Clustering a large number of time series into relatively homogeneous groups is a well-studied unsupervised learning technique that has been widely used for grouping financial instruments (say, stocks) based on their stochastic properties across the entire time period under consideration. However, clustering algorithms ignore the notion of biclustering, that is, grouping of stocks only within a subset of times rather than over the entire time period. Biclustering algorithms enable grouping of stocks and times simultaneously, and thus facilitate improved pattern extraction for informed trading strategies. While biclustering methods may be employed for grouping low-frequency (daily) financial data, their use with high-frequency financial time series of intra-day trading data is especially useful. This paper develops a biclustering algorithm based on pairwise or groupwise mutual information between one-minute averaged stock returns within a trading day, using jackknife estimation of mutual information (JMI). We construct a multiple day time series biclustering (MI-MDTSB) algorithm that can capture refined and local comovement patterns between groups of stocks over a subset of continuous time points. Extensive numerical studies based on high-frequency returns data reveal interesting intra-day patterns among different asset groups and sectors.

中文翻译：

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基于信息论的高频金融时间序列双聚类

将大量时间序列聚类成相对同质的组是一种经过充分研究的无监督学习技术，已广泛用于基于金融工具（例如股票）在整个考虑的整个时间段内的随机属性进行分组。然而，聚类算法忽略了双聚类的概念，即只在一个时间子集内而不是在整个时间段内对股票进行分组。双聚类算法可以同时对股票和时间进行分组，从而促进改进的模式提取，以实现明智的交易策略。虽然双聚类方法可用于对低频（每日）金融数据进行分组，但它们与日内交易数据的高频金融时间序列一起使用尤其有用。本文利用互信息的折刀估计（JMI）开发了一种基于交易日内一分钟平均股票收益之间的成对或成组互信息的双聚类算法。我们构建了一个多日时间序列双聚类 (MI-MDTSB) 算法，该算法可以在连续时间点的子集上捕获股票组之间的精细和局部联动模式。基于高频回报数据的广泛数值研究揭示了不同资产组和行业之间有趣的日内模式。我们构建了一个多日时间序列双聚类 (MI-MDTSB) 算法，该算法可以在连续时间点的子集上捕获股票组之间的精细和局部联动模式。基于高频回报数据的广泛数值研究揭示了不同资产组和行业之间有趣的日内模式。我们构建了一个多日时间序列双聚类 (MI-MDTSB) 算法，该算法可以在连续时间点的子集上捕获股票组之间的精细和局部联动模式。基于高频回报数据的广泛数值研究揭示了不同资产组和行业之间有趣的日内模式。