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市场信息程度如何影响做市商盈利能力

文章 arXiv papers · 作者: Konrad Ochędzan et al.
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总结

本文通过基于智能体的计算市场研究知情交易的普遍程度如何影响做市商利润。模型包含信息程度和库存风险厌恶程度各异的做市商、外生基本价值、内生价格，以及遵循状态依赖自激过程的市场接受者订单流。作者证明了该订单流过程在有限时域内的稳定性，并采用集中训练、分散执行的多智能体强化学习求解做市问题。

模拟结果显示，当市场整体信息程度较低时，知情订单流可能尤其有害，使做市商面临逆向选择。随着信息程度提高，盈利能力总体呈上升趋势，但复杂动态和随机学习过程中也会出现局部非单调变化。作者提出的解释是，价格发现能力提升最终可能抵消逆向选择成本。这些是来自简化模型的计算结果；描述没有提供实证市场验证，也未说明结果对其他模型假设的稳健性。

核心观点

  • 模型结合了异质做市商、内生价格和自激的市场接受者订单流。
  • 当市场整体信息程度较低时，知情订单流可能带来严重的逆向选择成本。
  • 模拟中，市场信息程度较高与做市商盈利能力总体上升相关。
  • 价格发现改善可能抵消逆向选择成本，但局部结果呈非单调变化。
  • 研究使用多智能体强化学习求解模型中的流动性供给问题。

标签

做市市场微观结构机器学习统计学
全文
# Market Informedness and Market-Maker Profitability: The Trade-Off Between Adverse Selection and Price Discovery


# Market Informedness and Market-Maker Profitability: The Trade-Off Between Adverse Selection and Price Discovery









This paper studies how market informedness affects market makers' profitability in a computational market environment with heterogeneous learning agents. We develop an agent-based market model in which market makers differ in their information sets and inventory-risk aversion, prices form endogenously, fundamental values evolve exogenously, and market-taker order flow follows a state-dependent self-exciting process. The model provides a controlled computational laboratory for analyzing the interaction between informed trading, adverse selection, price discovery, and liquidity provision. We establish finite-horizon stability properties of the market-taker order-flow process and solve the market-making problem using multi-agent reinforcement learning with centralized training and decentralized execution. The results show that informed market order flow is particularly harmful when aggregate market informedness is low, exposing market makers to severe adverse-selection risk. However, as market informedness increases, market-maker profitability displays an overall upward trend despite local non-monotonicities arising from complex market dynamics and stochastic learning. This suggests that the price-discovery benefits of informed trading can offset its adverse-selection costs. The findings contribute to computational economics by showing how agent heterogeneity, endogenous price formation, and learning-based liquidity provision jointly shape market outcomes.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。