Human Bias in Algorithmic Trading: Evaluating Behavioral Finance Impacts on Automated Systems

This research study aims to explore how algorithmic trading systems are affected by human bias. The study will examine how automated trading methods and behavioral finance technology interact. The relationship between these two disciplines is the focus of the paper in particular. Nevertheless, even though algorithmic trading aims to reduce human intervention and increase computing efficiency, the innate prejudices that accompany human decision-making impact these systems. This study aims to investigate how trading algorithms are biased by cognitive biases including overconfidence and aversion to losses, and how these biases impact the algorithms’ performance. It is possible to assess the effects that these biases have on market behavior and trade results by looking at case studies and real market data collected. The findings highlight how crucial it is to use behavioral finance knowledge to develop and apply trading algorithms to increase their resilience and flexibility. This is due to the results illuminating how vital it is to use these ideas. This research adds to a deeper knowledge of the constraints of the existing algorithmic trading techniques by offering suggestions for mitigating the effects of human bias on automated trading systems. Furthermore, it advances our comprehension of the constraints associated with these methods.