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用于公平市场费用设计的强化学习

文章 arXiv papers · 作者: Kshama Dwarakanath et al.
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总结

本文介绍一种用于设计市场费用的强化学习框架，并考虑交易者会根据费用调整策略。该框架联合学习费用方案和交易者策略，在盈利能力与不同交易者群体之间的明确公平性目标之间进行权衡。

作者在包含做市商和消费者投资者的模拟股票交易所中展示了该方法。在模拟中，调整分配给不同投资者群体的相对权重，会使学习得到的费用安排向权重较高的群体倾斜。该示例表明，公平性目标会与运营方收入和参与者利润一起影响市场定价。证据仅限于所述模拟；本文未报告真实市场测试，也未说明该框架在其他市场环境中的表现。

核心观点

  • 由于参与者会根据费用方案调整策略，市场费用能够影响交易者行为。
  • 该框架同时学习交易者策略和市场费用。
  • 加权目标在盈利与不同交易者群体的公平结果之间进行权衡。
  • 在模拟交易所中，提高某一群体的公平性权重会使费用方案倾向于该群体。

标签

做市机器学习市场微观结构统计学
全文
# Equitable Marketplace Mechanism Design


# Equitable Marketplace Mechanism Design









We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilitates matching between buyers and sellers. In return, such a marketplace typically charges fees for facilitating trade. The goal of this work is to design a dynamic fee schedule for the marketplace that is equitable and profitable to all traders while being profitable to the marketplace at the same time (from charging fees). Since the traders adapt their strategies to the fee schedule, we present a reinforcement learning framework for simultaneously learning a marketplace fee schedule and trading strategies that adapt to this fee schedule using a weighted optimization objective of profits and equitability. We illustrate the use of the proposed approach in detail on a simulated stock exchange with different types of investors, specifically market makers and consumer investors. As we vary the equitability weights across different investor classes, we see that the learnt exchange fee schedule starts favoring the class of investors with the highest weight. We further discuss the observed insights from the simulated stock exchange in light of the general framework of equitable marketplace mechanism design.

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。