How low-cost AI universal approximators reshape market efficiency

ArXiv ID: 2501.07489 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Efficient Market Hypothesis (EMH), AI-powered Trading, Market Efficiency, Equilibrium Price Process, Multi-agent Systems, General Financial Markets

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats
  • Why: The paper builds on established theoretical frameworks (e.g., Timmermann & Granger, 2004; Jarrow & Larsson, 2012) and proposes formal models for market efficiency under AI influence, indicating moderate-to-high mathematical complexity; however, it focuses entirely on theoretical hypotheses and equilibrium concepts without presenting any empirical data, backtests, or implementation details, resulting in low empirical rigor.
  flowchart TD
    A["Research Goal: <br>How do low-cost AI universal approximators <br>reshape market efficiency?"] --> B["Methodology: <br>Generalised market efficiency framework <br>Disting. model complexity via beliefs <br>Analyse equilibrium price process"]
    B --> C["Inputs: <br>Financial market data <br>AI trading models <br>Investor belief distributions"]
    C --> D["Computation: <br>Multi-agent simulation <br>Strategy optimisation analysis <br>Out-of-equilibrium dynamics"]
    D --> E["Key Findings: <br>1. AI advanced models push market <br>toward higher efficiency <br>2. Harder for inefficient traders to profit <br>3. Need adaptive multi-agent frameworks <br>for out-of-equilibrium dynamics"]