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根据预测成交量和价格区间动态拆分订单

文章 arXiv papers · 作者: Ritwika Chattopadhyay et al.
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总结

文档介绍一种大额订单自适应执行策略，其出发点是大规模交易可能造成的市场冲击和滑点。该策略提出预测未来成交量和价格区间，再根据这些预测随着市场状况变化调整每笔拆分订单的规模。

预测方法结合指数加权移动平均与马尔可夫链蒙特卡洛模拟。论文报告称，该方法提高了执行效率并降低市场冲击，并称其适用于波动较大的市场环境。摘录未说明数据、比较基准、评估设计或报告的改善幅度，因此仅凭该说明无法评估结果的稳健性或可迁移性。

核心观点

  • 大额订单在执行时可能造成市场冲击和滑点。
  • 该策略预测成交量和价格区间，以调整拆分订单的规模。
  • 该策略结合指数加权移动平均与马尔可夫链蒙特卡洛模拟。
  • 文档报告称执行效率有所提升、市场冲击有所降低，但摘录未提供评估细节。

标签

交易执行市场微观结构波动率
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# Volatility-Volume Order Slicing via Statistical Analysis


# Volatility-Volume Order Slicing via Statistical Analysis









This paper addresses the challenges faced in large-volume trading, where executing substantial orders can result in significant market impact and slippage. To mitigate these effects, this study proposes a volatility-volume-based order slicing strategy that leverages Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations. These methods are used to dynamically estimate future trading volumes and price ranges, enabling traders to adapt their strategies by segmenting order execution sizes based on these predictions. Results show that the proposed approach improves trade execution efficiency, reduces market impact, and offers a more adaptive solution for volatile market conditions. The findings have practical implications for large-volume trading, providing a foundation for further research into adaptive execution 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。