AI Model Complexity, Trading, and the Evolution of Market Efficiency

Summary

This paper considers how inexpensive AI systems that approximate complex relationships could change the way trading strategies incorporate information into prices. It frames market efficiency through equilibrium price processes, using investors’ beliefs and the complexity of their models and optimized strategies to distinguish levels of efficiency.

The authors discuss how broader access to more capable approximators may make profitable opportunities harder for traders with less effective models to exploit. They also argue that equilibrium-based accounts are not enough to describe how efficiency changes in an adaptive market with interacting participants, and identify out-of-equilibrium dynamics as a challenge. The supplied text is conceptual: it outlines a framework and research problem but gives no empirical results, specific trading rules, or evidence that AI adoption necessarily makes markets more efficient. Its conclusions depend on the stated conditions and on how models, beliefs, and adaptation are represented.

Key ideas

• Trading strategies transmit information into prices, linking model quality to market efficiency.
• Low-cost AI approximators may enable more complex strategies and reduce opportunities for less effective traders.
• The paper uses equilibrium price processes and investor beliefs to distinguish levels of market efficiency.
• Adaptive multi-agent markets may move out of equilibrium, creating challenges for standard efficiency analysis.
• The supplied description presents a conceptual discussion without empirical findings or a specific trading strategy.

Tags

# How low-cost AI universal approximators reshape market efficiency


# How low-cost AI universal approximators reshape market efficiency









The efficient market hypothesis (EMH) famously stated that prices fully reflect the information available to traders. This critically depends on the transfer of information into prices through trading strategies. Traders optimise their strategy with models of increasing complexity that identify the relationship between information and profitable trades more and more accurately. Under specific conditions, the increased availability of low-cost universal approximators, such as AI systems, should be naturally pushing towards more advanced trading strategies, potentially making it harder and harder for inefficient traders to profit. In this paper, we leverage on a generalised notion of market efficiency, based on the definition of an equilibrium price process, that allows us to distinguish different levels of model complexity through investors' beliefs, and trading strategies optimisation, and discuss the relationship between AI-powered trading and the time-evolution of market efficiency. Finally, we outline the need for and the challenge of describing out-of-equilibrium market dynamics in an adaptive multi-agent environment.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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