Jian Zou : Pattern Detection for High-Frequency Financial Time Series via Clustering and Bi-Clustering

Posted by Yichao Wu , part of the Statistics and Data Science Seminar.

At

Feb. 10, 2021, 4 p.m.

In

Zoom

Abstract

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 comovement 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 a 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.