The Role of Behavioral Finance In Investment Decision-Making Using Machine Learning-Understanding the Psychology Behind Stock Market Trends

Background: By investing decision-making, behavioral finance assists one to better understand the psychological aspects impacting stock market patterns. The conventional wisdom on finance assumes that reasonable decision-making occurs. Behavioral finance, on the other hand, draws attention to the heuristics and preconceptions influencing investing choices. Problem Statement: These address overconfidence, herd behavior, cover loss aversion. The question under study is how these cognitive biases influence personal portfolio strategies and market movements, therefore producing likely mispricing and volatility. Findings: The study maps behavioral trends using a simulation model comprising historical market data and investor sentiment monitoring via machine learning, so including psychological elements shown by Results of a one thousand trading day simulation showing a 15.2% increase in expected accuracy when compared with conventional models. Outcome: This outcome also revealed clear relationships between sentiment-driven trading and price fluctuations. One instance of this would be the measurement of loss aversion, which showed a twenty percent drop in risk tolerance after a negative market shock. Moreover, the findings of this study underscore the significance of adding psychological insights into investment techniques to gain more accurate forecasting and portfolio management.