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Activity Number: 444 - Highlights from the Journal STAT
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: SSC (Statistical Society of Canada)
Abstract #312617
Title: Clustering and Bi-Clustering for High-Frequency Financial Time Series Based on Mutual Information
Author(s): Jian Zou* and Haitao Liu and Nalini Ravishanker
Companies: Worcester Polytechnic Institute and Worcester Polytechnic Institute and University of Connecticut
Keywords: Biclustering; High-Frequency Time Series; Mutual Information

Exploring high frequency transaction level financial data is of considerable interest to researchers and investors. The extra amount of information contained in high-frequency data and keen interests in high-frequency finance motivate researchers to study dynamic patterns of co-movement over multiple trading days. In this paper, we have developed a series of clustering and biclustering algorithms based on mutual information for high frequency financial time series . We examine the co-movement probabilities of selected m-tuples of stocks over multiple trading days under different metrics. Additionally, we propose an unified framework to describe patterns and monitor the structure of high-dimensional daily or weekly time series that track linkages between any given m-tuple of stocks over a long time period.

Authors who are presenting talks have a * after their name.

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