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2018 贸易战期间股票订单流的马尔可夫链分析

文章 arXiv papers · 作者: Salam Rabindrajit Luwang et al.
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总结

本研究使用 2018 US 贸易战期间六个行业股票的高频订单序列建立模型。研究采用一阶、时间齐次的离散时间马尔可夫链，通过卡方检验检查马尔可夫假设，并以最大似然法估计转移概率。研究使用热力图和推导出的链统计量比较高波动日与低波动日的订单行为。

分析报告称，高波动日的新增和撤销订单更多；作者据此推断，活跃交易者会挂出限价单，随后撤销其中许多订单。这类订单较高的平稳概率和较短的平均重现时间支持这一解释。不同波动条件下相似的谱隙和熵率表明交易策略可能相近；金融和银行业反复出现的完全成交则被视为韧性的证据。文档没有提供样本细节、数值估计或因果识别，因此关于交易者意图和行业韧性的说法应视为对观测订单数据的解释。

核心观点

  • 本研究用一阶、时间齐次的马尔可夫链表示股票订单序列。
  • 卡方检验用于评估马尔可夫假设，转移概率通过最大似然法估计。
  • 据报告的链统计量，高波动日的新增和撤销订单更为突出。
  • 相似的谱隙和熵率表明，不同波动条件下的订单动态总体相近。
  • 金融和银行业反复出现完全成交，被解释为贸易战期间韧性的表现。

标签

股票高频交易市场微观结构统计学波动率
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# High-Frequency Stock Market Order Transitions during the US-China Trade War 2018: A Discrete-Time Markov Chain Analysis


# High-Frequency Stock Market Order Transitions during the US-China Trade War 2018: A Discrete-Time Markov Chain Analysis









Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the USA-China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test. We estimate the transition probability matrix of the sequence using maximum likelihood estimation. From the heat-map of these matrices, we found the presence of active participation by different types of traders during high volatility days. On such days, these traders place limit orders primarily with the intention of deleting the majority of them to influence the market. These findings are supported by high stationary distribution and low mean recurrence values of add and delete orders. Further, we found similar spectral gap and entropy rate values, which indicates that similar trading strategies are employed on both high and low volatility days during the trade war. Among all the sectors considered in this study, we observe that there is a recurring pattern of full execution orders in Finance & Banking sector. This shows that the banking stocks are resilient during the trade war. Hence, this study may be useful in understanding stock market order dynamics and devise trading strategies accordingly on high and low volatility days during extreme macroeconomic events.

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。