低成本AI通用逼近器如何重塑市场效率
How low-cost AI universal approximators reshape market efficiency
AI总结：
本文基于均衡价格过程定义的广义市场效率概念，探讨了低成本AI通用逼近器普及推动交易策略升级、增加低效交易者盈利难度的机制，以及AI交易与市场效率演化的关系，并指出了自适应多智能体环境下非均衡市场动态研究的需求与挑战。
摘要：
有效市场假说（EMH）有一个著名论断：价格完全反映了交易者可获取的信息。这一特性的关键在于信息通过交易策略转化为价格的过程。交易者会使用复杂度不断提升的模型优化自身策略，这些模型能越来越精准地识别信息与盈利交易之间的关联。在特定条件下，低成本通用逼近器（例如AI系统）的普及度提升，理应会自然推动更先进的交易策略发展，这可能会让低效交易者越来越难获利。本文中，我们基于均衡价格过程的定义采用了一种广义的市场效率概念，该概念能让我们通过投资者信念与交易策略优化区分不同层级的模型复杂度，并探讨AI驱动的交易与市场效率随时间演化的关系。最后，我们概述了在自适应多智能体环境中描述非均衡市场动态的必要性与挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
