These papers are currently in draft status and should not be cited
James, Andrew. April 2026. AI in Financial Markets: A framework for AI induced market endogeneity.
Abstract
The adoption of artificial intelligence in finance is widely assumed to enhance market efficiency by reducing human bias and improving information processing. This paper challenged this assumption and argued that artificial intelligence systems in financial markets do not enhance efficiency, instead they replicate and enhance historically embedded inefficiencies. Challenging the assumption that data-driven trading leads to more rational markets, by showing how financial data encodes behavioural bias and structural constraints that persist under limits to arbitrage. Drawing on behavioural finance, limits to arbitrage and reflexivity, the paper will propose a conceptual framework of AI-induced market endogeneity, in which AI models become endogenous to price formation rather than an external optimiser. The paper also suggests a regulatory approach for AI models in financial markets including a discussion on model governance, transparency and accountability.
Available here: https://rpc.cfainstitute.org/blogs/enterprising-investor/2026/vix-policy-uncertainty-risk-signal