Modern Behavioural Finance Theories

This chapter enhances the discussion on behavioural finance by presenting innovative frameworks that tackle the increasing complexity of contemporary financial systems. This chapter examines the adaptive market hypothesis (AMH), which harmonises behavioural anomalies with market evolution principles, as well as the use of artificial intelligence and machine learning in sentiment-driven asset pricing models. These methodologies provide a detailed comprehension of how technology innovations are transforming the behavioural patterns of market actors. Primary areas of emphasis are the use of big data analytics and natural language processing to measure investor sentiment and discern trading patterns affected by cognitive biases. The chapter analyses the behavioural consequences of algorithmic trading, emphasising how automation may enhance or alleviate psychological inclinations like swarming and overreaction. This chapter illustrates how the integration of theoretical ideas and practical applications enables modern behavioural finance to provide a comprehensive framework for tackling contemporary market difficulties. It underscores the essential need of merging behavioural insights with technical instruments to optimise risk management, augment market efficiency, and respond to the swiftly evolving environment of global finance. This chapter establishes current behavioural finance as crucial for comprehending and navigating the interaction between human behaviour and algorithmic market systems.