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利用短期订单流信号进行最优执行与投机

文章 arXiv papers · 作者: Peter Bank et al.
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总结

本文将价格建模为随订单流变动。市价单的价格冲击以及市价单和限价单的到达率取决于流动性过程，两种订单类型相互激发，使流动性趋向均值回归。交易者收到关于订单流即将变化的短期信号，并通过与其他参与者相同的订单机制影响市场。

作者使用Meyer σ-代数表示信号，并使用Marcus型随机微分方程处理同时订单的时间安排。作者通过Hamilton–Jacobi–Bellman方程建立交易者的问题，并进行数值求解。报告的示例显示，信号可以提升交易执行效果并支持投机交易，但文中未提供数值表现指标或实证市场验证。因此，该框架的实用性取决于模型假设和校准。

核心观点

  • 价格由订单流驱动，而订单冲击和订单到达率随流动性变化。
  • 限价单与市价单的相互激发会产生均值回归的流动性。
  • 短期信号向交易者提示近期订单流可能发生的变化。
  • 最优交易问题被表述为HJB方程，并通过数值方法求解。
  • 该模型示例说明如何将信号用于执行和投机策略。

标签

交易执行市场微观结构高频交易统计学
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# Optimal execution and speculation with trade signals


# Optimal execution and speculation with trade signals









We propose a price impact model where changes in prices are purely driven by the order flow in the market. The stochastic price impact of market orders and the arrival rates of limit and market orders are functions of the market liquidity process which reflects the balance of the demand and supply of liquidity. Limit and market orders mutually excite each other so that liquidity is mean reverting. We use the theory of Meyer-$σ$-fields to introduce a short-term signal process from which a trader learns about imminent changes in order flow. Her trades impact the market through the same mechanism as other orders. With a novel version of Marcus-type SDEs we efficiently describe the intricate timing of market dynamics at moments when her orders concur with that of others. In this setting, we examine an optimal execution problem and derive the Hamilton--Jacobi--Bellman (HJB) equation for the value function of the trader. The HJB equation is solved numerically and we illustrate how the trader uses the signals to enhance the performance of execution problems and to execute speculative strategies.

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。