Yatırımcı tipleri ve riskten kaçınma: Finansal karar vermede veri bilimi yaklaşımı

This study examines the manifestation of behavioral biases in investor decisionmaking through a quantitative data science approach using the FAR-Trans dataset. We developed empirical measures for three key behavioral finance constructs (loss aversion, herding behavior, and overconfidence) derived from established theoretical frameworks and applied them to classify investors into distinct behavioral groups. A decision tree-based multi-class classification model was employed to predict behavioral bias categories, demonstrating exceptional predictive performance with 96% accuracy and macro-average ROC-AUC scores of 0.99 validated through crossvalidation analysis. Statistical significance tests and visual analyses confirm that each behavioral group exhibits clearly distinguishable patterns in their respective dominant metrics, with loss-averse investors displaying higher tendencies to maintain losing positions, herding investors showing increased propensity to follow popular assets, and overconfident investors engaging in more frequent trading activities. These findings provide robust empirical evidence for the effective identification and classification of behavioral biases from transactional financial data and demonstrate the practical utility of machine learning techniques for understanding psychological factors in financial markets.