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用于股票协方差与投资组合配置的共交易网络

文章 arXiv papers · 作者: Yutong Lu et al.
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总结

研究使用US只股票之间的交易时间接近程度定义共交易关系，并构建动态网络。研究对这些网络应用谱聚类，以识别依赖关系随时间变化的股票群组，并补充基于行业板块的分类。

利用覆盖2017至2019年的高频限价订单簿数据，作者发现低延迟共交易与收益协方差之间存在统计显著的正向关系。他们还开发了一种利用网络信息的高维协方差估计器。报告称，使用该估计值的均值方差投资组合比标准基准波动率更低、夏普比率更高。摘要未说明基准的构建方式、交易成本或超出研究数据期间的稳健性，因此实际评估这些投资组合结果还需要相关细节。

核心观点

  • 研究利用股票间的交易时间构建共交易指标和动态股票网络。
  • 谱聚类识别出具有经济意义且依赖关系随时间变化的股票群组。
  • 研究报告称，低延迟共交易与收益协方差之间存在正向关系。
  • 网络信息协方差估计器支持构建均值方差投资组合；报告称其波动率和夏普比率优于标准基准。

标签

股票市场微观结构统计学投资组合构建美国市场
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# Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets









The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States. In this article, we investigate how such concurrent cross-stock trading behaviors, which we denote as co-trading, shape the market structures and affect stock price co-movements. By leveraging a co-trading-based pairwise similarity measure, we propose a novel method to construct dynamic networks of stocks. Our empirical studies employ high-frequency limit order book data from 2017-01-03 to 2019-12-09. By applying spectral clustering on co-trading networks, we uncover economically meaningful clusters of stocks. Beyond the static Global Industry Classification Standard (GICS) sectors, our data-driven clusters capture the time evolution of the dependency among stocks. Furthermore, we demonstrate statistically significant positive relations between low-latency co-trading and return covariance. With the aid of co-trading networks, we develop a robust estimator for high-dimensional covariance matrix, which yields superior economic value on portfolio allocation. The mean-variance portfolios based on our covariance estimates achieve both lower volatility and higher Sharpe ratios than standard benchmarks.

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。