal of Informatics Education and Research, 2024)
This paper explores the intersection of psychology and financial markets through the lens of behavioral finance, specifically examining how human emotions and cognitive biases such as overconfidence, loss aversion, herding, and anchoring impact investor behavior and market volatility. Traditional financial theories, like the Efficient Market Hypothesis, posit that investors act rationally, yet empirical evidence suggests significant deviations due to psychological influences. By integrating theories from pioneers like Kahneman and Tversky, and through a mixed-methods approach combining quantitative regression and correlational studies with qualitative content analyses, this study assesses how behavioral biases influence investment decisions and market dynamics, especially during periods of market extremes. The findings reveal that biases like overconfidence and herding exacerbate market volatility, while loss aversion may moderate it. The study also explores the role of modern digital platforms in amplifying these biases and suggests strategies to mitigate their adverse effects. The implications of this research are vast, offering insights that could lead to more robust financial models and effective regulatory frameworks that accommodate the psychological nuances of investor behavior.