限价订单市场中的强化学习与理性预期均衡

IF 1.9 3区 经济学 Q2 ECONOMICS
Xuan Zhou , Shen Lin , Xue-Zhong He
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引用次数: 0

摘要

本文表明,简单的基于收益的强化学习有助于在限价订单市场中实现理性期望均衡。在均衡状态下,投机者主要提供流动性,而具有内在交易动机的非投机者的私人价值增加了流动性消费。在非投机者信息获取的驱动下,投机者的流动性消费在基本面波动中呈驼峰型,而非投机者的流动性消费在基本面波动中呈u型。相比之下,流动性供应在投机者的基本波动中减少,但在非投机者的基本波动中呈驼峰状。与根据资产基本面进行交易的知情交易者不同,不知情的交易者更多地根据订单和交易信息进行交易。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement learning and rational expectations equilibrium in limit order markets
This paper shows that simple payoff-based reinforcement learning can help to achieve rational expectations equilibrium in limit order markets. In equilibrium, speculators mainly supply liquidity, while liquidity consumption increases in the private values of no-speculators with intrinsic motives for trade. Driven by information acquisition of the non-speculators, liquidity consumption is hump-shaped in fundamental volatility for the speculators but U-shaped for the non-speculators. In contrast, liquidity supply decreases in fundamental volatility for the speculators but is hump-shaped for the non-speculators. Unlike the informed traders who trade on asset fundamentals, the uninformed traders trade more on order book and trading information.
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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