Behavioral Prediction of Mongolian Investors using Machine Learning Techniques

In this study, we employed machine learning to investigate the impact of behavioral and psychological factors on investment decision-making in the Mongolian stock market. Survey data were collected from individual investors and analyzed using Principal Component Analysis (PCA) with Varimax rotation to extract latent behavioral constructs. Three core factors were identified: Market Reaction and Short-Term Trends, Sensitivity to News and Fundamental Information, and Risk Attitude and Self-Confidence. Using these factors, K-means clustering revealed three investor profiles: Independent Risk Seekers, Reactive Traders, and Cautious Fundamental Investors. Subsequently, Random Forest, Logistic Regression, and Gradient Boosting models were trained in Python to predict investors’ “buy or sell” decisions. Among the tested algorithms, Logistic Regression achieved the highest performance (Accuracy= 0.765, AUC= 0.707, Precision= 0.72, Recall= 0.69). These results demonstrate the potential of machine learning to quantify psychological behavior and implement behavioral finance theory.