Abstract
Behavioral finance emerged as a response to the limitations of classical financial theory, revealing that investor behavior is shaped by cognitive biases, heuristics, and emotional influences rather than pure rationality. The rapid diffusion of artificial intelligence (AI) across financial systems now challenges this foundation, introducing new dynamics in how decisions are made, information is processed, and markets evolve. This paper examines the theoretical implications of AI for behavioral finance, proposing that algorithmic systems act not merely as analytical tools but as active participants in financial cognition. Through concepts such as computational rationality, algorithmic irrationality, and algorithmic nudges, the study explores how technology both mitigates and amplifies human biases, creating hybrid decision environments where psychological and algorithmic factors interact. The framework extends the Adaptive Market Hypothesis (AMH) to an AI-driven context, conceptualizing market efficiency as a co-evolutionary process shaped by human learning and machine adaptation. While AI enhances information assimilation and decision precision, the convergence of similar algorithmic models introduces new systemic risks, including volatility, feedback loops, and loss of market diversity. By integrating insights from behavioral economics, cognitive psychology, and computational finance, this paper advances a revised theoretical model of financial behavior in the algorithmic era. It argues that understanding the cognitive-algorithmic interface is essential for scholars, practitioners, and regulators seeking to interpret market dynamics in increasingly automated and data-driven environments.
Keywords:
Algorithmic Decision-Making, Behavioral Finance, Cognitive Bias, Investor Behavior, Market DynamicsReferences
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Copyright & License
This work is licensed under a Creative Commons Attribution 4.0 International License.