Behavioral Finance and Computational Models: A Neural Network Approach to Understanding Investor Behavior

Abstract

Behavioral finance studies the impact of psychological and emotional factors on investors' financial decisions. Neural networks, as a tool of artificial intelligence, are applied in analyzing complex data and predicting financial behaviors, offering significant improvements in analyzing irrational investor behaviors. The study examined the impact of neural networks in enhancing the prediction of investors’ financial behaviors and compare them with other computational models. This study conducted a comprehensive review of existing research on the use of neural networks in behavioral finance. Data was collected from scientific articles and reputable research studies available in databases. The analyses were aimed at assessing the effectiveness of neural networks in predicting investor behaviors and comparing them with traditional models. The findings showed that neural networks, due to their ability to process complex data and identify nonlinear patterns, exhibit higher accuracy in predicting financial behaviors compared to traditional models. These models are capable of identifying irrational investor behaviors such as overconfidence and herding behavior. Furthermore, neural networks performed better in predicting market fluctuations and identifying financial risks. The integration of neural networks with behavioral finance can significantly improve financial decision-making and risk management in complex markets. However, the need for high-quality data and the development of new methods to enhance the transparency and explainability of neural networks remains crucial.