The Algorithmic Market Hypothesis: Information Efficiency in the Age of AI
TL;DR
It is concluded that although prices reflect the dominant algorithmic interpretation of information, ultra-fast information processing by AI may render markets less predictable rather than more efficient, as prediction itself becomes endogenous to the system being predicted.
Abstract
This paper examines the transformation of financial markets as trading shifts from human-driven to algorithmically dominated activity, with particular focus on large language models (LLMs) as a distinct and increasingly influential category of market participant. We argue that LLMs represent a qualitative shift from traditional algorithmic investing such as high-frequency trading platforms to novel forms of information processing that simultaneously reduce certain information asymmetries while creating new systemic risks. Building on the Grossman– Stiglitz paradox of informationally efficient markets, we demonstrate how the decreasing cost of information processing paradoxically increases market inefficiencies through data mining and the proliferation of false positives. The paper develops new frameworks for understanding markets where price discovery occurs through the interaction of diverse AI architectures, including high-frequency time-series models and natural language processing systems. It then examines the emergence of company-specific semantic factors as sources of alpha. We conclude that although prices reflect the dominant algorithmic interpretation of information, ultra-fast information processing by AI may render markets less predictable rather than more efficient, as prediction itself becomes endogenous to the system being predicted. The paper’s key arguments and findings are as follows: